IPL - Batsman Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPL Insights\n",
    "### Importing all the library\n",
    "numpy, pandas, matplotlib, seaborn, math, pylab, json, datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd  \n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "from IPython.display import Markdown, display\n",
    "from sklearn.cluster import KMeans\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from gmplot import gmplot\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing csv file in a variable\n",
    "Storing matches.csv in variable data_setm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  season       city        date                        team1  \\\n",
       "0         1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1         2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2         3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3         4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4         5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong          \n",
       "1  A Nand Kishore         S Ravi          \n",
       "2     Nitin Menon      CK Nandan          \n",
       "3    AK Chaudhary  C Shamshuddin          \n",
       "4                                         "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_setm = pd.read_csv('datasets/matches.csv',keep_default_na=False)\n",
    "\n",
    "data_setm.columns = ['match_id','season','city','date','team1','team2','toss_winner','toss_decision','result','dl_applied','winner','win_by_runs','win_by_wickets','player_of_match','venue','umpire1','umpire2','umpire3']\n",
    "data_setm.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['match_id' 'season' 'city' 'date' 'team1' 'team2' 'toss_winner'\n",
      " 'toss_decision' 'result' 'dl_applied' 'winner' 'win_by_runs'\n",
      " 'win_by_wickets' 'player_of_match' 'venue' 'umpire1' 'umpire2' 'umpire3']\n"
     ]
    }
   ],
   "source": [
    "print(data_setm.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting the Histogram of the Matches in Each season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram_plot(dataset, title):\n",
    "    plt.figure(figsize=(8, 6))    \n",
    "    \n",
    "    ax = plt.subplot()    \n",
    "    ax.spines[\"top\"].set_visible(False)    \n",
    "    ax.spines[\"bottom\"].set_visible(False)    \n",
    "    ax.spines[\"right\"].set_visible(False)    \n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "    \n",
    "    ax.get_xaxis().tick_bottom()\n",
    "    ax.get_yaxis().tick_left() \n",
    "    \n",
    "    plt.title(title, fontsize = 22)\n",
    "    plt.hist(dataset, edgecolor='black', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSRovPoFLkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpMcNYkqTGDGNJkhozjCVJaswwliSpsVnDOMk+Sb6W5O+S3JXk3/ftRyS5Ncn9Sa5KstfilytJ0uozzJ7xU8DpVXUscBxwdpKTgfcA76+qlwOPAhcvXpmSJK1es4ZxdZ7sJ/fshwJOB67u2zcD5y9KhZIkrXJDnTNOsibJncAu4EbgO8BjVfVsv8g24JAZ1t2UZGuSrRMTE6OoWZKkVWXtMAtV1XPAcUkOAK4Bjh72DapqAphM4ZpzhZIkrXJzupq6qh4DbgJOAQ5IMhnmG4DtI65NkqSxMMzV1C/q94hJsi9wFnAPXSi/oV9sI3DtYhUpSdJqNsxh6vXA5iRr6ML7U1V1fZK7gU8m+Y/AHcAVi1inJEmr1qxhXFXfAI6fpv0B4KTFKEqSpHHiE7gkSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIaM4wlSWrMMJYkqTHDWJKkxgxjSZIamzWMkxya5KYkdye5K8lb+vaDktyY5L7+9cDFL1eSpNVnmD3jZ4G3VdUxwMnA7yQ5BrgM2FJVRwJb+mlJkjRHs4ZxVe2oqq/3408A9wCHAOcBm/vFNgPnL1aRkiStZnM6Z5zkcOB44FZgXVXt6GftBNbNsM6mJFuTbJ2YmFhAqZIkrU5rh10wyfOAzwBvrarHk/xkXlVVkppuvaqaACZTeNplJEkaZ0PtGSfZky6Ir6yqz/bNjyRZ389fD+xanBIlSVrdhrmaOsAVwD1V9b6BWdcBG/vxjcC1oy9PkqTVb5jD1KcCbwK+meTOvu0dwLuBTyW5GHgIuGBxSpQkaXWbNYyr6stAZph9xmjLkSRp/PgELkmSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKkxw1iSpMYMY0mSGjOMJUlqzDCWJKmxWcM4yUeT7EryrYG2g5LcmOS+/vXAxS1TkqTVa5g9448DZ09puwzYUlVHAlv6aUmSNA+zhnFV/Q3w/SnN5wGb+/HNwPkjrkuSpLEx33PG66pqRz++E1g3onokSRo7C76Aq6oKqJnmJ9mUZGuSrRMTEwt9O0mSVp2181zvkSTrq2pHkvXArpkWrKoJYDKFZwxtSZLG1Xz3jK8DNvbjG4FrR1OOJEnjZ5hbm/4KuAU4Ksm2JBcD7wbOSnIfcGY/LUmS5mHWw9RVdeEMs84YcS2SJI0ln8AlSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LU2ILCOMnZSb6d5P4kl42qKEmSxsm8wzjJGuBDwOuAY4ALkxwzqsIkSRoXC9kzPgm4v6oeqKqngU8C542mLEmSxsdCwvgQ4OGB6W19myRJmoNU1fxWTN4AnF1Vv9VPvwl4VVX97pTlNgGb+smJqppYQL1Ta9g0yu1pevbz0rCfl459vTTs5+EtZM94O3DowPSGvu3/U1UTVXViP4z6H2XT7ItoBOznpWE/Lx37emnYz0NaSBjfBhyZ5IgkewFvBK4bTVmSJI2PtfNdsaqeTfK7wBeBNcBHq+qukVUmSdKYmHcYA1TV54HPj6iW+fBcxNKwn5eG/bx07OulYT8Pad4XcEmSpNHwcZiSJDW2rMI4yaFJbkpyd5K7krylbz8oyY1J7utfD+zbk+SD/eM4v5HkhIFtvbffxj39Mmn1uZabefTz0UluSfJUkj+Ysi0fibobo+rrmbajzii/0/38NUnuSHL9Un+W5WzEPzsOSHJ1knv7n9OntPhMy8WyCmPgWeBtVXUMcDLwO/0jNi8DtlTVkcCWfhq6R3Ee2Q+bgA8DJHk1cCrwCuCfAq8EXrOEn2O5m2s/fx/4N8CfDG7ER6IOZSR9vZvtqDOqfp70FuCexS15RRplP38A+EJVHQ0cy5j397IK46raUVVf78efoPvHOYTuMZub+8U2A+f34+cBf1GdrwIHJFkPFLAPsBewN7An8MiSfZBlbq79XFW7quo24Jkpm/KRqLMYVV/vZjtipN9pkmwAfhW4fAlKX1FG1c9JXgD8EnBFv9zTVfXYknyIZWpZhfGgJIcDxwO3Auuqakc/ayewrh+f9pGcVXULcBOwox++WFVj/VvXTIbs55n4SNQ5WGBfz7QdTTGCfv5T4BLgx4tR32qxwH4+Avgu8LH+dMDlSfZbrFpXgmUZxkmeB3wGeGtVPT44r7rLv3d7CXiSlwM/R/dUsEOA05P8s0Uqd8VaaD9reKPq691tRyP52XEusKuqbl+8Kle+EXyf1wInAB+uquOBH/DTQ9tjadmFcZI96f6Rr6yqz/bNj/SHn+lfd/XtMz2S818AX62qJ6vqSeAGYKwvDphqjv08k6EeiTruRtTXM21HvRH186nA65M8SHfa5fQkn1ikklekEfXzNmBbVU0e3bmaLpzH1rIK4/6K5yuAe6rqfQOzrgM29uMbgWsH2v9VOicD/6c/VPIPwGuSrO2/OK9hzC8OGDSPfp6Jj0Sdxaj6ejfbEaPr56p6e1VtqKrD6b7PX6qqixah5BVphP28E3g4yVF90xnA3SMud2WpqmUzAKfRHd74BnBnP5wDvJDuCr37gP8BHNQvH7qreb8DfBM4sW9fA/xXugC+G3hf68+2nIZ59PPBdL/JPg481o8/v593DvD3/b/BO1t/tuU2jKqvZ9pO68+3XIZRfqcHtvla4PrWn205DSP+2XEcsLXf1ueAA1t/vpaDT+CSJKmxZXWYWpKkcWQYS5LUmGEsSVJjhrEkSY0ZxpIkNWYYS5LUmGEsSVJjhrEkSY39P85UteZXJ8l8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_years = list([data_setm['season']])\n",
    "histogram_plot(season_years, title = \"Seasons\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing csv file in a variable\n",
    "storing deliveries.csv in variable data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over   ...    bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0   ...           0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0   ...           0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0   ...           0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0   ...           0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0   ...           0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = pd.read_csv('datasets/deliveries.csv')\n",
    "data_set.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['match_id' 'inning' 'batting_team' 'bowling_team' 'over' 'ball' 'batsman'\n",
      " 'non_striker' 'bowler' 'is_super_over' 'wide_runs' 'bye_runs'\n",
      " 'legbye_runs' 'noball_runs' 'penalty_runs' 'batsman_runs' 'extra_runs'\n",
      " 'total_runs' 'player_dismissed' 'dismissal_kind' 'fielder']\n"
     ]
    }
   ],
   "source": [
    "print(data_set.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the two CSV dataframes in single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over   ...    result  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0   ...    normal   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0   ...    normal   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0   ...    normal   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0   ...    normal   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0   ...    normal   \n",
       "\n",
       "   dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "0           0  Sunrisers Hyderabad           35               0   \n",
       "1           0  Sunrisers Hyderabad           35               0   \n",
       "2           0  Sunrisers Hyderabad           35               0   \n",
       "3           0  Sunrisers Hyderabad           35               0   \n",
       "4           0  Sunrisers Hyderabad           35               0   \n",
       "\n",
       "   player_of_match                                      venue      umpire1  \\\n",
       "0     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "1     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "2     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "3     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "4     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "\n",
       "    umpire2 umpire3  \n",
       "0  NJ Llong          \n",
       "1  NJ Llong          \n",
       "2  NJ Llong          \n",
       "3  NJ Llong          \n",
       "4  NJ Llong          \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = pd.merge(data_set, data_setm, on='match_id')\n",
    "data_set.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating custom list\n",
    "list contains match_id,batsman's name, runs scored, is_super_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Player_Runs  = list([data_set['match_id'],data_set['batsman'],data_set['batsman_runs'],data_set['is_super_over'],data_set['wide_runs'],data_set['noball_runs'],data_set['season']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Match Id for particular season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the year to see the statistics :2017\n",
      "Match id of match during the year are :\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]\n",
      "Total no. of matches in that year : 59\n"
     ]
    }
   ],
   "source": [
    "input_year = int(input(\"Enter the year to see the statistics :\"))\n",
    "Years = list([[data_setm['season']],[data_setm['match_id']]])\n",
    "print(\"Match id of match during the year are :\")\n",
    "match_during_input_year = []\n",
    "for i in range(len(Years[0][0])):\n",
    "    if(Years[0][0][i]==input_year):\n",
    "        match_during_input_year.append(Years[1][0][i])\n",
    "print(match_during_input_year)\n",
    "print(\"Total no. of matches in that year :\",len(match_during_input_year))\n",
    "#for id we use Years[1][0][i]\n",
    "#for year we use Years[0][0][i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the list of Players who have batted\n",
    "player contains the list of players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "player = set()\n",
    "for i in range(len(Player_Runs[0:][1])):\n",
    "    player.add(Player_Runs[0:][1][i])\n",
    "#print(player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AT Rayudu', 'SS Shaikh', 'Z Khan', 'DE Bollinger', 'PC Valthaty', 'SA Asnodkar', 'AA Bilakhia', 'AD Nath', 'SS Tiwary', 'GJ Bailey', 'P Awana', 'S Badrinath', 'Mohammad Ashraful', 'RR Raje', 'SM Pollock', 'DPMD Jayawardene', 'KP Appanna', 'HV Patel', 'JP Faulkner', 'NLTC Perera', 'SN Thakur', 'SB Wagh', 'K Rabada', 'MS Wade', 'Harbhajan Singh', 'AB de Villiers', 'A Mithun', 'NL McCullum', 'KMDN Kulasekara', 'C Nanda', 'DS Lehmann', 'KM Jadhav', 'Sunny Gupta', 'C de Grandhomme', 'VVS Laxman', 'A Uniyal', 'Mashrafe Mortaza', 'UT Yadav', 'TM Dilshan', 'GJ Maxwell', 'DB Ravi Teja', 'S Rana', 'KK Cooper', 'RR Bhatkal', 'W Jaffer', 'RS Bopara', 'MJ Clarke', 'V Shankar', 'Sunny Singh', 'AUK Pathan', 'CK Langeveldt', 'Anirudh Singh', 'BA Bhatt', 'A Mukund', 'X Thalaivan Sargunam', 'S Badree', 'CRD Fernando', 'Basil Thampi', 'KC Cariappa', 'Bipul Sharma', 'S Kaul', 'T Taibu', 'RR Pant', 'CL White', 'S Tyagi', 'BB Sran', 'MM Sharma', 'JP Duminy', 'A Mishra', 'Sohail Tanvir', 'P Sahu', 'WD Parnell', 'SS Iyer', 'AA Chavan', 'Misbah-ul-Haq', 'MN Samuels', 'Y Venugopal Rao', 'TL Suman', 'SM Harwood', 'MJ Guptill', 'R Sathish', 'RR Powar', 'A Symonds', 'Jaskaran Singh', 'BJ Rohrer', 'B Sumanth', 'A Choudhary', 'JJ Roy', 'YK Pathan', 'CJ Anderson', 'AJ Tye', 'JJ van der Wath', 'S Sohal', 'GB Hogg', 'OA Shah', 'Gurkeerat Singh', 'MN van Wyk', 'B Laughlin', 'IK Pathan', 'SR Tendulkar', 'Joginder Sharma', 'U Kaul', 'DR Martyn', 'ML Hayden', 'D Salunkhe', 'SP Goswami', 'KP Pietersen', 'CR Brathwaite', 'J Theron', 'RR Rossouw', 'Pankaj Singh', 'AC Voges', 'MA Agarwal', 'AR Patel', 'R Ninan', 'AM Rahane', 'WPUJC Vaas', 'Younis Khan', 'CH Gayle', 'KV Sharma', 'C Madan', 'Shakib Al Hasan', 'JD Ryder', 'EJG Morgan', 'V Pratap Singh', 'Sandeep Sharma', 'MC Henriques', 'MP Stoinis', 'CK Kapugedera', 'SP Fleming', 'MV Boucher', 'AC Gilchrist', 'HH Pandya', 'CA Lynn', 'MJ McClenaghan', 'K Goel', 'CR Woakes', 'Kamran Akmal', 'KB Arun Karthik', 'MS Gony', 'DJG Sammy', 'Kuldeep Yadav', 'J Suchith', 'VRV Singh', 'VS Malik', 'Ankit Sharma', 'Q de Kock', 'AS Raut', 'BE Hendricks', 'CH Morris', 'M Manhas', 'NJ Rimmington', 'PA Patel', 'Iqbal Abdulla', 'SK Warne', 'BA Stokes', 'S Chanderpaul', 'RA Tripathi', 'DM Bravo', 'AP Dole', 'AD Mascarenhas', 'Y Nagar', 'M Vijay', 'SMSM Senanayake', 'A Nehra', 'KJ Abbott', 'SPD Smith', 'PV Tambe', 'DJ Muthuswami', 'YV Takawale', 'T Henderson', 'RA Jadeja', 'AG Paunikar', 'AR Bawne', 'B Kumar', 'SA Abbott', 'DS Kulkarni', 'AA Noffke', 'M Ashwin', 'AB Dinda', 'LR Shukla', 'DW Steyn', 'DP Vijaykumar', 'AA Jhunjhunwala', 'SB Styris', 'BCJ Cutting', 'S Ladda', 'SP Jackson', 'NV Ojha', 'AF Milne', 'R Vinay Kumar', 'JDS Neesham', 'S Narwal', 'KD Karthik', 'Kamran Khan', 'Imran Tahir', 'J Botha', 'MJ Lumb', 'Washington Sundar', 'SB Jakati', 'DJ Harris', 'AP Majumdar', 'S Kaushik', 'BMAJ Mendis', 'YS Chahal', 'PD Collingwood', 'M Muralitharan', 'Shahid Afridi', 'M Kaif', 'KK Nair', 'DA Warner', 'P Kumar', 'Mohammad Hafeez', 'Mandeep Singh', 'AS Rajpoot', 'DNT Zoysa', 'AP Tare', 'M Rawat', 'KS Williamson', 'KC Sangakkara', 'HH Gibbs', 'R McLaren', 'DB Das', 'PSP Handscomb', 'VR Aaron', 'GD McGrath', 'Mohammad Nabi', 'S Randiv', 'SW Tait', 'DAJ Bracewell', 'Y Gnaneswara Rao', 'BAW Mendis', 'MDKJ Perera', 'KH Pandya', 'TM Srivastava', 'SM Katich', 'Yuvraj Singh', 'S Nadeem', 'PP Chawla', 'RS Gavaskar', 'A Flintoff', 'DJ Jacobs', 'UBT Chand', 'BB Samantray', 'MK Tiwary', 'DJ Hooda', 'KL Rahul', 'P Negi', 'N Saini', 'RR Sarwan', 'PP Ojha', 'PJ Sangwan', 'CA Pujara', 'JM Kemp', 'LPC Silva', 'D Kalyankrishna', 'JDP Oram', 'DJ Bravo', 'VH Zol', 'ER Dwivedi', 'R Bhatia', 'J Arunkumar', 'WA Mota', 'MD Mishra', 'MR Marsh', 'RS Sodhi', 'DT Patil', 'Niraj Patel', 'SK Raina', 'UT Khawaja', 'RG More', 'D du Preez', 'DH Yagnik', 'L Ablish', 'A Chopra', 'JEC Franklin', 'L Balaji', 'RE van der Merwe', 'JR Hopes', 'Umar Gul', 'MK Pandey', 'GR Napier', 'F Behardien', 'G Gambhir', 'H Das', 'Mohammad Asif', 'AM Nayar', 'MS Bisla', 'JJ Bumrah', 'MS Dhoni', 'STR Binny', 'F du Plessis', 'SW Billings', 'AC Thomas', 'Harmeet Singh', 'CJ Jordan', 'YA Abdulla', 'KAJ Roach', 'B Lee', 'S Sriram', 'AB Barath', 'DL Vettori', 'RE Levi', 'LA Carseldine', 'A Ashish Reddy', 'SE Marsh', 'SN Khan', 'TA Boult', 'SD Chitnis', 'AN Ghosh', 'PA Reddy', 'AB McDonald', 'MA Khote', 'C Munro', 'TR Birt', 'A Singh', 'Vishnu Vinod', 'SC Ganguly', 'S Vidyut', 'R Tewatia', 'MF Maharoof', 'AS Yadav', 'GH Vihari', 'JO Holder', 'SE Bond', 'TM Head', 'NJ Maddinson', 'IC Pandey', 'R Dravid', 'R Ashwin', 'N Rana', 'R Rampaul', 'LRPL Taylor', 'Abdur Razzak', 'VS Yeligati', 'Parvez Rasool', 'AD Russell', 'TS Mills', 'M Ntini', 'Sachin Baby', 'Swapnil Singh', 'BJ Haddin', 'LA Pomersbach', 'TD Paine', 'KA Pollard', 'MEK Hussey', 'Ankit Soni', 'S Anirudha', 'S Aravind', 'UA Birla', 'Harpreet Singh', 'V Kohli', 'JA Morkel', 'B Akhil', 'Mohammed Shami', 'M Klinger', 'B Chipli', 'RD Chahar', 'FH Edwards', 'Yashpal Singh', 'WP Saha', 'DJ Thornely', 'D Wiese', 'S Sreesanth', 'GC Smith', 'R Bishnoi', 'M Kartik', 'LH Ferguson', 'RG Sharma', 'IR Jaggi', 'BJ Hodge', 'RJ Harris', 'T Kohli', 'BB McCullum', 'P Dogra', 'A Kumble', 'PR Shah', 'Shoaib Ahmed', 'BR Dunk', 'CJ Ferguson', 'M Vohra', 'Ishan Kishan', 'AJ Finch', 'R Sharma', 'VY Mahesh', 'S Dhawan', 'RV Gomez', 'I Malhotra', 'AG Murtaza', 'TG Southee', 'R Dhawan', 'SB Joshi', 'AC Blizzard', 'JD Unadkat', 'DP Nannes', 'AN Ahmed', 'Anureet Singh', 'P Parameswaran', 'MG Johnson', 'AD Mathews', 'Shivam Sharma', 'DJ Hussey', 'NS Naik', 'J Yadav', 'Salman Butt', 'SR Watson', 'FY Fazal', 'JE Taylor', 'RV Uthappa', 'RJ Peterson', 'SV Samson', 'AL Menaria', 'Karanveer Singh', 'Shoaib Malik', 'Rashid Khan', 'LMP Simmons', 'PM Sarvesh Kumar', 'M de Lange', 'RJ Quiney', 'ND Doshi', 'J Syed Mohammad', 'S Gopal', 'MA Starc', 'PJ Cummins', 'RP Singh', 'HM Amla', 'NM Coulter-Nile', 'MM Patel', 'SA Yadav', 'LJ Wright', 'SL Malinga', 'CM Gautam', 'DR Smith', 'RN ten Doeschate', 'A Chandila', 'A Zampa', 'SP Narine', 'DT Christian', 'M Morkel', 'K Upadhyay', 'SK Trivedi', 'JC Buttler', 'SB Bangar', 'CJ McKay', 'Shoaib Akhtar', 'RT Ponting', 'AB Agarkar', 'T Thushara', 'JH Kallis', 'Azhar Mahmood', 'V Sehwag', 'I Sharma', 'R Shukla', 'KW Richardson', 'ST Jayasuriya', 'DA Miller', 'DL Chahar', 'SJ Srivastava', 'L Ronchi', 'CA Ingram', 'MC Juneja']\n"
     ]
    }
   ],
   "source": [
    "player_list = list(player)\n",
    "print(player_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_list= ['JP Duminy', 'ML Hayden', 'AS Rajpoot', 'Rashid Khan', 'CJ Ferguson', 'MJ Clarke', 'VY Mahesh', 'BA Bhatt', 'KC Sangakkara', 'DP Nannes', 'CH Gayle', 'A Symonds', 'MN van Wyk', 'YS Chahal', 'H Das', 'SL Malinga', 'VRV Singh', 'L Ronchi', 'F du Plessis', 'S Rana', 'DL Chahar', 'AR Patel', 'AR Bawne', 'JM Kemp', 'MJ Lumb', 'Washington Sundar', 'MEK Hussey', 'SM Katich', 'SR Tendulkar', 'AC Voges', 'WA Mota', 'Mohammad Nabi', 'JO Holder', 'NV Ojha', 'T Thushara', 'S Vidyut', 'MV Boucher', 'UT Khawaja', 'RD Chahar', 'ST Jayasuriya', 'Vishnu Vinod', 'R Sathish', 'RS Bopara', 'M Rawat', 'L Ablish', 'CJ Jordan', 'Imran Tahir', 'AA Bilakhia', 'R Bhatia', 'LA Carseldine', 'UT Yadav', 'Sunny Gupta', 'RJ Quiney', 'RS Gavaskar', 'DA Warner', 'T Henderson', 'MA Starc', 'LJ Wright', 'RE van der Merwe', 'JD Unadkat', 'DM Bravo', 'MC Juneja', 'B Chipli', 'Mohammad Asif', 'M Ntini', 'PP Ojha', 'DR Smith', 'CK Kapugedera', 'CA Lynn', 'VS Yeligati', 'KP Appanna', 'C Nanda', 'RN ten Doeschate', 'VS Malik', 'NLTC Perera', 'S Anirudha', 'A Singh', 'Iqbal Abdulla', 'SW Billings', 'A Uniyal', 'Abdur Razzak', 'SA Asnodkar', 'DJ Bravo', 'PJ Cummins', 'PJ Sangwan', 'AUK Pathan', 'TL Suman', 'Anureet Singh', 'YV Takawale', 'CJ Anderson', 'IR Jaggi', 'JJ Roy', 'YK Pathan', 'IK Pathan', 'M Kartik', 'GH Vihari', 'HM Amla', 'M Muralitharan', 'BJ Rohrer', 'Sohail Tanvir', 'DW Steyn', 'A Zampa', 'SV Samson', 'MC Henriques', 'A Choudhary', 'Umar Gul', 'PSP Handscomb', 'Mandeep Singh', 'Shakib Al Hasan', 'BE Hendricks', 'DPMD Jayawardene', 'RP Singh', 'Sandeep Sharma', 'M Ashwin', 'JA Morkel', 'CA Pujara', 'DJG Sammy', 'AC Blizzard', 'SB Wagh', 'KM Jadhav', 'Gurkeerat Singh', 'BB Samantray', 'AP Majumdar', 'AG Murtaza', 'DR Martyn', 'R Ninan', 'Mohammed Shami', 'WP Saha', 'AA Noffke', 'Jaskaran Singh', 'LH Ferguson', 'DS Kulkarni', 'RA Jadeja', 'KAJ Roach', 'GB Hogg', 'DB Ravi Teja', 'R Tewatia', 'SC Ganguly', 'SJ Srivastava', 'DA Miller', 'RS Sodhi', 'TS Mills', 'S Ladda', 'MK Pandey', 'TA Boult', 'SB Styris', 'JDS Neesham', 'CR Woakes', 'JP Faulkner', 'AC Thomas', 'MA Agarwal', 'DT Christian', 'Salman Butt', 'RR Sarwan', 'MS Bisla', 'B Laughlin', 'DJ Jacobs', 'TG Southee', 'VH Zol', 'J Yadav', 'X Thalaivan Sargunam', 'BCJ Cutting', 'AB de Villiers', 'SR Watson', 'K Goel', 'PR Shah', 'SP Goswami', 'KK Nair', 'T Taibu', 'R Ashwin', 'A Nehra', 'Shivam Sharma', 'MP Stoinis', 'M de Lange', 'RR Powar', 'Z Khan', 'AJ Finch', 'R Dhawan', 'S Kaushik', 'D Salunkhe', 'SN Khan', 'AL Menaria', 'PA Reddy', 'KL Rahul', 'Sachin Baby', 'AD Nath', 'MS Wade', 'N Saini', 'CM Gautam', 'SB Bangar', 'DJ Hussey', 'Harbhajan Singh', 'MD Mishra', 'UA Birla', 'RJ Harris', 'B Kumar', 'A Flintoff', 'CH Morris', 'MF Maharoof', 'L Balaji', 'SE Bond', 'R Rampaul', 'AA Chavan', 'C de Grandhomme', 'PA Patel', 'Kamran Akmal', 'S Sriram', 'CA Ingram', 'JEC Franklin', 'S Sohal', 'U Kaul', 'AA Jhunjhunwala', 'TM Dilshan', 'Yuvraj Singh', 'M Klinger', 'M Kaif', 'JDP Oram', 'AD Mathews', 'SM Pollock', 'PP Chawla', 'S Kaul', 'SB Jakati', 'Harpreet Singh', 'Misbah-ul-Haq', 'MR Marsh', 'Parvez Rasool', 'D Kalyankrishna', 'RR Pant', 'PD Collingwood', 'W Jaffer', 'SW Tait', 'HH Gibbs', 'RV Uthappa', 'SA Abbott', 'ND Doshi', 'AB Agarkar', 'OA Shah', 'DJ Muthuswami', 'C Madan', 'DJ Thornely', 'DJ Harris', 'JC Buttler', 'BJ Hodge', 'AT Rayudu', 'JH Kallis', 'AS Yadav', 'SP Fleming', 'AN Ahmed', 'V Kohli', 'DH Yagnik', 'JJ Bumrah', 'S Randiv', 'STR Binny', 'KP Pietersen', 'UBT Chand', 'SK Warne', 'MS Dhoni', 'AP Dole', 'HH Pandya', 'R Bishnoi', 'SM Harwood', 'S Badrinath', 'AS Raut', 'BA Stokes', 'TM Head', 'Ankit Soni', 'R Vinay Kumar', 'AP Tare', 'Younis Khan', 'AD Mascarenhas', 'Y Venugopal Rao', 'SMSM Senanayake', 'MS Gony', 'Bipul Sharma', 'KC Cariappa', 'A Mithun', 'SA Yadav', 'Pankaj Singh', 'NS Naik', 'AB Barath', 'S Nadeem', 'Mohammad Hafeez', 'HV Patel', 'S Tyagi', 'GD McGrath', 'Harmeet Singh', 'SP Jackson', 'KA Pollard', 'G Gambhir', 'B Lee', 'Yashpal Singh', 'V Shankar', 'YA Abdulla', 'LR Shukla', 'V Sehwag', 'JR Hopes', 'R Sharma', 'SP Narine', 'AD Russell', 'DB Das', 'I Malhotra', 'SS Iyer', 'DNT Zoysa', 'A Kumble', 'J Theron', 'Basil Thampi', 'S Gopal', 'SS Shaikh', 'RG Sharma', 'MK Tiwary', 'Y Nagar', 'S Badree', 'Ishan Kishan', 'DT Patil', 'MN Samuels', 'DL Vettori', 'JE Taylor', 'RG More', 'DP Vijaykumar', 'P Negi', 'LMP Simmons', 'N Rana', 'Ankit Sharma', 'B Akhil', 'GJ Maxwell', 'R Shukla', 'CL White', 'KD Karthik', 'M Manhas', 'R McLaren', 'V Pratap Singh', 'Niraj Patel', 'Shoaib Ahmed', 'IC Pandey', 'KS Williamson', 'A Mishra', 'TD Paine', 'SE Marsh', 'AB Dinda', 'LA Pomersbach', 'GJ Bailey', 'J Syed Mohammad', 'NM Coulter-Nile', 'FY Fazal', 'CRD Fernando', 'ER Dwivedi', 'SD Chitnis', 'J Suchith', 'FH Edwards', 'Kuldeep Yadav', 'K Upadhyay', 'JD Ryder', 'AN Ghosh', 'PM Sarvesh Kumar', 'AJ Tye', 'NL McCullum', 'PV Tambe', 'P Kumar', 'RR Rossouw', 'C Munro', 'SB Joshi', 'LPC Silva', 'AB McDonald', 'KJ Abbott', 'J Arunkumar', 'S Aravind', 'BR Dunk', 'J Botha', 'RR Bhatkal', 'RT Ponting', 'D Wiese', 'BJ Haddin', 'NJ Rimmington', 'AF Milne', 'M Vijay', 'A Chopra', 'Mashrafe Mortaza', 'BB Sran', 'RJ Peterson', 'KV Sharma', 'JJ van der Wath', 'Karanveer Singh', 'KW Richardson', 'D du Preez', 'CK Langeveldt', 'SS Tiwary', 'DJ Hooda', 'WD Parnell', 'DAJ Bracewell', 'GC Smith', 'Shoaib Akhtar', 'KMDN Kulasekara', 'PC Valthaty', 'S Narwal', 'CR Brathwaite', 'LRPL Taylor', 'GR Napier', 'RR Raje', 'AC Gilchrist', 'S Dhawan', 'MM Sharma', 'P Sahu', 'Sunny Singh', 'RA Tripathi', 'Shahid Afridi', 'R Dravid', 'A Ashish Reddy', 'P Dogra', 'AG Paunikar', 'VR Aaron', 'DE Bollinger', 'BMAJ Mendis', 'AM Nayar', 'SPD Smith', 'VVS Laxman', 'CJ McKay', 'Anirudh Singh', 'S Sreesanth', 'TR Birt', 'P Parameswaran', 'MG Johnson', 'B Sumanth', 'KK Cooper', 'MDKJ Perera', 'I Sharma', 'Shoaib Malik', 'Y Gnaneswara Rao', 'Kamran Khan', 'RV Gomez', 'NJ Maddinson', 'Swapnil Singh', 'WPUJC Vaas', 'A Mukund', 'SK Trivedi', 'A Chandila', 'Azhar Mahmood', 'Joginder Sharma', 'F Behardien', 'Q de Kock', 'KH Pandya', 'KB Arun Karthik', 'S Chanderpaul', 'MM Patel', 'P Awana', 'DS Lehmann', 'AM Rahane', 'MJ Guptill', 'MJ McClenaghan', 'M Morkel', 'SN Thakur', 'TM Srivastava', 'M Vohra', 'BAW Mendis', 'T Kohli', 'RE Levi', 'EJG Morgan', 'Mohammad Ashraful', 'K Rabada', 'MA Khote', 'BB McCullum', 'SK Raina']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating custom list of a particular player\n",
    "List contains match_id, player_name, runs_scored at particular ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the name of the Batsman :V Kohli\n",
      "V Kohli\n"
     ]
    }
   ],
   "source": [
    "name = input(\"Enter the name of the Batsman :\")\n",
    "print(name)\n",
    "Player_Match = []\n",
    "for i in range(len(Player_Runs[0:][1])):\n",
    "    if(Player_Runs[0:][1][i]==name and Player_Runs[0:][3][i]==0):\n",
    "        Player_Match.append([[Player_Runs[0:][0][i]],[Player_Runs[0:][1][i]],[Player_Runs[0:][2][i]],[Player_Runs[0:][6][i]]])\n",
    "#Player_Match[-50:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the Ball faced by the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Balls faced by V Kohli are :  3403\n"
     ]
    }
   ],
   "source": [
    "Player_Balls = []\n",
    "for i in range(len(Player_Runs[0:][1])):\n",
    "    if(Player_Runs[0:][1][i]==name and Player_Runs[0:][3][i]==0 and Player_Runs[0:][4][i]==0 ):\n",
    "        Player_Balls.append([[Player_Runs[0:][0][i]],[Player_Runs[0:][1][i]],[Player_Runs[0:][2][i]]])\n",
    "\n",
    "count = 0\n",
    "for i in range(len(Player_Match)):\n",
    "    #if(Player_Match[i][2][0]):\n",
    "        count += 1\n",
    "count\n",
    "print(\"No of Balls faced by\",name, \"are : \",len(Player_Balls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Runs, Century and Half Century scored by the player in a match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Player_Match_Runs = []\n",
    "Runs = []\n",
    "match_no = Player_Match[0][0]\n",
    "sum = 0\n",
    "for i in range(len(Player_Match)):\n",
    "    if(Player_Match[i][0] == match_no):\n",
    "        sum += Player_Match[i][2][0]\n",
    "    else:\n",
    "        Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "        Runs.append(sum)\n",
    "        sum = Player_Match[i][2][0]\n",
    "        match_no =  Player_Match[i][0];\n",
    "Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "Runs.append(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Century and Half Century scored by the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Century :  4\n",
      "Half Century :  30\n"
     ]
    }
   ],
   "source": [
    "fifty = 0\n",
    "century = 0\n",
    "for i in range(len(Player_Match_Runs)):\n",
    "    if(Player_Match_Runs[i][2][0]>=50 and Player_Match_Runs[i][2][0]<100):\n",
    "        fifty = fifty+1\n",
    "    elif(Player_Match_Runs[i][2][0]>=100):\n",
    "        century = century+1\n",
    "print(\"Century : \",century)\n",
    "print(\"Half Century : \",fifty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runs Scored in each match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[12], ['V Kohli'], [62], [2017]],\n",
       " [[17], ['V Kohli'], [28], [2017]],\n",
       " [[20], ['V Kohli'], [64], [2017]],\n",
       " [[27], ['V Kohli'], [0], [2017]],\n",
       " [[30], ['V Kohli'], [10], [2017]],\n",
       " [[33], ['V Kohli'], [55], [2017]],\n",
       " [[37], ['V Kohli'], [20], [2017]],\n",
       " [[42], ['V Kohli'], [6], [2017]],\n",
       " [[45], ['V Kohli'], [5], [2017]],\n",
       " [[55], ['V Kohli'], [58], [2017]],\n",
       " [[60], ['V Kohli'], [1], [2008]],\n",
       " [[63], ['V Kohli'], [23], [2008]],\n",
       " [[70], ['V Kohli'], [13], [2008]],\n",
       " [[74], ['V Kohli'], [12], [2008]],\n",
       " [[76], ['V Kohli'], [1], [2008]],\n",
       " [[80], ['V Kohli'], [9], [2008]],\n",
       " [[84], ['V Kohli'], [34], [2008]],\n",
       " [[90], ['V Kohli'], [0], [2008]],\n",
       " [[94], ['V Kohli'], [21], [2008]],\n",
       " [[101], ['V Kohli'], [3], [2008]],\n",
       " [[107], ['V Kohli'], [10], [2008]],\n",
       " [[111], ['V Kohli'], [38], [2008]],\n",
       " [[119], ['V Kohli'], [3], [2009]],\n",
       " [[122], ['V Kohli'], [11], [2009]],\n",
       " [[124], ['V Kohli'], [50], [2009]],\n",
       " [[127], ['V Kohli'], [2], [2009]],\n",
       " [[129], ['V Kohli'], [22], [2009]],\n",
       " [[134], ['V Kohli'], [19], [2009]],\n",
       " [[139], ['V Kohli'], [16], [2009]],\n",
       " [[148], ['V Kohli'], [15], [2009]],\n",
       " [[153], ['V Kohli'], [20], [2009]],\n",
       " [[159], ['V Kohli'], [38], [2009]],\n",
       " [[171], ['V Kohli'], [19], [2009]],\n",
       " [[173], ['V Kohli'], [24], [2009]],\n",
       " [[174], ['V Kohli'], [7], [2009]],\n",
       " [[178], ['V Kohli'], [2], [2010]],\n",
       " [[181], ['V Kohli'], [16], [2010]],\n",
       " [[188], ['V Kohli'], [17], [2010]],\n",
       " [[192], ['V Kohli'], [24], [2010]],\n",
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       " [[202], ['V Kohli'], [34], [2010]],\n",
       " [[205], ['V Kohli'], [42], [2010]],\n",
       " [[209], ['V Kohli'], [13], [2010]],\n",
       " [[214], ['V Kohli'], [58], [2010]],\n",
       " [[220], ['V Kohli'], [3], [2010]],\n",
       " [[223], ['V Kohli'], [14], [2010]],\n",
       " [[226], ['V Kohli'], [37], [2010]],\n",
       " [[231], ['V Kohli'], [9], [2010]],\n",
       " [[237], ['V Kohli'], [23], [2011]],\n",
       " [[242], ['V Kohli'], [12], [2011]],\n",
       " [[245], ['V Kohli'], [71], [2011]],\n",
       " [[248], ['V Kohli'], [35], [2011]],\n",
       " [[258], ['V Kohli'], [30], [2011]],\n",
       " [[263], ['V Kohli'], [56], [2011]],\n",
       " [[268], ['V Kohli'], [67], [2011]],\n",
       " [[280], ['V Kohli'], [27], [2011]],\n",
       " [[283], ['V Kohli'], [27], [2011]],\n",
       " [[288], ['V Kohli'], [39], [2011]],\n",
       " [[291], ['V Kohli'], [15], [2011]],\n",
       " [[296], ['V Kohli'], [11], [2011]],\n",
       " [[302], ['V Kohli'], [31], [2011]],\n",
       " [[304], ['V Kohli'], [70], [2011]],\n",
       " [[306], ['V Kohli'], [8], [2011]],\n",
       " [[307], ['V Kohli'], [35], [2011]],\n",
       " [[312], ['V Kohli'], [8], [2012]],\n",
       " [[317], ['V Kohli'], [6], [2012]],\n",
       " [[320], ['V Kohli'], [57], [2012]],\n",
       " [[326], ['V Kohli'], [22], [2012]],\n",
       " [[329], ['V Kohli'], [16], [2012]],\n",
       " [[333], ['V Kohli'], [4], [2012]],\n",
       " [[338], ['V Kohli'], [16], [2012]],\n",
       " [[344], ['V Kohli'], [18], [2012]],\n",
       " [[350], ['V Kohli'], [45], [2012]],\n",
       " [[356], ['V Kohli'], [9], [2012]],\n",
       " [[360], ['V Kohli'], [36], [2012]],\n",
       " [[362], ['V Kohli'], [9], [2012]],\n",
       " [[367], ['V Kohli'], [3], [2012]],\n",
       " [[372], ['V Kohli'], [73], [2012]],\n",
       " [[376], ['V Kohli'], [42], [2012]],\n",
       " [[383], ['V Kohli'], [24], [2013]],\n",
       " [[388], ['V Kohli'], [46], [2013]],\n",
       " [[392], ['V Kohli'], [35], [2013]],\n",
       " [[396], ['V Kohli'], [58], [2013]],\n",
       " [[401], ['V Kohli'], [65], [2013]],\n",
       " [[407], ['V Kohli'], [1], [2013]],\n",
       " [[411], ['V Kohli'], [11], [2013]],\n",
       " [[417], ['V Kohli'], [1], [2013]],\n",
       " [[420], ['V Kohli'], [32], [2013]],\n",
       " [[426], ['V Kohli'], [25], [2013]],\n",
       " [[429], ['V Kohli'], [57], [2013]],\n",
       " [[432], ['V Kohli'], [93], [2013]],\n",
       " [[438], ['V Kohli'], [99], [2013]],\n",
       " [[441], ['V Kohli'], [17], [2013]],\n",
       " [[448], ['V Kohli'], [14], [2013]],\n",
       " [[452], ['V Kohli'], [56], [2013]],\n",
       " [[459], ['V Kohli'], [49], [2014]],\n",
       " [[462], ['V Kohli'], [0], [2014]],\n",
       " [[468], ['V Kohli'], [31], [2014]],\n",
       " [[471], ['V Kohli'], [21], [2014]],\n",
       " [[475], ['V Kohli'], [4], [2014]],\n",
       " [[481], ['V Kohli'], [0], [2014]],\n",
       " [[484], ['V Kohli'], [35], [2014]],\n",
       " [[488], ['V Kohli'], [0], [2014]],\n",
       " [[492], ['V Kohli'], [4], [2014]],\n",
       " [[495], ['V Kohli'], [10], [2014]],\n",
       " [[499], ['V Kohli'], [27], [2014]],\n",
       " [[503], ['V Kohli'], [67], [2014]],\n",
       " [[506], ['V Kohli'], [38], [2014]],\n",
       " [[510], ['V Kohli'], [73], [2014]],\n",
       " [[522], ['V Kohli'], [13], [2015]],\n",
       " [[525], ['V Kohli'], [41], [2015]],\n",
       " [[534], ['V Kohli'], [18], [2015]],\n",
       " [[538], ['V Kohli'], [51], [2015]],\n",
       " [[540], ['V Kohli'], [62], [2015]],\n",
       " [[543], ['V Kohli'], [35], [2015]],\n",
       " [[546], ['V Kohli'], [1], [2015]],\n",
       " [[550], ['V Kohli'], [34], [2015]],\n",
       " [[554], ['V Kohli'], [48], [2015]],\n",
       " [[557], ['V Kohli'], [32], [2015]],\n",
       " [[562], ['V Kohli'], [82], [2015]],\n",
       " [[566], ['V Kohli'], [19], [2015]],\n",
       " [[568], ['V Kohli'], [44], [2015]],\n",
       " [[571], ['V Kohli'], [1], [2015]],\n",
       " [[574], ['V Kohli'], [12], [2015]],\n",
       " [[575], ['V Kohli'], [12], [2015]],\n",
       " [[580], ['V Kohli'], [75], [2016]],\n",
       " [[587], ['V Kohli'], [79], [2016]],\n",
       " [[590], ['V Kohli'], [33], [2016]],\n",
       " [[592], ['V Kohli'], [80], [2016]],\n",
       " [[595], ['V Kohli'], [100], [2016]],\n",
       " [[603], ['V Kohli'], [14], [2016]],\n",
       " [[606], ['V Kohli'], [52], [2016]],\n",
       " [[611], ['V Kohli'], [108], [2016]],\n",
       " [[615], ['V Kohli'], [20], [2016]],\n",
       " [[617], ['V Kohli'], [7], [2016]],\n",
       " [[620], ['V Kohli'], [109], [2016]],\n",
       " [[624], ['V Kohli'], [75], [2016]],\n",
       " [[626], ['V Kohli'], [113], [2016]],\n",
       " [[632], ['V Kohli'], [54], [2016]],\n",
       " [[633], ['V Kohli'], [0], [2016]],\n",
       " [[636], ['V Kohli'], [54], [2016]]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Player_Match_Runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Runs Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Runs scored by V Kohli are : 4418 in 141 matches\n"
     ]
    }
   ],
   "source": [
    "total_runs = 0\n",
    "matchess = 0\n",
    "for i in range(len(Player_Match_Runs)):\n",
    "    #if(Player_Match_Runs[i][3][0]==input_year):\n",
    "        \n",
    "        #for i in range(len(Runs)):\n",
    "        total_runs += Runs[i]\n",
    "        matchess += 1\n",
    "print(\"Total Runs scored by\",name,\"are :\",total_runs,\"in\",matchess,\"matches\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of the Runs Scored by the Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_runs = 0\n",
    "for i in range(len(Runs)):\n",
    "    total_runs += Runs[i]\n",
    "total_runs\n",
    "\n",
    "x = np.arange(len(Player_Match_Runs))\n",
    "plt.figure(figsize=(20, 10))  \n",
    "plt.scatter(x,Runs)\n",
    "plt.plot(Runs)\n",
    "plt.title('PlayerStats')\n",
    "plt.plot(x,0*x+total_runs/len(Runs))\n",
    "plt.plot(x,0*x+50)\n",
    "plt.plot(x,0*x+100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the runs scored by the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4418"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_runs = 0\n",
    "k = len(Runs)\n",
    "for i in range(k):\n",
    "    total_runs += Runs[i]\n",
    "total_runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strike Rate of the Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strike Rate of V Kohli is 129.8266235674405\n"
     ]
    }
   ],
   "source": [
    "strike_rate = total_runs/len(Player_Balls)*100\n",
    "print(\"Strike Rate of\",name,\"is\",strike_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average of the Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of V Kohli is 37.440677966101696\n"
     ]
    }
   ],
   "source": [
    "dismisal_list = list(data_set.player_dismissed)\n",
    "current_player_dismisal_count = 0\n",
    "for item in dismisal_list:\n",
    "    if(item == name):\n",
    "        current_player_dismisal_count = current_player_dismisal_count+1\n",
    "print(\"Average of\",name,\"is\",total_runs/current_player_dismisal_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attributes</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Name</td>\n",
       "      <td>V Kohli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Runs Scored</td>\n",
       "      <td>4418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Strike rate</td>\n",
       "      <td>129.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Average</td>\n",
       "      <td>37.4407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Century</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fifty</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Attributes   Values\n",
       "0         Name  V Kohli\n",
       "1  Runs Scored     4418\n",
       "2  Strike rate  129.827\n",
       "3      Average  37.4407\n",
       "4      Century        4\n",
       "5        Fifty       30"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attribute_player = [\"Name\",\"Runs Scored\",\"Strike rate\",\"Average\",\"Century\",\"Fifty\"]\n",
    "value_player = [name,total_runs,strike_rate,total_runs/current_player_dismisal_count,century,fifty]\n",
    "player_consolidated = {\"Attributes\":attribute_player,\"Values\":value_player}\n",
    "player_consolidated_dataframe = pd.DataFrame(player_consolidated)\n",
    "player_consolidated_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9971641204866131\n",
      "0.9600000000000002\n",
      "-1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from scipy import spatial\n",
    "\n",
    "dataSetI = [0.8,0.6]\n",
    "dataSetII = [0.8,0.7]\n",
    "dataSetIII = [6,8]\n",
    "dataset4 = [-0.8,-0.6]\n",
    "datset5 = [32,24]\n",
    "print(1 - spatial.distance.cosine(dataSetI, dataSetII))\n",
    "print(1 - spatial.distance.cosine(dataSetI, dataSetIII))\n",
    "print(1 - spatial.distance.cosine(dataSetI, dataset4))\n",
    "print(1 - spatial.distance.cosine(dataSetI, datset5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete Analysis of Players (Batsman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def per_batsman_analysis(batsman_name,Players_Runs,  Players_SR):\n",
    "    #print(batsman_name)\n",
    "    Player_Match = []\n",
    "    for i in range(len(Player_Runs[0:][1])):\n",
    "        if(Player_Runs[0:][1][i]==batsman_name and Player_Runs[0:][3][i]==0):\n",
    "            Player_Match.append([[Player_Runs[0:][0][i]],[Player_Runs[0:][1][i]],[Player_Runs[0:][2][i]],[Player_Runs[0:][6][i]]])\n",
    "    per_batsman_analysis1(batsman_name,Player_Match,Players_Runs,  Players_SR)\n",
    "def per_batsman_analysis1(batsman_name,Player_Match,Players_Runs,  Players_SR):\n",
    "    Player_Balls = []\n",
    "    for i in range(len(Player_Runs[0:][1])):\n",
    "        if(Player_Runs[0:][1][i] == batsman_name and Player_Runs[0:][3][i]==0 and Player_Runs[0:][4][i]==0 ):\n",
    "            Player_Balls.append([[Player_Runs[0:][0][i]],[Player_Runs[0:][1][i]],[Player_Runs[0:][2][i]]])\n",
    "    per_batsman_analysis2(batsman_name,Player_Match,Player_Balls,Players_Runs,  Players_SR)\n",
    "def per_batsman_analysis2(batsman_name,Player_Match,Player_Balls,Players_Runs,  Players_SR):\n",
    "    count = 0\n",
    "    #print(Player_Balls)\n",
    "    for i in range(len(Player_Match)):\n",
    "        #if(Player_Match[i][2][0]):\n",
    "            count += 1\n",
    "    Player_Match_Runs = []\n",
    "    Runs = []\n",
    "    match_no = Player_Match[0][0]\n",
    "    sum = 0\n",
    "    for i in range(len(Player_Match)):\n",
    "        if(Player_Match[i][0] == match_no):\n",
    "            sum += Player_Match[i][2][0]\n",
    "        else:\n",
    "            Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "            Runs.append(sum)\n",
    "            sum = Player_Match[i][2][0]\n",
    "            match_no =  Player_Match[i][0];\n",
    "    Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "    Runs.append(sum)\n",
    "    total_runs = 0\n",
    "    k = len(Runs)\n",
    "    for i in range(k):\n",
    "        total_runs += Runs[i]\n",
    "    #print(total_runs)\n",
    "    #print(total_runs/len(Player_Balls)*100)\n",
    "    player_dismisal_count = 0\n",
    "    for item in dismisal_list:\n",
    "        if(item == batsman_name):\n",
    "            player_dismisal_count = player_dismisal_count+1\n",
    "    Player_ball_faced.append(player_dismisal_count)\n",
    "    Players_Runs.append(total_runs)\n",
    "    Players_SR.append(total_runs/len(Player_Balls)*100)\n",
    "    Players_avg.append(total_runs/player_dismisal_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
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     ]
    }
   ],
   "source": [
    "Players_Runs = []\n",
    "Players_SR = []\n",
    "Players_avg = []\n",
    "Player_ball_faced = []\n",
    "count = 0\n",
    "for Name_bat in player_list:\n",
    "    per_batsman_analysis(Name_bat,Players_Runs,  Players_SR)\n",
    "    count += 1\n",
    "    #if(count>20):\n",
    "        #plt.scatter(Players_Runs,Players_SR)    \n",
    "        #plt.show()\n",
    "    print(count)\n",
    "    #print(Name_bat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1993, 1107, 6, 11, 98, 98, 15, 6, 1687, 4, 3626, 974, 167, 15, 2, 88, 4, 34, 1295, 91, 14, 606, 12, 26, 278, 9, 1977, 241, 2334, 181, 56, 2, 38, 1553, 12, 145, 394, 127, 3, 768, 19, 270, 531, 55, 0, 3, 18, 69, 342, 81, 94, 0, 103, 2, 4014, 11, 96, 106, 159, 5, 6, 125, 280, 3, 11, 16, 2385, 16, 384, 2, 2, 0, 326, 7, 422, 136, 2, 88, 226, 4, 0, 423, 1262, 77, 24, 39, 676, 33, 192, 521, 76, 59, 2904, 1139, 113, 280, 577, 20, 193, 36, 166, 5, 1426, 969, 25, 39, 6, 1112, 498, 1, 1802, 52, 26, 0, 974, 390, 295, 120, 2, 893, 342, 125, 87, 10, 19, 3, 46, 1557, 9, 8, 1, 90, 1732, 10, 22, 375, 35, 1349, 5, 1563, 4, 8, 0, 2215, 5, 131, 42, 46, 527, 20, 814, 420, 193, 73, 798, 4, 92, 54, 29, 6, 10, 124, 3473, 2622, 218, 92, 241, 1158, 31, 231, 41, 5, 163, 1, 67, 117, 1603, 153, 0, 33, 177, 401, 164, 725, 137, 20, 22, 140, 169, 49, 1322, 799, 237, 7, 117, 158, 62, 439, 177, 36, 1, 51, 12, 126, 2322, 128, 31, 21, 327, 368, 0, 217, 1153, 2587, 73, 259, 106, 724, 147, 515, 9, 28, 20, 117, 225, 17, 3, 564, 203, 130, 23, 886, 3778, 15, 0, 179, 506, 1, 15, 39, 111, 527, 1400, 2416, 2427, 49, 196, 36, 4418, 170, 25, 2, 766, 1001, 300, 198, 3560, 34, 406, 19, 9, 1441, 194, 316, 205, 7, 304, 339, 3, 79, 985, 10, 99, 187, 15, 34, 608, 7, 23, 42, 26, 64, 47, 3, 4, 18, 38, 2344, 4132, 124, 47, 101, 0, 405, 2728, 417, 66, 271, 574, 304, 7, 807, 11, 35, 10, 28, 14, 6, 4207, 1648, 285, 13, 319, 13, 161, 121, 3, 2, 1, 353, 1079, 437, 87, 76, 1228, 19, 954, 2903, 514, 159, 0, 121, 1, 0, 411, 340, 10, 2477, 26, 302, 663, 29, 52, 183, 4, 24, 99, 48, 4, 20, 12, 604, 7, 1, 53, 26, 18, 340, 53, 30, 6, 40, 123, 13, 23, 59, 40, 409, 0, 91, 127, 18, 1, 7, 2511, 53, 2, 4, 32, 317, 18, 12, 36, 10, 8, 1276, 373, 63, 12, 739, 2, 5, 505, 37, 95, 1017, 15, 20, 2069, 3561, 112, 19, 43, 391, 81, 2174, 280, 127, 49, 46, 21, 23, 672, 1703, 282, 8, 63, 34, 75, 1, 151, 35, 116, 14, 45, 52, 19, 3, 50, 20, 12, 81, 19, 42, 4, 388, 36, 14, 726, 480, 51, 25, 39, 5, 18, 3057, 189, 63, 126, 7, 8, 957, 3, 11, 83, 854, 2, 55, 24, 2753, 4540]\n"
     ]
    }
   ],
   "source": [
    "print(Players_Runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[124.87468671679198, 137.51552795031057, 46.15384615384615, 122.22222222222223, 83.76068376068376, 104.25531914893618, 55.55555555555556, 85.71428571428571, 121.19252873563218, 30.76923076923077, 151.209341117598, 129.86666666666667, 126.51515151515152, 53.57142857142857, 40.0, 88.88888888888889, 133.33333333333331, 100.0, 127.96442687747036, 112.34567901234568, 233.33333333333334, 128.1183932346723, 100.0, 108.33333333333333, 143.29896907216494, 75.0, 122.64267990074443, 129.56989247311827, 119.81519507186857, 126.57342657342659, 74.66666666666667, 66.66666666666666, 122.58064516129032, 118.6401833460657, 66.66666666666666, 133.0275229357798, 127.50809061488673, 127.0, 150.0, 144.36090225563908, 73.07692307692307, 116.88311688311688, 117.21854304635761, 79.71014492753623, 0.0, 60.0, 72.0, 81.17647058823529, 120.4225352112676, 119.11764705882352, 104.44444444444446, 0.0, 100.98039215686273, 25.0, 142.1388101983003, 68.75, 97.95918367346938, 176.66666666666666, 112.7659574468085, 38.46153846153847, 120.0, 97.65625, 111.55378486055776, 50.0, 61.111111111111114, 35.55555555555556, 135.20408163265304, 69.56521739130434, 158.02469135802468, 100.0, 50.0, 0.0, 138.72340425531914, 100.0, 137.45928338762215, 120.35398230088497, 20.0, 104.76190476190477, 134.52380952380955, 57.14285714285714, 0.0, 124.7787610619469, 126.32632632632632, 140.0, 68.57142857142857, 169.56521739130434, 117.56521739130434, 76.74418604651163, 107.86516853932584, 129.92518703241896, 78.35051546391753, 147.5, 145.49098196392785, 120.40169133192389, 104.62962962962963, 89.45686900958466, 141.76904176904176, 66.66666666666666, 137.85714285714286, 124.13793103448276, 105.0632911392405, 62.5, 124.43280977312392, 128.1746031746032, 125.0, 205.26315789473685, 50.0, 120.8695652173913, 130.36649214659687, 33.33333333333333, 123.25581395348837, 68.42105263157895, 74.28571428571429, 0.0, 141.98250728862973, 99.74424552429667, 122.40663900414938, 133.33333333333331, 66.66666666666666, 134.89425981873111, 125.73529411764706, 110.61946902654867, 114.4736842105263, 71.42857142857143, 79.16666666666666, 50.0, 102.22222222222221, 130.73047858942067, 90.0, 72.72727272727273, 100.0, 93.75, 122.92405961674946, 111.11111111111111, 100.0, 118.29652996845425, 145.83333333333331, 106.80918448139352, 100.0, 141.44796380090497, 200.0, 72.72727272727273, 0.0, 120.11930585683297, 62.5, 98.49624060150376, 91.30434782608695, 97.87234042553192, 135.47557840616966, 105.26315789473684, 123.33333333333334, 123.16715542521995, 119.87577639751552, 97.33333333333334, 113.67521367521367, 44.44444444444444, 93.87755102040816, 154.2857142857143, 111.53846153846155, 60.0, 58.82352941176471, 187.87878787878788, 148.1655290102389, 138.65679534637758, 94.37229437229438, 101.0989010989011, 96.01593625498009, 126.9736842105263, 119.23076923076923, 96.25, 66.12903225806451, 166.66666666666669, 123.48484848484848, 50.0, 104.6875, 82.97872340425532, 130.43124491456467, 113.33333333333333, 0.0, 137.5, 173.52941176470588, 112.64044943820224, 102.49999999999999, 125.64991334488735, 135.64356435643566, 71.42857142857143, 66.66666666666666, 99.29078014184397, 112.66666666666667, 84.48275862068965, 122.97674418604652, 142.42424242424244, 113.9423076923077, 50.0, 104.46428571428572, 100.63694267515923, 116.98113207547169, 165.66037735849056, 143.90243902439025, 73.46938775510205, 50.0, 102.0, 109.09090909090908, 136.95652173913044, 117.21352852094901, 164.1025641025641, 86.11111111111111, 80.76923076923077, 108.63787375415282, 126.02739726027397, 0.0, 103.33333333333334, 114.49851042701091, 131.1866125760649, 94.8051948051948, 103.60000000000001, 98.14814814814815, 125.91304347826086, 132.43243243243242, 113.68653421633555, 69.23076923076923, 96.55172413793103, 76.92307692307693, 144.44444444444443, 114.79591836734696, 85.0, 37.5, 151.20643431635386, 130.12820512820514, 107.43801652892562, 88.46153846153845, 109.78934324659231, 131.6376306620209, 115.38461538461537, 0.0, 116.23376623376625, 130.0771208226221, 33.33333333333333, 78.94736842105263, 73.58490566037736, 109.9009900990099, 145.98337950138506, 125.22361359570662, 126.16187989556136, 109.22592259225922, 125.64102564102564, 118.7878787878788, 138.46153846153845, 129.8266235674405, 124.08759124087592, 96.15384615384616, 50.0, 126.82119205298012, 134.72409152086138, 100.0, 92.5233644859813, 136.7127496159754, 154.54545454545453, 142.4561403508772, 111.76470588235294, 64.28571428571429, 118.8943894389439, 116.1676646706587, 142.98642533936652, 138.51351351351352, 77.77777777777779, 112.1771217712177, 124.17582417582418, 42.857142857142854, 101.28205128205127, 117.82296650717703, 58.82352941176471, 139.43661971830986, 152.03252032520325, 83.33333333333334, 130.76923076923077, 130.752688172043, 58.333333333333336, 74.19354838709677, 100.0, 37.142857142857146, 77.10843373493977, 138.23529411764704, 75.0, 80.0, 150.0, 122.58064516129032, 146.59161976235146, 124.60796139927623, 127.83505154639174, 71.21212121212122, 134.66666666666666, 0.0, 115.71428571428572, 155.44159544159544, 136.27450980392157, 88.0, 147.2826086956522, 173.41389728096678, 116.47509578544062, 175.0, 128.50318471337582, 122.22222222222223, 74.46808510638297, 83.33333333333334, 100.0, 51.85185185185185, 85.71428571428571, 130.8960796515246, 117.29537366548041, 110.03861003861005, 56.52173913043478, 129.1497975708502, 100.0, 93.6046511627907, 107.07964601769913, 100.0, 100.0, 100.0, 129.77941176470588, 126.6431924882629, 128.90855457227138, 129.8507462686567, 138.1818181818182, 164.3908969210174, 82.6086956521739, 126.35761589403972, 125.94360086767895, 109.36170212765957, 92.98245614035088, 0.0, 101.68067226890756, 16.666666666666664, 0.0, 129.24528301886792, 93.66391184573003, 38.46153846153847, 132.7438370846731, 54.166666666666664, 122.76422764227641, 121.875, 111.53846153846155, 104.0, 105.78034682080926, 133.33333333333331, 171.42857142857142, 111.23595505617978, 150.0, 80.0, 90.9090909090909, 133.33333333333331, 131.87772925764193, 87.5, 50.0, 176.66666666666666, 118.18181818181819, 46.15384615384615, 108.28025477707006, 103.921568627451, 100.0, 42.857142857142854, 153.84615384615387, 123.0, 162.5, 100.0, 103.50877192982458, 114.28571428571428, 113.9275766016713, 0.0, 71.09375, 141.11111111111111, 163.63636363636365, 100.0, 58.333333333333336, 123.39066339066338, 74.64788732394366, 100.0, 100.0, 106.66666666666667, 115.69343065693431, 112.5, 70.58823529411765, 92.3076923076923, 76.92307692307693, 88.88888888888889, 119.58762886597938, 139.17910447761196, 84.0, 133.33333333333331, 110.62874251497006, 28.57142857142857, 166.66666666666669, 120.8133971291866, 137.03703703703704, 179.24528301886792, 123.72262773722629, 93.75, 111.11111111111111, 138.39464882943145, 121.86858316221767, 109.80392156862746, 146.15384615384613, 138.70967741935485, 146.44194756554307, 176.08695652173913, 115.5154091392136, 145.0777202072539, 92.02898550724638, 84.48275862068965, 83.63636363636363, 91.30434782608695, 85.18518518518519, 116.46447140381282, 131.7092034029389, 105.61797752808988, 53.333333333333336, 95.45454545454545, 61.81818181818181, 129.31034482758622, 50.0, 98.69281045751634, 94.5945945945946, 170.58823529411765, 107.6923076923077, 77.58620689655173, 110.63829787234043, 111.76470588235294, 60.0, 98.0392156862745, 95.23809523809523, 60.0, 110.95890410958904, 86.36363636363636, 71.1864406779661, 57.14285714285714, 128.05280528052805, 120.0, 107.6923076923077, 132.24043715846994, 158.41584158415841, 100.0, 80.64516129032258, 95.1219512195122, 35.714285714285715, 100.0, 120.59171597633136, 132.16783216783216, 143.1818181818182, 140.0, 77.77777777777779, 72.72727272727273, 132.73231622746187, 50.0, 57.89473684210527, 113.69863013698631, 121.13475177304964, 20.0, 85.9375, 109.09090909090908, 131.22020972354625, 139.09313725490196]\n"
     ]
    }
   ],
   "source": [
    "print(Players_SR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[39.86, 35.70967741935484, 6.0, 5.5, 16.333333333333332, 16.333333333333332, 7.5, 6.0, 25.953846153846154, 4.0, 40.741573033707866, 36.074074074074076, 55.666666666666664, 5.0, 2.0, 5.5, 2.0, 6.8, 30.11627906976744, 22.75, 14.0, 20.2, inf, 13.0, 23.166666666666668, 9.0, 38.76470588235294, 24.1, 33.82608695652174, 45.25, 9.333333333333334, 2.0, 7.6, 20.986486486486488, 6.0, 18.125, 28.142857142857142, 21.166666666666668, inf, 27.428571428571427, 6.333333333333333, 15.882352941176471, 29.5, 11.0, nan, 1.5, 4.5, 13.8, 11.4, 20.25, 13.428571428571429, 0.0, 14.714285714285714, 2.0, 40.14, 5.5, 13.714285714285714, 21.2, 14.454545454545455, 5.0, inf, 17.857142857142858, 18.666666666666668, 1.5, 11.0, 1.3333333333333333, 28.392857142857142, 5.333333333333333, 38.4, 2.0, 2.0, 0.0, 23.285714285714285, 7.0, 19.181818181818183, 17.0, 0.5, 44.0, 22.6, 4.0, nan, 21.15, 22.945454545454545, 12.833333333333334, 4.0, 7.8, 21.125, 8.25, 24.0, 27.42105263157895, 15.2, 29.5, 29.632653061224488, 21.49056603773585, 16.142857142857142, 15.555555555555555, 44.38461538461539, 3.3333333333333335, 27.571428571428573, 12.0, 7.545454545454546, 2.5, 25.464285714285715, 28.5, 25.0, 13.0, 6.0, 20.21818181818182, 20.75, 1.0, 28.15625, 3.466666666666667, 26.0, 0.0, 24.35, 20.526315789473685, 19.666666666666668, 17.142857142857142, 1.0, 23.5, 16.285714285714285, 25.0, 21.75, 10.0, 19.0, 3.0, 6.571428571428571, 25.524590163934427, 9.0, 8.0, inf, 11.25, 24.055555555555557, 10.0, 7.333333333333333, 19.736842105263158, 17.5, 25.452830188679247, 5.0, 34.733333333333334, inf, 2.6666666666666665, 0.0, 28.397435897435898, inf, 18.714285714285715, 14.0, 11.5, 20.26923076923077, 10.0, 17.695652173913043, 18.26086956521739, 27.571428571428573, 18.25, 21.0, 4.0, 13.142857142857142, 9.0, 14.5, 3.0, 10.0, 24.8, 38.16483516483517, 30.847058823529412, 14.533333333333333, 11.5, 16.066666666666666, 25.17391304347826, 10.333333333333334, 10.5, 5.857142857142857, 5.0, 23.285714285714285, 1.0, 22.333333333333332, 8.357142857142858, 27.16949152542373, 21.857142857142858, 0.0, 16.5, 29.5, 19.095238095238095, 16.4, 30.208333333333332, 17.125, 5.0, 7.333333333333333, 14.0, 16.9, 7.0, 26.979591836734695, 15.666666666666666, 14.8125, 7.0, 9.75, 8.31578947368421, 31.0, 36.583333333333336, 17.7, 4.5, 1.0, 10.2, 6.0, 15.75, 21.700934579439252, 25.6, 15.5, 10.5, 29.727272727272727, 18.4, nan, 16.692307692307693, 26.813953488372093, 25.613861386138613, 18.25, 14.38888888888889, 13.25, 23.35483870967742, 18.375, 12.261904761904763, inf, 28.0, 10.0, 16.714285714285715, 18.75, 8.5, 3.0, 25.636363636363637, 40.6, 16.25, 7.666666666666667, 27.6875, 29.515625, 7.5, 0.0, 17.9, 33.733333333333334, 1.0, 15.0, 13.0, 27.75, 23.954545454545453, 33.333333333333336, 27.146067415730336, 28.55294117647059, 9.8, 21.77777777777778, 36.0, 37.440677966101696, 17.0, 12.5, 2.0, 20.157894736842106, 35.75, 15.0, 9.9, 37.87234042553192, 17.0, 21.36842105263158, 6.333333333333333, inf, 30.659574468085108, 21.555555555555557, 31.6, 29.285714285714285, 3.5, 10.857142857142858, 14.125, 3.0, 8.777777777777779, 22.386363636363637, 10.0, 9.9, 23.375, 5.0, 6.8, 20.96551724137931, 3.5, 11.5, 21.0, 2.1666666666666665, 9.142857142857142, 5.222222222222222, 3.0, 4.0, 4.5, 19.0, 28.585365853658537, 31.541984732824428, 12.4, 11.75, 50.5, nan, 15.0, 27.555555555555557, 26.0625, 4.714285714285714, 13.55, 26.09090909090909, 21.714285714285715, inf, 27.82758620689655, 11.0, 11.666666666666666, 5.0, inf, 7.0, 6.0, 32.6124031007752, 29.428571428571427, 20.357142857142858, 2.6, 21.266666666666666, 6.5, 12.384615384615385, 15.125, 3.0, inf, inf, 17.65, 39.96296296296296, 31.214285714285715, 12.428571428571429, 12.666666666666666, 25.06122448979592, 19.0, 26.5, 24.811965811965813, 22.347826086956523, 19.875, 0.0, 30.25, 0.5, 0.0, 31.615384615384617, 11.724137931034482, 5.0, 39.317460317460316, 2.6, 27.454545454545453, 24.555555555555557, 14.5, 6.5, 18.3, inf, 12.0, 14.142857142857142, 48.0, 4.0, 10.0, inf, 21.571428571428573, 3.5, inf, 26.5, 26.0, 9.0, 8.947368421052632, 10.6, 10.0, 3.0, 20.0, 24.6, 13.0, 7.666666666666667, 14.75, 13.333333333333334, 20.45, 0.0, 10.11111111111111, 31.75, 18.0, inf, 3.5, 26.431578947368422, 8.833333333333334, inf, inf, 10.666666666666666, 15.095238095238095, 9.0, 12.0, 12.0, 10.0, 4.0, 28.355555555555554, 14.346153846153847, 7.0, inf, 28.423076923076923, 2.0, inf, 22.954545454545453, 12.333333333333334, 11.875, 25.425, 15.0, 20.0, 27.223684210526315, 32.669724770642205, 8.0, 19.0, 10.75, 27.928571428571427, 10.125, 28.233766233766232, 18.666666666666668, 11.545454545454545, 9.8, 11.5, 21.0, 7.666666666666667, 17.68421052631579, 37.02173913043478, 14.842105263157896, 8.0, 15.75, 8.5, 15.0, inf, 11.615384615384615, 35.0, 12.88888888888889, 7.0, 7.5, 13.0, 19.0, 3.0, 10.0, 6.666666666666667, 4.0, 10.125, 9.5, 6.0, inf, 20.42105263157895, 9.0, 7.0, 29.04, 34.285714285714285, 10.2, 8.333333333333334, 6.5, 2.5, 9.0, 33.59340659340659, 21.0, 7.875, 11.454545454545455, 2.3333333333333335, 8.0, 24.53846153846154, 1.5, 3.6666666666666665, 13.833333333333334, 21.35, 2.0, 18.333333333333332, 12.0, 28.091836734693878, 33.88059701492537]\n"
     ]
    }
   ],
   "source": [
    "print(Players_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling NaN values in Average Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "P_avg = []\n",
    "for item in Players_avg:\n",
    "    if(str(item) == 'nan'):\n",
    "        P_avg.append(0)\n",
    "    elif(str(item)=='inf'):\n",
    "        P_avg.append(0)\n",
    "    else:\n",
    "        P_avg.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[39.86, 35.70967741935484, 6.0, 5.5, 16.333333333333332, 16.333333333333332, 7.5, 6.0, 25.953846153846154, 4.0, 40.741573033707866, 36.074074074074076, 55.666666666666664, 5.0, 2.0, 5.5, 2.0, 6.8, 30.11627906976744, 22.75, 14.0, 20.2, 0, 13.0, 23.166666666666668, 9.0, 38.76470588235294, 24.1, 33.82608695652174, 45.25, 9.333333333333334, 2.0, 7.6, 20.986486486486488, 6.0, 18.125, 28.142857142857142, 21.166666666666668, 0, 27.428571428571427, 6.333333333333333, 15.882352941176471, 29.5, 11.0, 0, 1.5, 4.5, 13.8, 11.4, 20.25, 13.428571428571429, 0.0, 14.714285714285714, 2.0, 40.14, 5.5, 13.714285714285714, 21.2, 14.454545454545455, 5.0, 0, 17.857142857142858, 18.666666666666668, 1.5, 11.0, 1.3333333333333333, 28.392857142857142, 5.333333333333333, 38.4, 2.0, 2.0, 0.0, 23.285714285714285, 7.0, 19.181818181818183, 17.0, 0.5, 44.0, 22.6, 4.0, 0, 21.15, 22.945454545454545, 12.833333333333334, 4.0, 7.8, 21.125, 8.25, 24.0, 27.42105263157895, 15.2, 29.5, 29.632653061224488, 21.49056603773585, 16.142857142857142, 15.555555555555555, 44.38461538461539, 3.3333333333333335, 27.571428571428573, 12.0, 7.545454545454546, 2.5, 25.464285714285715, 28.5, 25.0, 13.0, 6.0, 20.21818181818182, 20.75, 1.0, 28.15625, 3.466666666666667, 26.0, 0.0, 24.35, 20.526315789473685, 19.666666666666668, 17.142857142857142, 1.0, 23.5, 16.285714285714285, 25.0, 21.75, 10.0, 19.0, 3.0, 6.571428571428571, 25.524590163934427, 9.0, 8.0, 0, 11.25, 24.055555555555557, 10.0, 7.333333333333333, 19.736842105263158, 17.5, 25.452830188679247, 5.0, 34.733333333333334, 0, 2.6666666666666665, 0.0, 28.397435897435898, 0, 18.714285714285715, 14.0, 11.5, 20.26923076923077, 10.0, 17.695652173913043, 18.26086956521739, 27.571428571428573, 18.25, 21.0, 4.0, 13.142857142857142, 9.0, 14.5, 3.0, 10.0, 24.8, 38.16483516483517, 30.847058823529412, 14.533333333333333, 11.5, 16.066666666666666, 25.17391304347826, 10.333333333333334, 10.5, 5.857142857142857, 5.0, 23.285714285714285, 1.0, 22.333333333333332, 8.357142857142858, 27.16949152542373, 21.857142857142858, 0.0, 16.5, 29.5, 19.095238095238095, 16.4, 30.208333333333332, 17.125, 5.0, 7.333333333333333, 14.0, 16.9, 7.0, 26.979591836734695, 15.666666666666666, 14.8125, 7.0, 9.75, 8.31578947368421, 31.0, 36.583333333333336, 17.7, 4.5, 1.0, 10.2, 6.0, 15.75, 21.700934579439252, 25.6, 15.5, 10.5, 29.727272727272727, 18.4, 0, 16.692307692307693, 26.813953488372093, 25.613861386138613, 18.25, 14.38888888888889, 13.25, 23.35483870967742, 18.375, 12.261904761904763, 0, 28.0, 10.0, 16.714285714285715, 18.75, 8.5, 3.0, 25.636363636363637, 40.6, 16.25, 7.666666666666667, 27.6875, 29.515625, 7.5, 0.0, 17.9, 33.733333333333334, 1.0, 15.0, 13.0, 27.75, 23.954545454545453, 33.333333333333336, 27.146067415730336, 28.55294117647059, 9.8, 21.77777777777778, 36.0, 37.440677966101696, 17.0, 12.5, 2.0, 20.157894736842106, 35.75, 15.0, 9.9, 37.87234042553192, 17.0, 21.36842105263158, 6.333333333333333, 0, 30.659574468085108, 21.555555555555557, 31.6, 29.285714285714285, 3.5, 10.857142857142858, 14.125, 3.0, 8.777777777777779, 22.386363636363637, 10.0, 9.9, 23.375, 5.0, 6.8, 20.96551724137931, 3.5, 11.5, 21.0, 2.1666666666666665, 9.142857142857142, 5.222222222222222, 3.0, 4.0, 4.5, 19.0, 28.585365853658537, 31.541984732824428, 12.4, 11.75, 50.5, 0, 15.0, 27.555555555555557, 26.0625, 4.714285714285714, 13.55, 26.09090909090909, 21.714285714285715, 0, 27.82758620689655, 11.0, 11.666666666666666, 5.0, 0, 7.0, 6.0, 32.6124031007752, 29.428571428571427, 20.357142857142858, 2.6, 21.266666666666666, 6.5, 12.384615384615385, 15.125, 3.0, 0, 0, 17.65, 39.96296296296296, 31.214285714285715, 12.428571428571429, 12.666666666666666, 25.06122448979592, 19.0, 26.5, 24.811965811965813, 22.347826086956523, 19.875, 0.0, 30.25, 0.5, 0.0, 31.615384615384617, 11.724137931034482, 5.0, 39.317460317460316, 2.6, 27.454545454545453, 24.555555555555557, 14.5, 6.5, 18.3, 0, 12.0, 14.142857142857142, 48.0, 4.0, 10.0, 0, 21.571428571428573, 3.5, 0, 26.5, 26.0, 9.0, 8.947368421052632, 10.6, 10.0, 3.0, 20.0, 24.6, 13.0, 7.666666666666667, 14.75, 13.333333333333334, 20.45, 0.0, 10.11111111111111, 31.75, 18.0, 0, 3.5, 26.431578947368422, 8.833333333333334, 0, 0, 10.666666666666666, 15.095238095238095, 9.0, 12.0, 12.0, 10.0, 4.0, 28.355555555555554, 14.346153846153847, 7.0, 0, 28.423076923076923, 2.0, 0, 22.954545454545453, 12.333333333333334, 11.875, 25.425, 15.0, 20.0, 27.223684210526315, 32.669724770642205, 8.0, 19.0, 10.75, 27.928571428571427, 10.125, 28.233766233766232, 18.666666666666668, 11.545454545454545, 9.8, 11.5, 21.0, 7.666666666666667, 17.68421052631579, 37.02173913043478, 14.842105263157896, 8.0, 15.75, 8.5, 15.0, 0, 11.615384615384615, 35.0, 12.88888888888889, 7.0, 7.5, 13.0, 19.0, 3.0, 10.0, 6.666666666666667, 4.0, 10.125, 9.5, 6.0, 0, 20.42105263157895, 9.0, 7.0, 29.04, 34.285714285714285, 10.2, 8.333333333333334, 6.5, 2.5, 9.0, 33.59340659340659, 21.0, 7.875, 11.454545454545455, 2.3333333333333335, 8.0, 24.53846153846154, 1.5, 3.6666666666666665, 13.833333333333334, 21.35, 2.0, 18.333333333333332, 12.0, 28.091836734693878, 33.88059701492537]\n"
     ]
    }
   ],
   "source": [
    "print(P_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of Players Runs vs Strike Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ClassA_Runs = []\n",
    "ClassB_Runs = []\n",
    "ClassC_Runs = []\n",
    "ClassA_SR = []\n",
    "ClassB_SR = []\n",
    "ClassC_SR = []\n",
    "for i in range(len(Players_Runs)-1):\n",
    "    if(Players_Runs[i]>1500 and Players_SR[i]<130):\n",
    "        ClassB_Runs.append(Players_Runs[i])\n",
    "        ClassB_SR.append(Players_SR[i])\n",
    "    elif(Players_Runs[i]>1500 and Players_SR[i]>130):\n",
    "        ClassA_Runs.append(Players_Runs[i])\n",
    "        ClassA_SR.append(Players_SR[i])\n",
    "    else:\n",
    "        ClassC_Runs.append(Players_Runs[i])\n",
    "        ClassC_SR.append(Players_SR[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1t2/ozJlPd3KpffWMZyTs2nU5Dz9cZXKyyjOescjp0+nmf1BS10xN7eT06bP9XoakVb4n\npXzxPSnlj+9LdcpGQWW/hnxfA/wp8AMxxnet3vz3IYSvjTH+BfBC4M+BvwF+NoQwQW0Y+NOpDQDf\nNorFlLvugrvvXqJUWqFYNFySJEmSJEn50q8KptcBu4AfCyHUZzG9CpgJIewA/hl4W4xxJYQwA7yX\n2ryo18cYF/uy4j4KAa666kK/lyFJkiRJkrSuJE2HryLm9OmzQ/WkLGeU8sX3pJQvvielfPE9KeWP\n70t1ytTUzqZXHuvXVeQkSZIkSZI0JAyYJEmSJEmSlIkBkyRJkiRJkjIxYJIkSZIkSVImBkySJEmS\nJEnKxIBJkiRJkiRJmRgwSZIkSZIkKRMDJkmSJEmSJGViwCRJkiRJkqRMDJgkSZIkSZKUiQGTJEmS\nJEmSMjFgkiRJkiRJUiYGTJIkSZIkScrEgEmSJEmSJEmZGDBJkiRJkiQpEwMmSZIkSZIkZWLAJEmS\nJEmSpEwMmCRJkiRJkpSJAZMuUS4nzM6OUi4n/V6KJEmSJEkaEKP9XoDyo1xOmJ6eoFIpMDdXZWZm\nkWIx7feyJEmSJElSzlnBpIvm50eoVGoviUqlwPz8SJ9XJEmSJEmSBoEBky4qlVaYnKwCMDlZpVRa\n6fOKJEmSJEnSILBFThcViykzM4vMz49QKq3YHidJkiRJklpiwKRLFIspxeKFfi9DkiRJkiQNEFvk\nJEmSJEmSlIkBkyRJkiRJkjIxYJIkSZIkSVImBkySJEmSJEnKxIBJkiRJkiRJmRgwSZIkSZIkKRMD\nJkmSJEmSJGViwCRJkiRJkqRMDJgkSZIkSZKUiQGTJEmSJEmSMjFgkiRJkiRJUiYGTJIkSZIkScrE\ngEmSJEmSJEmZGDBtU+VywuzsKOVy0u+lSJIkSZKkATfa7wWo98rlhOnpCSqVAnNzVWZmFikW034v\nS5IkSZIkDSgrmLah+fkRKpXat75SKTA/P9LnFUmSJEmSpEFmwLQNlUorTE5WAZicrFIqrfR5RZIk\nSZIkaZDZIrcNFYspMzOLzM+PUCqt2B4nSZIkSZIysYJpyLQ6vLtYTDl48AKAw74lSZIkSVImBkxD\npD68++jRcaanJy4JjdYLnjY6XpIkSZIkqVUGTEOk2fDuZkGSw74lSZIkSVInGDANkWbDu5sFSQ77\nliRJkiRJneCQ7wFRLiebDuVuNry7VFphbq5KpVK4JEhy2LckSZIkSeqEJE2HL1Q4ffrsUD2pT35y\nJ9/5nRcuBkQzM4tth0GtBFSSWjM1tZPTp8/2exmSVvmelPLF96SUP74v1SlTUzubDm+2gmkAvOc9\nPKHFrVi80NZjFItp239GkiRJkiSpFc5gGgDPfS65nZW03tXp1B2ea0mSJElSXlnBNABCIJezkupX\np6tUCszNba11T63xXEuSJEmS8syAaUDkscVtvavT5W2Nw8JzLUmSJEnKM1vktGWl0kpuW/eGjeda\nkiRJkpRnVjANoLxcEa5YTHPZujeMPNeSJEmSpDwzYBoweZvFk8fWvWHluZYkSZIk5ZUtcgNmvVk8\nneJVyiRJkiRJ0lYYMA2Ybs3iqVdGHT06zvT0hCGTJEmSJElqmS1yA2a9WTydmMnkVcokSZIkSdJW\nGTANoMZZPJ2ayVQqrTA3V6VSKXiVMkmSJEmS1BYDpgHXqcojr1ImSZIkSZK2yoBpwHWy8sirlEmS\nJEmSpK0wYBpwVh5JkiRJkqR+M2AaAlYeSZIkSZKkfir0ewHa3DvfCa985TgnTvjtkiRJkiRJ+WNi\nkXMnThT41m+F48fHuPHGywyZJEmSJElS7phW5NyxY2OcP1/7/fJywrFjY/1dkCiXE2ZnRymXk34v\nRZIkSZKkXDBgyrkDB5bZsaP2+7GxlAMHlvu7oG2uXE6Ynp7g6NFxpqcnDJkkSZIkScKAKff27avy\n1rfC/v3L3H77Ofbtq/Z7Sdva/PwIlUrtbVOpFJifH+nziiRJkiRJ6j+vIjcAXvhCeNazlvq9DAGl\n0gpzc1UqlQKTk1VKpZV+L0nKpFxOmJ8foVRaoVhM+70cSZIkSQPKgElP4IazuWIxZWZm0fOjoVBv\n+axUCszNVZmZWfQ1LUmSJGlLDJgGTLfDHzecmysWU4rFC/1ehpTZei2fvrYlSZIkbYUzmAZILwZM\nO2NI2j5KpRUmJ2tz3Wz5lCRJkpSFAdMAOX58lAcfLHD+fPfCHzec0vZRb/m8+eYlqxUlSZIkZWKL\n3IAolxPuuWeURx9NeOwxeNrTVroS/jhjSNpebPmUJEmS1AkGTANifn6ExcUC11yTcu4cXH/9ha6F\nP244JUmSJElSO2yRGxD11rUdO+Daa6vs328AJEmSJEmS8sEKpgFh65okSZIkScorA6YBYuuaJEmS\nJEnKI1vkJEmSJEmSlIkBkyRJkiRJkjIxYJIkSZIkSVImBkxSF5TLCbOzo5TLSb+XIkmSJElS1xkw\naVvrRhBULidMT09w9Og409MThkySJEmSpKFnwKRtq1tB0Pz8CJVK7a1VqRSYnx/pyONKkiRJkpRX\nBkzatroVBJVKK0xOVgGYnKxSKq105HElSZIkScqr0X4vQOqXUmmFubkqlUqho0FQsZgyM7PI/PwI\npdIKxWLakceVJEmSJCmvDJi0bXUzCCoWU4rFCx17PEmSJEmS8syASduaQZAkSeqG8pmTzJ+6j9Ke\nvRR3Xdfv5UiS1HXOYJIkSZI6qHzmJNPvvomj/zjD9LtvonzmZL+XJElS1xkwDbhyOWF2dvTiFdDW\nfi5JkqTemj91H5WlCgCVpQrzp+7r84okSeo+A6YBVi4nTE9P8KY3jXPDDZcxOzvC9PQER4+OMz09\nYcgkSZLUB6U9e5kcnwRgcnyS0p69fV6RJEnd5wymATY/P8LCQoGHHkpI04Sf/dkJLr8cduyASqXA\n/PyI84UkSZJ6rLjrOmaed6szmCRJ24oB0wArlVaYmUlJ04RCASYmIElSIGFyskqptNLvJUqSJG1L\nxV3XGSxJkrYVA6YBViym3HLLIocPT5CmCbt3Vzl0aIlTpwqUSisUi2m/lyhJkiRJkrYBA6YBt29f\nlbm5c8zPjzSEStW+rKVcTtasQ5IkSZIkbQcGTEOgWEz7PmupPnC8UikwN1dlZmbRkEmSJEmSpG3C\nq8ipI+bnR6hUai+n+oBxdUa5nDA7O+pVASVJkjTUymdOMvuhN1M+c7LfS5G0BQZM6ohSaYXJyVpr\nngPGO6deGXb06DjT0xOGTJIkSRpK5TMnmX73TRz9xxmm332TIZM0gPraIhdCeA7w8zHGrw0hfDlw\nN1BevftojPH3QwgvA14BXAB+JsZ4d5+Wqw0UiykzM4vOYOqw9SrD+t0OKUmSJHXa/Kn7qCxVAKgs\nVZg/dZ9XYpQGTN8CphDCfwe+C/j31ZueCbwxxnik4ZjPBKaBZwETwF+FEP4sxrjU6/X2U4xw992j\nF6uCmoU4/R6ynYdZUMOmVFphbq5KpVKwMkySJElDq7RnL3MfnqWyVGFyfJLSnr39XpKkNvWzgulf\ngAPA76x+/kwghBD2U6ti+iHg2cBfrwZKSyGEjwBfAvxtH9bbF+VywmteAw8/PM6dd9Za0BYXnzhI\n2yHbw8nKMEmSJG0HxV3XMfO8W5k/dR+lPXutXpIGUN8Cphjj20MIn9tw098At8cYPxBCeD3w48A/\nAI82HHMWuHKzx96160mMjg7HkOl3vAPOnIGRkQKnTxdIErjySjh7tsCHPnQ5e/c+ftzZszAy8sT7\nNNimpvB7mUNTUzv7vQRJDXxPSvnie1JbMTX1TPZe98x+L2No+b5Ut/V1BtMafxhjrNR/D/wq8B6g\n8V2wE6is/YNrnTnz6c6vrk+e8YyEXbsu5+GHq0xNPV7BNDlZ5RnPWOT06fTicTt3TlxspWq8T1Jn\nTU3t5PTps/1ehqRVvielfPE9OVjKZ05aNbQN+L5Up2wUVOYpYPqTEMIPxhj/Bng+8AFqVU0/G0KY\nAMaBpwMf7OMae65YTLnrLrj77qUNZzDZSiVJkiSpHfUrt1WWKsx9eJaZ591qyCRpy/IUMN0M/GoI\nYRn4BPDyGONjIYQZ4L1AAXh9jHGxn4vshxDgqqseH57dbJB2fch2uZwwOzs60EFTvweWS5IkScPO\nK7dJ6qQkTYdv83769NmhelLtlDM2DvuenBzMYd/D8Bw03CwxlvLF96SUL74nB0djBdPk+KQVTEPM\n96U6ZWpqZ9LsvjxVMKkD5udHqFQKAFQqBebnR5pWPOVVP56DFVOSpO3GuSuSvHKbpE4q9HsBak+9\n/a1cTtb9vFRaYXKyNgx8crJ6cW7TIOn1c6hXTB09Os709MTFc9kra7+HkiR1W71q4eg/zjD97pso\nnznZ7yVJ6pPirus4+IzvNlySlJkVTAOksXVsbq7KoUNLHDkyfvHzeivZoA/77vVz6GfV19rvqe2A\nkqRecO6KrGCTJHWaFUwDZG0QcuzY2BOCEagFNAcPXhjooKL+HICuV/f0s+prvXBLkqRuK+3Zy+T4\nJACT45OU9uzt84rUS1awSZK6wYBpgKwNQg4cWO5oMJK3Vq1eta7VK6Zuvnmp5xVEw9DSuJG8vaYk\nSTX1uSs3f+m0Q323ofUq2CRJysoWuQGyXuvYtdd2ppVsq61a3RyO3cvWtWIx7csw9GFoaWzG9j9J\nyrfirusMlrap0p69zH149uKVw6xgkyR1ggHTgFkbhHQqGNlKmNPtAKFUWmFurkqlUhjK6p66foVb\n3TYMVzSUJGkYeeUwSVI32CI3pNptTdpKq1a35wcViymHDi3x5V9+gUOHltYNr2zByq9hb/+TJGmQ\neeUwSVKnWcE0hLZSWbSVVq1uVxiVy8nFq+Tdf3+Ba6+99Hl0q4Kqm21/28kwt/9JkiRJki5lwDSE\nttqa1G6rVrcDhM2eRzdasJwb1FkbvabyHuTlfX2SJElSu8pnTtoeq66xRW4I9bI1qVhMOXjwQlc2\n4Js9j248z263/WU1LC2BvbpC4FblfX2SJElSu8pnTjL97ps4+o8zTL/7JspnTvZ7SRoyVjANoayV\nRXmp3NjseXSjgirPg8WHqboq7wPA874+9Zf/8ydJkgbR/Kn7qCxVAKgsVZg/dZ8/y6ijDJiG1Fav\nTJa3EGOz59HpK7DleW7QMIUeeQ7yIP/rU//U/+evslRh7sOzzDzvVn8wkyRJA6G0Zy9zH56lslRh\ncnyS0p69/V6ShowBky4xTCHGVnU6tOqUYQo98hzkQf7Xp/7xf/4kSdKgKu66jpnn3WoltrrGgGmb\naLXtrVRa4c47qzz0UIFrrhnsEGPYDFvokdcgry7v61N/+D9/kiRpkBV3XWewpK4xYNoGmrW9bRQ6\nJUM80zgvM6a2wtBD6i//50+SJElanwHTkNgoNFn/ymgr64ZO8/MjLC4WuOIKWFwcrBa5VoKjvM2Y\nkjR4/J8/aTg4sF+SpM4q9HsByqZcTvilXxrj5S+f4E1vGueGGy7jxIlLv6179lRZXk45f56Ls3vW\nD51qLXKTk1WAzHN+yuWE2dnRnlzivdXLyjd73pKk7al85iSzH3qzl2reZrxUtyRJnWcF0wCrhyoP\nPFDgscdqgUqaJhw+PMHc3LmLbXBHjoyTpglJknLo0NJqxc76A6OzzvmpVxHt2VPlyJHxnlUKtTqc\nfJgGZUuSsvGqgNuXA/slSeo8A6YBNj8/wsJCgZUVWFnNSUZGaiFTPWCpBy87dgAknDpVAKobBklb\nnfPT2H62vJySpgk7dvTmanStBkdbCdAGeWaTBpOvOak3DBm2Lwf2S5LUeQZMA2zPnioLC7C8XKtO\nuuqqlImJhN27Hw9YNgpemgVJW93cNlYR1SumIOlJpVA7wVE7AZozm9Rrvuak3jFk2L4c2C9JUucZ\nMA2wf/iHkdUgB0ZGEr75m8/z9KenlwQs7VbsZNncNoZZu3dXOXRoiVOnCj2rwujGFdZabb2TOsXX\nnNQ7hgzbmwP7pe5wgL60fRkwDbgkgUKh9vHqq+HgwSduRNsJXrJsbtcPs6otP5e8KZcTFhYSJiaq\nLC46s0m94ZwwqbcMGSSpc5xtJ21vbQdMIYRvAl4EfA7wOuDfgecDd8YYFzu7PG1k//4L3HPPKAsL\ntYqh/fuK7WtKAAAgAElEQVSzVzk0bm4nJqosLCSUy0nLFUjdqCLqh8ZKromJKi9+8Xn277/QlUos\n5+2oUdZB+5IkSf3ibDtpeytsfkhNCGEshHAceAfwvcALgF3AlwG/DrwnhLCrK6vUuorFlNtuW+RH\nfmSJ227rzJyW+ub2xS8+D8Db376D6ekJyuUk82MPksZKrsXFArt3p10Ll6anJzh6dHxbnuduKJcT\nZmdHB/pcFospBw92J9CUpHaUz5xk9kNvpnzmZL+XImkAlPbsZXJ8EsDZdtI21HLABPwocD3wCuDz\ngPru7RjwKmpB0xs6ujptKstGtNlGvFhM2b07ZXHx0la5ThiUzX+ptMLkZK29r5ttSuu1JGrrehHY\nlcsJv/Vb5P41LElZ1Vtdjv7jDNPvvsmQSdKm6rPtbv7SadvjpG2onYDpJcAdMcbbgXP1G2OMF2KM\nvwrcBuzv8PrUJZttxLsRsAxStU69kuvmm5e6ehWvXgVZ20W3A7v6a/iXfoncv4YlKav1Wl0kaTPF\nXddx8BnfbbgkbUPtBEyfDfzdBvf/E/CUbMtRr2y2Ee9GwDJo1Tq9aFPqVZC1XXQ7sBu017AkZWGr\niyRJakc7Q77/DfjCDe5/NvDxbMvRet75TrjjjnE+//NX+OhHRzhwYJl9+7JdnW29K1WtHTZdH9hd\nb2vLOnDYq2Otb1gGo+dBtwdk11/DZ8/6GpY0/OqtLl5uXJIktSJJ09Y2YCGEnwFeTa0N7u+B08Dz\nY4x/HkJ4JTADHIkx/ki3Ftuq06fPDk0ZyIkTBV72ss/g059OSVMYGYHx8ZTbbz+XOWRqDJSAi1dN\nm5ysXqymabyaWuPtnfiaVusoT1p9bZbLCR/60OU84xmf8jUs5cTU1E5Onz7b72WoDeUzJw2uhpjv\nSSl/fF+qU6amdjadE9JOi9xPA/PAnwAfAlLgaAjhNPBrwAeAn8qwTq3j2LExzp+Heg5YrcLycsKx\nY2OZH7uxBaxZ60/99vPn4YEHChw/3rzorZUB3l4dazgMyrD2VrUzH6xYTHnZy/A1LElb5PBwSZKG\nU8sBU4xxCXgB8H3A3wAfXr3rA8APAP8pxvjvHV/hNnfgwDI7dkCyut8tFGBsLOXAgeWOfp1ms2tK\npRUmJqo89FDC2bMJ99yzfqgwSAO8h1EvA59h/F47W0mDxMvGa9A5PFzSIPDfW6l9Lc9gCiF8DnA6\nxvhm4M3r3H9lCOGrYozv6dzytG9flbe+Fe64Y7mjM5jWaja7plhMuf76C9x5Z4GJCVhcrG2+184M\nWm+D7lyh3mhsY5yby97GuJlh/F47H0yDol75UVmqMPfhWS8BrYFU2rOXuQ/PUlmqdHV4eF7b8PK6\nLkmP899baWvaGfJ9P/ASYK7J/d8CvAm4POuidKkXvhCe9ayl1c8u3ch3cqZRs2HT+/df4F3vGt1w\n8+0GvX8a2xgffLDWxvja13a2wq3RMH6vuz0cXMOj3xvD9So//IFXg6YXw8PzujnM67okXcp/b/Oj\n3z97qT1NA6YQwrXASxtuSoAXhxCK6xxeoDb8+1xnl6eN9KpypZXNtxv0jXVzuHmptMKdd1b52MdG\nSFO4555R9u/v3pyrYf1eezU/baadjWG3fhjqVeWH1G3FXdd1daOQ181hXtcl6VL+e5sPhvKDZ6MK\npgeB64GvXP08BQ6s/lpPFXhd55amzWylVWmrQUcrm+/GY/J0tbh+r6UxCLzzzirXX3+B/ftr56kT\n66q3Md5xR4HLLmvexthJ2yGM6ffrRvnT6sawmz8Medl4qTV53RzmdV2SLuW/t/kwDKF8oXySsfn7\nWC7tpVocrLVvRdOAKcaYhhD2AVdRq176KPBDwPF1Dl8BHokxWsHUQ622KtU3ynv2VDlyZLzrFU+t\nVlb1YgO/1SqvTq6tsYXtX/91hDvvLHDPPbW33uJiZ74XrbQxqnW9nmulwdDqxrDbPwx1u/JDGgZ5\n3RzmdV2Snsh/b/tv0EP5QvkkV0zfRFKpkM7N8tjMrUMfMm04gynGeBY4CxBC+Drgn2OMC71YmDbX\nSqtS40Z5eTklTRN27OjucOZWKqs6uYHfKAzaapVXs7VtJXiqB4EPPFAgSWBiAhYWCqQpXHllZ74X\nw9q21i/DOMhc2bW6MRz0H4akYZHXzWFe1yVJeTPoofzY/H0kldp/OiaVCmPz97G0nQOmRjHGvwQI\nIUxSG+RdWPM4O4HnxRh/uaMr1IY2a1Vq3CinaUKSpEDS1SqXViqrOrWB3yyo2spA6mZr22ooVg9/\njh8f5Z57RllcLHDFFbWrAC4udq7iaFDb1vLYijaMg8zVGa1sDAf9hyFJkqS8GORQfrm0l3RutlbB\nNDnJcmn4/9Ox5YAphPBZwNt5fCZTMwZMOdK4Ud69u8qhQ0ucOlXYsOIp62a/lWqaTm3gNwuqWq3y\nary/2dqyhGLFYsprX7vM/v0XLn6t+mN2KljJY1CzmTy3oj3/+bXvbTcHpg+DQXzd9cIg/zAkSZKk\n7KrF63hs5tZtNYMpSdPWNgQhhN8Fvh34fWCJ2hXmfg6Yojb4+zLg+THG93Vnqa07ffrsUO1ypqZ2\ncvr02S3/+VY3gI2b/cnJ7m/2O7Exzbrm+p9fWCiQJCm33LLIvn3VddfW6/PTjjyvbSOzs6McPTp+\n8fObb17i4MH+VmG1ci6zvieHxaC+7jR8fE9K+eJ7Usof35fqlKmpnUmz+wrN7ljHPmA2xvgdwKuo\nXVXuj2OMLwe+HPgU8F+zLFTdUSymHDy4eRVG4zDqBx8scPx4ywVuXV3XZo8xM7PIzTcvbWlzOz8/\nwsJCgYWFhI9/vMDhwxOUy8m6a8v6tbppveqqQVAqrTA5WWsXzEsr2qCdy3I5YXZ2lHK56d/zXTNo\n50qSJElS97QTMO0C/hogxvgY8ADwrNXP/xW4HfjmTi9QvVMqrTAxUWVhIeHRRxPuuaf5prVxU9vP\nDS5kC6pKpRWSJKVahSSpzanaaJPciVCsG/IY1LTyushjaNerc9mJ9029gujo0XGmpyd6/h7M4+tO\nkiRJUn+0U6LySeBJDZ//C/DFaz5/aicWpf4oFlOuv/4Cd9xR4LLLagOoN7sC3J13Pj6sOm8zdNaz\ntvWtWKy1xR0+PEGaJuzePZib5F5fRW6z9sZ2ZivlZTh543Pq9rns1OypXlztbqPvtVcvlFQon9xW\nsyWGid87SVKntVPB9NfA94QQrlz9/P8AzwshTKx+/pXAo51cnNrTiYqI/fsvcO21VXbsaF6R0Lip\nfeihAgsL+WuRWe9cNKv22LevytzcOV71qvxU0WxFr6qryuWEl798gp//+XFe/vL1q2YGrXVq7WsD\n6Oq57NT56XYFUSsVUnmt6pPUfYXySa6YvoknHZ3hiumbKJRP9ntJapHfO0lSN7QTMP0MEIB/DSFc\nDdwGfBbwgRDCvcDLgXs6v0StZ22A0qlWmVZalho3tddcU2X37ny1yDQ7Fxtt6t0kt+748VE+8pER\nKpWEj3xkZN1ZXYPWOtXrQKxT56fbLYZ5CAr73YIrqbmx+ftIKhUAkkqFsfn7+rwitcrvnSSpG1pu\nkYsx/n0I4TnAzTHGR4BHQgjfCdwC7AX+APjv3VmmGq3XXtPJVpnNWpbWtsUAuWqRaXYuSqUV5uaq\nF6941eqm3suwP1H94pPNLkI5aK1TW31tbFUnz083Wwx7fV7W6lQroYZXvcWHF70ArtrT7+VsO8ul\nvaRzsySVCunkJMulvf1eklrk905SL9iKu/0kabMd4haEEEZjjH0fpnL69Nmh2oGsvaRk46Xdz5+H\n5zznAgcOLHPkyHhPLxee1+Cl3sL10EMFrrmmym23PX4u2l2zl2F/oo3O7yBr57WxnS7z2s/3eePf\ndQA337zEwYMX+r4u5UO9xSepVBh98tU88sZf94fXPnDzMLi6+b3bTv9OSoOi1+/Lxn+n08lJHpu5\n1X8nhsTU1M6mrQUtVTCFEC4Hkhhj01dkCKEE/BbwRW2vUG2pVxUsLBR45BF4//tHuf/+AocOLXHq\nVKFnQ57zXlmQrPOyb7faoxdDlAdNsZhy222DU53Uqq1WAg170NHPIezNKqgG4e8fdV9jiw9nzjA2\nfx9L/uDac9XidZ73AeX3TlI3rdeK6985w2/DGUwhhG8LIXyQ2vDuSgihHEK4Yc0xl4cQfh14L/D0\n7i1VdfX2muc85wJXXw07dtTCj1OnCj0b8vzLv7wjl8O9oRYKLS4WuOKKx6+Et1V79lRZXk45f34w\nZgn1SqdnVg3qnJ1OzT7T+prNmMrDbKhhVSifZHz2zQMx8He5tJd0crL2ya5dtvgMkUF6HUqS1tf4\n77StuNtH04AphPAdwO8Bnwf8CXAMuAL4XyGEb109pgR8ELgZuB94QbcXrJpiMeXVrz7P7t1VPvUp\n+OQnUyD7Zn+zjX59Q/3+94/yyCP0JXjZbI2dGqBcLiccOTJOmiYkScqhQ0tWSXTBIIc0Bh3NdSo0\nXC/MHLQh8r2UZWM+CFeVanx+1eJ1PDZzK5++eRruusuy+yExCK9DSdLmGv+dtj1u+9ioRe4HgE8A\nXxVjfBAghHAZ8A7gJ0IIDwF/vPoYPwf8dIxxscvr3bZOnChw7NgYBw4ss29fbWNVLKbccMN5Xve6\ny6hWE97whgn27Dl38f52tdJ2Ut9Q79gBV19dm//06lef71nwcuJEgcOHJ0jTpOkaOzVAufG5QsKp\nUwVga+dWzQ1yG2K/h2DnVbdb2AZtiHyvXDLrYG627R/m8l7K3uz5LRWvg6md4LyXoZD316EkqXW2\n4m4/G7XIfSFwaz1cAogxngN+klor3BzwMaAUY3y94VL3vPOdcOONl3H8+Bg33ngZJ048/m173/tG\nSdPavKHl5YRjx8a2/HVaqcZorBzYvbva03CpXE44fHiCj3+8wMJCwsJC84qRTrRwbYcqiTy0pg3y\neW7WwrXd9aKyq9NtmsMg62XH817K7mXVt4e8vw4lbc42V2n72qiC6Urgo+vcXr/tDPDVMcZKx1el\nS7zlLbXwCB4PkfbtWwLgwIFl7r13lOXlhLGxlAMHlrf8dVqpxuhn5cD8/MhquxpUq5AkaVfDiGGv\nksjLoORBP8/9HIKdV1Z2dV4rV3vKetnxeil7Xq8I5mXVt4e8vw4lbSxrNa2kwbZRwJSwfj9QPcH4\nBcOl3viO74Bjx9J1Q6R9+6rcfvu5J7TPbUWrG/1+bajrm1YokCQpt9zS/UCk0881T1ccy1NrWqvn\nOU/nr9sG+bkOemiYN63+sN6JjXmeS9kNHraPPL8OJW3MNldpe9soYNrMxzq2Cm3ohS9kwxBp377q\nxYqmrBo3+nnb4Lazac3b2iE/FUN1g1Zlkrfz100bPdc8vrbXY2VX57Tzw/qwb8yH/flJ21UrVZoa\nDFabSttbloBJPRIjnDpVWHfeUbc2m1vdzHd789vKpjWvQUSeKoZg8KpM8nb+uqnZc83ra1vd5Q/r\nw6/Xm2s388oTW6qGi9Wm0va2WcD08hDCvjW3jQMp8MMhhJesuS+NMX5fx1YnyuWE17wGHn54fN1K\nhm5tNreymc/L5jevQUQeK4YGqcokj+evW5o9136/tgelemrY+MP6cOv15trNvPLGlqrhY7WptH1t\nFjA9d/XXer5hndtSwICpg+bnRzhzpvb7tRvK48dHefDBAhMTm282290YbmUz3+/Nb11eg4hBqxjK\nm+10/po9136+tvMSIK9nOwRf/rA+vHq9uXYzr7yxSlOShsdGAdPn9WwVaqpUWuGtb4WHH770Eu7l\ncsI994zy6KMJjz0GT3vaStPN5lY2hlvZzNc3vwsLtSHce/ZsfeB4FnkOIgapYiiPttP5W++59vsq\njnkIkNfqZfBlW5G6odebazfzyhurNCVpeDQNmGKMD/RyIVpfsZhy111w991Ll2wojx8f5aGHCuza\nlbKyAtdff6HppmqrG8N2N/PFYsqhQ0scPjxBmiYcOTLOtdf2p8phs7Vvh4oHDad+X8Uxb5WBvQq+\nbCtSt/R6c+1mXnlklaYkDQeHfA+AEOCqqx7fMNWrl86eTUhTKBZX2L+/+YaqlxvDU6cKjI0lQL6q\nHBrludWnnwzdmvPc5LcysFd/v9lWpG7q9ebazbwkSeoGA6YBND8/wuJigd27UxYX169eWrsh7tXG\nMK9VDo3y2OrT7wDD0K259c7N1FS/V9UfeWxR7NXfb91sK7L1Tr3g60ySJHWbAdMAagxxdu+uPqF6\nqVlY0IuNYac2e5sFLlkCmbyFYHkId44fH+WBBwpcdll+Qre8WC+Q3OvIklzpxd9v3WorsvVOveDr\nTJIk9YIB0wDaLMTpdYVOY9hT//pZw6WNApesgUzeWn36XVHV2HL52GO1lst+h255krdAUv3TjbYi\nW+/UC77OJElSLxgwDaiN/se+lxvixrDnzjtrV41bXMxWibNZ4LL2/uPHR9m9O20rLNro/PW6Xa3f\nAUYrLZfbWd4CSQ0Xr+g1/PLQmubrTJIk9UKSpu1tlkII3wS8CPgc4HXAvwPPB+6MMS52fIVbcPr0\n2aHaAU5N7eT06bNtBR+9CklmZ0c5enQcgEcfhSSBK66o3XfzzUscPNh+JU5jaDU5uXEF08TE46HW\nesd2+mt3Sz9nMPXrOQ+y+ntS6oQ8BBCDLq/vyUta0yYn+9qa5utMvZTX96S0nfm+VKdMTe1Mmt3X\ncgVTCGEMeBu1cKkKFIBfBIrArwPfE0L4hhjjmWzL1XrabQvr1cylxuqba665NOxZW4nTaoiyWcVI\n4/0LCwlvf/sOoDPtZf1qV+vn8GQrdKT+8opewytPrWm+ziRJUre10yL3o8D1wCuAPwYeXL39GPAq\n4AjwBuDVnVygajYKPvpZ2bQ2nKivde3jdzogq99fLie8612jHWsv63e7Wr/k8epg2ly/rz4oaWO2\npkmSpO2knYDpJcAdMcbbQwhX12+MMV4AfjWEEID9GDB1RbPgo53g5sSJAocPT5CmSUevVrY2nFgv\nqOjE3KRmX7uT1Tf1xzt+vLvjyQwGPAdZ5eHqg5I21q2rD0qSJOVRO7vozwb+boP7/wn4vmzLUTPN\ngpRWW7rK5YTDhyf4+McLJAlA++1fWQKBxoBsYqLKPfeMZh4GXteN6pt6VdS73jXa8Y27wYDnoBP6\nffVBSa2xNU2SJG0XhTaO/TfgCze4/9nAx7MtRxspFlMOHrz0Cl+l0gqTk7XZRxu1dM3Pj5CmCYUC\npCkkSdpW+1c9EHjTm8a54YbLOHGiQLmcMDs7SrncdMbXJWufmVnk5puXuP76CywuXroxzpP1Nu6D\n9PiDwHOQXavvfUmSJEnqhXYqmN4CvDqEcC/w96u3pQAhhFcC301tDpO6aG0VUastYvUKIihw4ULK\n13/9cltftzZQu8DCQkK1mvCa10xw9dVpW1VInZyb1K32qnI5YWEhYWKi2nRYeVbbdc5TI89Bdg5n\nlyRJkpQnSZq2tikJIYwD9wBfB5wGpoAycPXqr78Fnhdj/PfuLLV1p0+fHaqdVv2SkuVywktfOsEn\nPlHgMz+zyl13tddWVC4nHD8+erE9rZ1L0pfLCTfccNnFFrvP+IyU0VG44ora/TffvMTBg71pt2ts\nr2rnObTzuBMTVa6//gL791/oysbd+UODfQ68zKuUL74npXzxPSnlj+9LdcrU1M6mLUwtVzDFGJdC\nCC8ADgIHgC8ARoAPAP8buD3GeD7jWrWBO+4Y5SMfqbUSfeQjI/zkT45xxRUJBw4ss29fddM/Xyym\n7N6dXmxPW1go8Mu/vINXv/p8S9VHt9yyeHFI+BVX1L7eVqt8Wp2btF4I0a3ZM42Pu7hYYPfutGvB\nh1dt8xxIkiRJ0jBp61JZMcYq8ObVX08QQrg2xvhA9mVprVrQcum360//dAdjY3DvvaPcfvu5DUOm\nelCzZ0+VyckqCwsFHnkE3v/+UaanCy1VAe3bV2Vu7tzFwAfoagVKs0HQ3Wqvsm1LkiRJkqStaTlg\nCiG8Mcb4mib3jQCvBX4MuLxDa9OqGGF6eoJHHnniTPZqFZaXE44dG2PfvqV1K37WtpQdOrTEsWNj\nvP/9o+zY8cQqoI1al9ZWnXSzAqVZpVK3Zs8402ZrBrnVTZIkSZLUGe1cRe6HQgi/sfbGEMJXAf8f\n8HPA/Z1amB73nvfUApbLL4crrkjZsQN27UpJktoV4cbGUg4cWL4YJB09Os709MTFq7utDWpOnSrw\n6lefZ/fuWsXTxESVhYWEcjlp+hj9UL9K1vnzsLycsmfP4xVa611RrxO69bjDKk+vF0mSJElS/7QT\nMB0Gbgoh3BlCSEIIV4YQbgX+Cvg84IeBL+vGIre75z6Xi5cjf+pTqxSLK1x1FXzBF6ywb9/yxfa4\nZpd+X+9y5vVqnRe/uDY2a25uBzfccBl33DGam8vHF4sphw4tkSQpaZpw5Mj40AQY5XLC7OzowD+f\nZq+5fhiWcypJkiRJg6idId+/EEJ4BLgV+Czgi4FrgN8HDsUYT3VniQoBZmYWOX689u3avbvK+943\n+oTh3s1mCDVr/aoP/X7ssQIPPZSQpgn33jvGVVdVtzy8u9NOnSowNlYLDDo50Lufms2WGkR5mVs1\nTOdUkgZRoXySsfn7WC7tpVq8rt/LkSRJfdDukO/fDiF8EnjL6p99UYzx3q6sTE/wrneNXhzOffXV\ncP/9Ba69dvGSwKjZDKFmV+wqlVaYmalVCBUKMDqacP31y+zeneZipk47AcagzALq1lXw+iEvc6uG\n6ZxK0qAplE9yxfRNJJUK6dwsj83casgkSdI21DRgCiE8t8ldjwC/QG2g96tCCGeBiz0pMcb3dHSF\nAh7fQJ87Vxvq/alPpSwuFjh+fJTXvnb54nHtXvq9WEy55ZZFDh+eIE0Tdu+usn9/fmYQtRpgrK1g\nOXRoiVOnCrkMm/JS9dMp7b7mumHYzqkkDZKx+ftIKhUAkkqFsfn7WDJgkiRp20nSdP3NdwihCmy0\nM6+HSmnD52mMsX9DWFadPn02X4lCRlNTO7nvvk/x0pdO8LGPFahWoX76i8UVbrsteztQp6p/+lVF\nNDs7ytGj4wCcPw9JkjI2ljA5mc92qUGpthokvTynU1M7OX36bFe/hqTW+Z7sr0sqmCYnrWCS70kp\nh3xfqlOmpnY2HXq7UYvc93RhLdqiBx5I+Ld/K7C8XPtePulJKZOTsLjYmXagehVKfVDynj3VtiuA\n+jkHp7GCpT4UHPLbLpWHqp9h4zmVpP6oFq/jsZlbncEkSdI21zRgijHe1cuFaGPHjo1x4UJtTlKa\nwuhoCiQsL6fs2VPd9M+3oh4QNc55aico6uccnMZWuj17qhw5Mm67lJRDVu9Jw6lavM62OEmStrmN\nZjB9DnA6xniu4fNNxRgf7NDa1ODAgWXuvXeU5eWEHTtSXvay8/ze7+0gTROOHBm/ZNj3VtUDosXF\n2pync+fStoKifs/Baaxgufba/g+elnQpr/YnSZIkDa+NWuTuB76L2hXjAP4vG89kquv7DKZhtG9f\nldtvP8exY2McOLDMqVMFxsY62wZWD4jOny8wNpZy2WW0HBTVqxK6MVx7KxUPtktJ+ePV/iRJkqTh\ntVHA9FPAPzV8/pNdXos2sW9flX37liiXE/7hHxImJqosLnauWmhtm1mrQVFjVUKnh2pb8SANj35X\nOUqSJEnqno1mMK0NlOaB98UYH+vukrSRxsBlYqLKi198nv37L6wbuqxX+dOsGqjx9oMH6xUFzWc7\nNR7fzaoEKx6k4dEYYtu+KkmSJA2XjSqY1poDbgP+R5fWohY0Bi6LiwV2706bhktrK3+Ai0O8jxxJ\n+cZvXOZ7v/fCJbfPzKTccssi+/ZtHC41PvahQ0tMTnanKsGKB2m42L4qSZIkDad2AqYEONWthag1\nmwUuJ04UOHZsjCuvrD6h8gdgYaHAQw8lLC8n3HnnOO973yjXX3/h4u1pmnD48ARzc+eaVhesrSo6\ndarQtaoEKx4kSZIkScq/dgKm1wOvDyF8HHgvsBBjdLffYxsFLidOFLjxxstYWkooFFKe8pQqY2OX\nBlEzMykrKwlpCoUCPPRQLShKkpQ0TSgUIE2TDVvR1gu5NqtKaGVQd7NjrHiQJEmSJCnf2gmYpoFd\nwO/XbwghrD0mjTG285jagmaBy7FjYywtJayswMpKwqOPFnjFKy6d0XTLLYscOjTBww8XSBK45poq\n+/df4Mu+bIXDhydI04TduzduRWu3qqiVQd0O85YkSZIkaXC1Ewa9H3hftxai7A4cWOb48VFWVhKS\nBJ70JJ4wo2nfvipve9s5jh+vfevr4VOxmDI3d64rrWitDOp2mLckSZIkSYOr5YApxvjdmx0TQtiR\naTXasnI54dSpAgcPnmdubgef8RmwZ8/6lUjFYsprX7u87u2thDrtVhu1MqjbYd6SJEmSJA2ulgOm\nEMJHgVfFGP+oyf03ADPAVIfWphbVA5+FhQKPPAI7d8L4eMqhQ0ttVyK1Miup3WqjVlrqHOYtSZIk\nSdLgahowhRCeDPyHhps+F3h2COHRdQ4vAP8VuKyjq1NL6oHPuXOwvJxQraaMjdUqmqAKXBoc1f/M\ner9vpTJpK9VGrVRHOcxbkiRJkqTBtFEF0yLwFuApq5+nwOtWf60noWEAuHqnHvicP1/g059OmZjg\nkuCnsaXtzjtrgdPi4qW/n5ur8vznX9i0MqkeVB06tMSpUwWrjSRJkiRJUvOAKcb4qRDCNwNfTC08\nugO4DZhf5/AV4DTwrna+eAjhOcDPxxi/NoTwNODN1IKsDwLfH+P/a+/uo+u66zvfv/fWo5M4lp1a\nyTUtnkvn3B+9oRAKnUYBzJN4SIBrRkwvDVPCJDdAU1rRYNoxHQoN09B0FoGF2sE0SZ3EFAxNYvA0\nNikRLYQHBS5tcxcJ8KsCqVMwRHZixUriIx2ds+8f+0iWZcmxfWydfez3ay0tnbP3Pls/xWuveH38\n/b7IC8wAACAASURBVH5/sRZCeDvwTmAa+JMY453H8jNa3VVXdbB9Oyxbtoxf/uVp0jTlzDPhySfz\nHeBWrkxYtSrj0kunuPfedp71rCo/+lEbAwOV2eBnbkvb2FhKlsGKFYe+njnf07N4ZdLcoKqnp3k7\nvR1NG58kSZIkSVo6R5zBFGP8J+CfAEIIa4E7Yoz3n4gfHEL4A+CtwJP1Qx8F3h9j/EoI4ZPA+hDC\nCDAIvBDoBr4eQrg7xjh5ItZQdFdd1cEdd3QBMDHRxje/2bbgde3t0NaW0dMDO3e2c8458NBDKWvX\n5gHQ3Ja23t6DVUtzX/f01Fi/fpr166cXDW+KsNPbsQ4YP9p7GlhJkiRJknT8jmUXuWvmHwshrAQO\nxBjLx/GzfwgMAJ+qv38B8NX66y8CryavjPpGPVCaDCE8CDwX+H+PdOOVK8+gvX3hMKaV3HXX3HfJ\notdVq1CrJTz1FFQqMDUFExMpDzxwFhddBKtXw6c/DffcA+vW5QHRQq9DOAuAiy46eO8YZ87D618P\nt90G+/bBypXw+tcvY/USj3T/whdgYgLa2g79HY9XjPCe9+S/0223wa23Qggnbr06da1evbzZS5A0\nh8+kVCw+k1Lx+FzqZDtiwBRC6ACuAC6MMV4+5/hLgE8CzwayEMLdwO/EGH94tD84xnhHCOHfzTmU\nxBhnykcmgBXA2cDcoeIzx49o376njnYZhfba185UMCXknYMLa2vLK5jOOCMPmzo7YfnyGuefX2bP\nnvxzq1bBG9948DMLvd6z5+Cx0dGE7dvb2bGjnXI55S//ssaGDZO89KV5cLd+/TSrVmWHfGYpnH9+\nwvLlB9v05v6Ox+POO9vZuzevEtu7F+68c5JVqxw0riNbvXo5e/ZMNHsZkup8JqVi8ZmUisfnUifK\nkYLKI+0i1wkMAy8GpkIIb48xTocQSsDfAV3AXcADwH8CvhlCeG6M8ZHjXGdtzuvlwDiwv/56/vHT\nwqZNFQC2b+9m2bLqEWcwXXBBlfvua+PRR+Gcc/IA6GjavRZqD5tpQ9u1K2ViIqG3N2NsLGXjxm46\nOpLZdrpmKJUyhobKJ6yl7Xh2xJMkSZIkSYc6UgXTu4EXAX8AfCLGOJMo/DH5PKTPxBh/EyCEcB3w\nXfId5t59nGv55xDCy2KMXwEuBv4B+DZwbQihmzzQ+iXyAeCnjU2bKtx+ezd79hw44nXDwymf/WwH\nWZbQ2/v0AdD8CqW584y2b2/n4YdT2togy6BchmXLMrIsb9Nr1vylGaVSdsJ+9okOrCRJkiRJOh0d\nKWB6M3B7jPEjMwfqVU3/F3m/1uzxGONjIYRbgN/g+AOmDcCN9Z/x/frProYQhoCvASnw345z3tMp\nbXQ0YePGbn7605Q0BTg0AJpfpbRQhdJMaARVduxo5/HHE5IEnvnMKgMD01xwQZXrr+86JSt9TmRg\nJUmSJEnS6ehIAVMJuGXesT7gTGB3jPG+eeceBNYcyw+PMf4rcGH99b8AL13gmhuBG4/lvqebkZE2\nsiwPhGo1SJJsNgBaaNe1md3gli2D/fvzCqXe3jw0Ghlpo1xOOffcjAMHYGBgmve+N2/VW7vWSh9J\nkiRJknS4IwVMbcD8so5X1r8PL3D9CuDUmK7dAoaHU7Zt62BgoDI7RwhSkiTjuuvKswHQTJgEB1vb\n5s4dKpWqvO5103NmNh08N7/V7kRV+iw090mSJEmSJLWuIwVMDwIXzDv2H8nb4+5c4PrX1D+jk2x4\nOOXKK5dRqSTs3NnOTTcdWHSO0EJDrI80d+hkzySaW1F18821eeGWJEmSJElqRUcKmD4LfCCE8EXg\nbuCdwPnAI8D/mnthCOE/A68G/ugkrVNz3HJLB+Vy3hJXqSRs29bBJz4xeUh10dwqoYUCoyNVI53M\nmUQzFVVTU/DjH7exeXPKl7/cPjtgXJIkSZIktZ70COc+CnwL+DwwAVwPTAFXxBinAEII/zGE8GVg\nCxCBj53c5Wp0NOFHP0rJMqhWIU0zLrxwmi1b2hkdTWavGRzsZtOmrvow76TJqz6or69KT0+Ncjnf\noW7ZMuYMGJckSZIkSa1o0QqmGONUCKGffDe5PvKQ6a9jjN+bc9kLgBcBfw28J8Z44GQuVjMDvVPO\nOy9jYiLhoosqDA11kWXJYUO8AcbGUjZu7Kaj4+D5ZlQKza+o2r69nR072imXT71d6SRJkiRJOt0c\nqUWOGGMV+Ez9ayEfBj4QY6yd6IXpcKOjCWNjCd3d+UDvVauqfPe77ezdm5KmAIcP8U6SjCzLK5hm\nKoV27arNDgjv7z/5f3QL7WT33vdWWL9+2mHfkiRJkiSdAo4YMD2dGKO7xi2RuSFNd3eNN71pCoCt\nWztJEqjVIEmyw4Z4r1lT4/rru2aHfEN22IDwYw2ZjnUXuIV2siuVpk/qrCd3qpMkSZIkaek0FDBp\n6cwNacrllN7ePEz68pfbgbxS6brrygsO8V679uCQ7499rJNKJa9omhkQ3t8/edTrGB7OW+7mtuQ9\nXYCz0E52J9NCFVOGTJIkSZIknTwGTC1ioZBmbqXSkSp15oZNAwMVdu5sp1JJ6OjIGBioHPUaRkcT\nNm7s5qc/TUkSmGnJm1+FNL966GjXeaIsVjElSZIkSZJODgOmFnGiQpr+/ho33XTgiDOYFmsvyweM\nJ6TpoS158z+7UPXQyWyHm2+pK6YkSZIkSTrdGTC1kPkhzfG2gvX31xZtizvSPWeCm4Va8mYUoXpo\nqSumJEmSJEk63RkwtaDh4ZRt2zpYsaI2G+aMjaV87GOdXH31VEOBypECoqMJbopSPbSUFVOSJEmS\nJJ3uDJhazPBwOrsLXHt7xjOeUWNyMuXRR+Fb32pncDBtaKj10wVETxfcWD0kSZIkSdLpx4CpRczM\nRbr77rbZXeCmpxN+8RdrLF9e41vfaqezs/G2tBMREFk9JEmSJEnS6cWAqQXEyOxcpCSp0d6eMT2d\n7wL3trdVWLs2Y3AwPWFtaQZEkiRJkiTpWBgwtYB77mF2LlKWpbzlLVM8/nh6yC5wp2pb2mI72kmS\nJEmSpOIwYGoB69bBxz9e45FHUs49t8YVV0wfFrYcbdVRKwU2x7tLniRJkiRJWlppsxego5fko5fY\ntSthy5Z2RkeTY/r8TGCzaVMXg4Pdx/z5pbbQjnaSJEmSJKl4DJhawD33QLmccvbZsH9/ysaNRxcS\njY4eGkS1WmDT11elpydvATwRs6UkSZIkSdLJYcDUAtatg+7uGvv3w/R0RpblgdGRQqKFqpVaLbCZ\n2dHuqqsmbY+TJEmSJKnAnMHUQrIMzjwzo6sro1w+8o5xC1UrXXbZdMsNA3dHO0mSJEmSis+AqQXM\ntMitWAGTkym/+IvTPO9506xff/iw7xl9fVW2bq0xPn5oEGVgI0mSJEmSTjQDphaQZfDYY9DeDk8+\nCd/7Xjt799a44ILqgtVIMzvFbdgwye7dactUK0mSJEmSpNZkwFRww8Mp73kPlMsJWZZxzjnQ2Qlj\nY/mw746OhK1ba7MzimZmL81ULjm7SJIkSZIknWwO+S64bds6mJqCNIUkSZiayquZ9u+HycnDh323\n2k5xkiRJkiSp9RkwFdzAQIXOzvx1Z2fGsmU1Hn884cknE/bvh6mpgzvCDQ+nfOlLbSRJ6+wUJ0mS\nJEmSWp8tcgXX31/jtttg8+YKK1bU2L69k7Z6UVJ3N/zar01z9dVT7NqVcOWVy6hUEtI04w1vqPCs\nZ9kaJ0mSJEmSTj4rmFrAxRfD1VdPkSQJkJEkecvcmjU1rr56ilIp45ZbOiiXE2o1qFYTvvzldu64\no5PBwW5GR5PD7jk6mrBlS/uC5yRJkiRJko6FFUwtIEZ4xzu6efDBNrIMzj67xhvfOM3ll1dmB3v/\n6EcpWQa1Wr7bXGfnofOZSqXp2fvNHQQ+f0D4QrvSSZIkSZIkHYkBUwu4555817haLQ+QDhxIWLUq\nmw2BRkbayLKU887LmJhIeO1rp3joobbZneTmz2FaeBB4dcHQSZIkSZIk6enYItcC1q2D3t58cHe1\nCpVKwo4dB9vb+vqq9PTUOOMMCKHK1VdXGBoqc9VVkwsGRTPXw8FB4O4+J0mSJEmSjpcVTC0gBLjh\nhjIf+lAn3/52O2edBeXywda3UiljaKh8WHvb3La4uRa+vsrWrbVFq54kSZIkSZIWY8DUIkqljA98\nYIrBwXTBEKhUyhYMlBabqzT/+sVCKkmSJEmSpKdjwNRCZkKg7dvbefRR2L69nfXrpxcNgxYb5n2k\n+y9W9SRJkiRJkrQYA6YWlM9faiNJ8tc33LBwcLTQXCUDJEmSJEmSdKIZMLWAL34RNm/uYmCgwu7d\nKY88kpJlkGX57nLzg6OZtrg1a2r09DhXSZIkSZIknVwGTAU3PJzy9rfD1FQHO3e28zu/M0l7e0aS\nJCRJvrvc3OBobltcT0+NDRsm2b07da6SJEmSJEk6aQyYCm7btg6mpvLXk5MJf/7nnZx9dsKqVTVe\n//ppLr+8ckhwNL8tbvfulMsusy1OkiRJkiSdPGmzF6AjGxio0NmZt8NVq1Aup+zbl9DRkfDsZ9cO\nq0rq66vS01MDmG2LGx1N2LKlndHRpBm/giRJkiRJOsVZwVRw/f01brsNfvd3qzz8cEqtlpBlkCTZ\ngjOVZnaaGxlpmz1/LDvJSZIkSZIkHSsDphaxe3fK9HQeLq1cWeO66w4NimYGe8/MWpoZ+r1lS7s7\nyUmSJEmSpJPKgKkFfOYzUKnkQ72TBJ797Bq7d6eMjuYB0/bt7ezY0U65fGiV0uhowthYQnd3jXLZ\nneQkSZIkSdLJYcDUAl7yEvj0p6m3xsHDDyds2tTFzTfns5YeeSRlYiKhtzebrVKC6mxrXHd3jTe9\naYr166dtj5MkSZIkSSecAVMLSBLo7c148klIU3jqqZQVK2BsLCXLYNky2L8fymXo7c2rlObuJlcu\np/T2ZoZLkiRJkiTppDBgagHr1sHP/3xtthoJZkKjg69LpSqve930nCqlKlu35p+xNU6SJEmSJJ1M\nBkwt4oILpvnxj1Pe9rYKa9dmh+wSN3e494z5u8lZvSRJkiRJkk4WA6aCGx1N+O3fhh/8oIssgx//\nOOWGG8pcdtn07Pn512/fnv+xrl8/PXudJEmSJEnSyWLAVHAjI2387GdQy7vheOSRdDZAevRRuPfe\ng7vHbdgwybXXdvHgg21kGezY0c4NN5StXpIkSZIkSSeVAVPB9fVVOe88GB/Pd5Hr6amxbVs7//Zv\nbVQq0NYGZ5yR8dhjKbfe2sHYWHpIGDUy0kapZBWTJEmSJEk6eQyYCq5Uyvjc52Dz5kkAHnss4bOf\n7aRan9ldqcDjj+dtct//fsqKFTX2788rmM491+HekiRJkiTp5DNgagEhQG9vjc2bO3nsMZiagmoV\n0jQ/n6aQJHDgQMpv/MYUkFcsHdxRTpIkSZIk6eQxYGoBN9wAv//7y8jqWVFXV8ZZZ8FznzvNI4+k\nPPxwG0mSh1CGSpIkSZIkaakZMLWAzZuZDZcAJicT2toy3vWuCmvXZofsGme4JEmSJEmSllra7AXo\n6V1xRd4CN6O7O6OrC+67r41SKeO9762wfv0027e385GPdDA6mix+M2B0NGHLlvanvU6SJEmSJOlo\nWMHUAl76UviFX6jyk5+kVKswPZ3w1FOwY0c769fn85be8Y5uHnwwH+69Y0c7N9xQXrCaaXQ0YXCw\nm/HxlK1bawwNLXydJEmSJEnS0bKCqQXccw9kWUpbW0KaJtRq0NOTUS6njIy0MTLSxthYSq2Wt9I9\n8kh+fCEjI22Mj+d/7OPji18nSZIkSZJ0tAyYWsC6dZAkGVkG7e35V7UKPT01+vqq9PVV6e2tze4m\nd+65+fGF9PVV6empAQc/L0mSJEmS1Ahb5FpACDA4OMm113bXA6aMSy6pcMUVB4d633BD+aiGfZdK\nGUNDZUZG2ujrq9oeJ0mSJEmSGmbA1AJihJtv7qRahSeegFWrEu67rx2Ynr1mZtj30SiVMkql6ae/\nUJIkSZIk6SjYItcC/uZv4MEH23jyyWR2wPeuXelsxdJc7hAnSZIkSZKWmgFTC8iy/CtJ8u9PPJEw\nMZGwY8ehQdLMDnGbNnUxONhtyCRJkiRJkpaEAVMLePOboVSq0tOTsXp1jZ6ejN7eg7vIzXCHOEmS\nJEmS1AwGTC0ghHyI98aNk3z842We9awanZ2H7wLnDnGSJEmSJKkZDJgKbng45a1vhV27Evr6quze\nnfLKV1ZYsaLGpZdOHbIL3MwOcVddNcnQUNkd4iRJkiRJ0pJwF7kCGx5OufLKZVQqcMcdy3jGM2pM\nTKSMjSW0tcEPftDNmjUHWLs2Y2Skjb6+qjvESZIkSZKkJWfAVGDbtnVQqeSDuqemEn72s3R24HeW\nQaWScOutHezdmzI+nrJ1a83KJUmSJEmStORskSuwgYEKHR15WNTZmXHeeTXOOivfTS5JoKMj4+d/\nvuZgb0mSJEmS1FRWMBVYf3+Nm246wM6dZ3LJJQdb4SDj3nvbGRiosHZtxn33tTM+njrYW5IkSZIk\nNUWSZadeO9WePROn1C+1evVyvvnNJw6ZszTX6Giy6DlJJ97q1cvZs2ei2cuQVOczKRWLz6RUPD6X\nOlFWr16eLHbOCqYWECMMDnYvOmfJwd6SJEmSJKmZnMHUAu65B+csSZIkSZKkwjJgagHr1kFPTw3A\nOUuSJEmSJKlwbJFrAT/6EZxzTo3nP7/K5ZdXnLMkSZIkSZIKxYCp4IaHU97+dpia6qCjI+MVr5g+\nJGBywLckSZIkSWo2W+QKbtu2DqamIMtgcjLh1ls7Zs+NjiYMDnazaVMXg4PdjI4uOsxdkiRJkiTp\npDFgKriBgQodHVCt5l8//GE6GySNjLQ5/FuSJEmSJDWdAVPB9ffXuOIKOOOMjHPPzciyg0FSX1/V\n4d+SJEmSJKnpnMHUAlavhloto1xO6OrKWLMmD5VKpYyhobIzmCRJkiRJUlMZMBXcli1tXHMN1Gop\nBw5AmsL113exdm2ZUimrf003e5mSJEmSJOk0ZotcwX3mM53UagffP/FE4rwlSZIkSZJUKAZMBfeW\nt0wd8r5SgSRx3pIkSZIkSSoOA6aCu+yyKqXSoceWL685b0mSJEmSJBWGAVMLWLny0PdJkjRnIZIk\nSZIkSQswYGoB55578HWSwK/8Sm3xiyVJkiRJkpaYAVPBDQ+n3H33wffLl2e84hXuGidJkiRJkorD\ngKngtm3roFqFtP4nVanA9dd3MTpqm5wkSZIkSSoGA6aCGxio0NmZt8YBdHTA2FjKyEhbcxcmSZIk\nSZJUZ8BUcP39NW67DS68sEJXV8aBAwmPPgpr1jiHSZIkSZIkFUN7sxegp3fxxfC971V5+OF2DhzI\nWLYMdu9OAUMmSZIkSZLUfFYwtYi+viq9vTVWrIDe3hpr1tTYsqXdWUySJEmSJKnprGBqEaVSxoYN\nk2zb1sGFF05z/fVdjI+nbN1aY2ioTKmUNXuJkiRJkiTpNGXA1CKGh1M2buwmyxK+/e02siyhsxPG\nx/OB36XSdLOXKEmSJEmSTlMGTC0gRti4sZuf/jQlSWDlSujqyoCEnp4afX3VZi9RkiRJkiSdxgyY\nWsA990CWJaQp1Gp5uHTddWV2707p66vaHidJkiRJkprKgKkFrFuXD/aenk4pl2FwcJL+/hruIidJ\nkiRJkorAXeRaQAiwYcMkHR0ZZ50FW7d2unucJEmSJEkqDAOmFrF7d0pHx6GDvSVJkiRJkoqgcC1y\nIYR/AvbX3z4EXAvcAmTA/cC7YoynXW9YX1+VrVtrjI+nDvaWJEmSJEmFUqgKphBCN5DEGF9W/7oc\n+Cjw/hjjS4AEWN/URS6xq67qoL0dXv7yZXR3V3nTm6YYGio72FuSJEmSJBVG0SqYngecEUL4Evna\n/hB4AfDV+vkvAq8GPt+c5S2tq67q4I47ugCoVlO+8Y1OfvjDGuvXTzd5ZZIkSZIkSQcVLWB6CvgI\ncBNQIg+UkhjjTLnOBLDi6W6ycuUZtLe3/oyiu+6a+y4f6r13bxsPPHAWF13UlCVJqlu9enmzlyBp\nDp9JqVh8JqXi8bnUyVa0gOlfgAfrgdK/hBAeJa9gmrEcGH+6m+zb99RJWt7Seu1rZyqYEvIRVPBz\nP1fj/PMPsGePLXJSs6xevZw9eyaavQxJdT6TUrH4TErF43OpE+VIQWWhZjABVwDXA4QQ1gBnA18K\nIbysfv5i4GvNWdrS27SpwpveNElbG3R21nj1q6e4444Dzl+SJEmSJEmFUrQKpr8CbgkhfJ28ZOcK\nYC9wYwihE/g+cHsT17fkNm2qcPvt3ezZc2pUZUmSJEmSpFNPoQKmGOMU8JYFTr10qdciSZIkSZKk\no1O0FjlJkiRJkiS1mEJVMGlhMcKdd7bT11cFYGSkjb6+qrOYJEmSJElSIRgwFdzoaMJ73gN793Zx\n8801AMrllK1bawwNlQ2ZJEmSJElS09kiV3AjI23s25e/fuSRlLGx/I9sfDxlZKStiSuTJEmSJEnK\nGTAVXF9flZUr89fnnlujtzevYurpqc22zEmSJEmSJDWTLXIFVyplfPCDsHnzNAMDFdauzZzBJEmS\nJEmSCsWAqeBGRxOuuQb27m3noYdShobKXHbZdLOXJUmSJEmSNMsWuYKbO4PJuUuSJEmSJKmIDJgK\nrq+vSpbBz34GSeLcJUmSJEmSVDwGTAW3a1fCT34C5XLCT36SsmtX0uwlSZIkSZIkHcKAqeC2beug\nUoE0henphG3bOpq9JEmSJEmSpEMYMBXcwECFzs78dUdHxsBApbkLkiRJkiRJmsdd5Aquv7/GbbfB\n5s0VBgYq9PfXmr0kSZIkSZKkQxgwtYCLL4YXvnCy2cuQJEmSJElakC1yBTc6mnDjjfl3SZIkSZKk\nIrKCqcBGRxMGB7uZmIDly7sZGipTKmXNXpYkSZIkSdIhrGAqsJGRNsbH8z+i8fGUkZG2Jq9IkiRJ\nkiTpcAZMBdbXV6Wnp8bkJDz5ZMYPfpDaKidJkiRJkgrHgKnASqWMDRsmmZ6Gxx5L+dSnOnnHO7oN\nmSRJkiRJUqEYMBXc7t0pBw5AtZp/jY3ZKidJkiRJkorFgKng1qyp8dRTBwOmFStq9PVVGR1N2LKl\n3WomSZIkSZLUdO4iV3C7d6ecdx48/ni+e9zAwDQAg4PdjI+nbN1ac3c5SZIkSZLUVFYwFVxfX5Vz\nz4VVq+BZz6qxfv20u8tJkiRJkqRCMWAquFIp44MfhOc/f5oNGyYplbLZ3eUAenryljlJkiRJkqRm\nsUWu4EZHE665Bvbubeehh1Jgkt27UzZsyL/39VVtj5MkSZIkSU1lwFRwIyNt7NuXvx4bS9m4sZuO\njoSeHmcvSZIkSZKkYrBFruD6+qqsXJm/TpKMLMt3jXP2kiRJkiRJKgormAquVMq49Va4885J1qyp\ncf31XYyPp85ekiRJkiRJhWHAVHDDwyk7d8Ill9To76+xdm2ZkZE2Zy9JkiRJkqTCMGAqsOHhlCuv\nXEalAtu2LeOmmw7Q31+jVJpu9tIkSZIkSZJmOYOpwLZt66BSyWcuVSoJ27Z1NHlFkiRJkiRJhzNg\nKrCBgQodHXkbXEdHxsBApckrkiRJkiRJOpwtcgXW31/jppsOsHPnmVxySd4eJ0mSJEmSVDQGTAXX\n31/j0kthzx7DJUmSJEmSVEy2yLWAGGHLlnZGR5NmL0WSJEmSJOkwBkwFNzqa8La3waZNXQwOdhsy\nSZIkSZKkwjFgKriRkTb27ctfj4+njIy0NXdBkiRJkiRJ8xgwFVxfX5WVK/PXPT01+vqqzV2QJEmS\nJEnSPA75bgGvex088cQU69dPUyplzV6OJEmSJEnSIQyYCmx0NGFwsJuJCVi+vJ3166ebvSRJkiRJ\nkqTD2CJXYCMjbYyP539Ezl+SJEmSJElFZcBUYH19VXp6aoDzlyRJkiRJUnHZIldgpVLG0FCZBx44\ni/PPLzt/SZIkSZIkFZIBU8GVShkXXQR79hguSZIkSZKkYrJFTpIkSZIkSQ0xYJIkSZIkSVJDDJgK\nbnQ04cYb8++SJEmSJElF5AymAhsdTRgc7GZiApYv72ZoyEHfkiRJkiSpeKxgKrCRkTbGx/M/ovHx\nlJGRtiavSJIkSZIk6XAGTAXW11elp6cGQE9Pjb6+apNXJEmSJEmSdDhb5AqsVMoYGirzwANncf75\ntsdJkiRJkqRiMmAquFIp46KLYM8ewyVJkiRJklRMtshJkiRJkiSpIQZMkiRJkiRJaogBkyRJkiRJ\nkhpiwCRJkiRJkqSGGDAV3PBwylvfmn+XJEmSJEkqIlOLAhseTrnyymV87nNw5ZXLDJkkSZIkSVIh\nmVgU2LZtHVQqCQCVSsK2bR1NXpEkSZIkSdLhDJgKbGCgQkdHBkBHR8bAQKXJK5IkSZIkSTpce7MX\noMX199e46aYD7Nx5JpdccoD+/lqzlyRJkiRJknQYA6aC6++vcemlsGeP4ZIkSZIkSSomW+QkSZIk\nSZLUEAOmghsdTbjxxvy7JEmSJElSEdkiV2CjowmDg91MTMDy5d0MDZUplbJmL0uSJEmSJOkQVjAV\n2MhIG+Pj+R/R+HjKyEhbk1ckSZIkSZJ0OAOmAuvrq9LTkw/37ump0ddXbfKKJEmSJEmSDmeLXIGV\nShlDQ2UeeOAszj/f9jhJkiRJklRMBkwFVyplXHQR7NljuCRJkiRJkorJFjlJkiRJkiQ1xIBJkiRJ\nkiRJDTFgkiRJkiRJUkMMmCRJkiRJktQQAyZJkiRJkiQ1xIBJkiRJkiRJDTFgkiRJkiRJUkMMmCRJ\nkiRJktQQAyZJkiRJkiQ1xIBJkiRJkiRJDTFgkiRJkiRJUkMMmCRJkiRJktQQAyZJkiRJkiQ1xIBJ\nkiRJkiRJDTFgkiRJkiRJUkMMmCRJkiRJktQQAyZJkiRJkiQ1xIBJkiRJkiRJDTFgkiRJkiRJxGR5\nyQAADBlJREFUUkMMmCRJkiRJktQQAyZJkiRJkiQ1xIBJkiRJkiRJDTFgkiRJkiRJUkMMmCRJkiRJ\nktQQAyZJkiRJkiQ1xIBJkiRJkiRJDTFgkiRJkiRJUkMMmCRJkiRJktQQAyZJkiRJkiQ1pL3ZCzga\nIYQU+ATwPGASuDLG+GBzVyVJkiRJkiRonQqmNwLdMcY+YCNwfZPXs2Se85wOkiT/LkmSJEmSVESt\nEjC9GLgLIMZ4L/DC5i5naTznOR2MjXUBMDbWZcgkSZIkSZIKqSVa5ICzgcfnvK+GENpjjNMLXbxy\n5Rm0t7ctzcpOorGxue8Sxsa6Wb26u1nLkTTH6tXLm70ESXP4TErF4jMpFY/PpU62VgmY9gNzn4Z0\nsXAJYN++p07+ipZAb+9MBVMCZPT2TrJnT6XZy5JOe6tXL2fPnolmL0NSnc+kVCw+k1Lx+FzqRDlS\nUNkqLXLfAC4BCCFcCHy3uctZGvffX6G3dxKA3t5J7r/fcEmSJEmSJBVPq1QwfR54VQjhm+TlPJc3\neT1L5v77K6xe3W3lkiRJkiRJKqyWCJhijDXgt5q9DkmSJEmSJB2uVVrkJEmSJEmSVFAGTJIkSZIk\nSWqIAZMkSZIkSZIaYsAkSZIkSZKkhhgwSZIkSZIkqSEGTJIkSZIkSWqIAZMkSZIkSZIaYsAkSZIk\nSZKkhhgwSZIkSZIkqSEGTJIkSZIkSWqIAZMkSZIkSZIaYsAkSZIkSZKkhhgwSZIkSZIkqSEGTJIk\nSZIkSWqIAZMkSZIkSZIaYsAkSZIkSZKkhhgwSZIkSZIkqSEGTJIkSZIkSWqIAZMkSZIkSZIaYsAk\nSZIkSZKkhiRZljV7DZIkSZIkSWphVjBJkiRJkiSpIQZMkiRJkiRJaogBkyRJkiRJkhpiwCRJkiRJ\nkqSGGDBJkiRJkiSpIQZMkiRJkiRJaogBkyRJkiRJkhrS3uwFaHEhhBT4BPA8YBK4Msb4YHNXJZ3a\nQgi/BvxZjPFlIYR/D9wCZMD9wLtijLUQwtuBdwLTwJ/EGO8MISwD/hroBSaAt8UY9zTll5BOASGE\nDmAz8O+ALuBPgO/hMyk1TQihDbgRCOTP4W8BZXwupaYJIfQC/wi8ivx5uwWfRzWJFUzF9kagO8bY\nB2wErm/yeqRTWgjhD4CbgO76oY8C748xvgRIgPUhhPOAQeBFwGuAPw0hdAFXAd+tX7sFeP9Sr186\nxfwm8Gj9mXot8Bf4TErN9gaAGOOLyJ+pa/G5lJqm/o8xfwkcqB/yeVRTGTAV24uBuwBijPcCL2zu\ncqRT3g+BgTnvXwB8tf76i0A/8B+Ab8QYJ2OMjwMPAs9lzvM651pJx+824I/qrxPyf3X1mZSaKMb4\nBeAd9bdrgXF8LqVm+gjwSWB3/b3Po5rKgKnYzgYen/O+GkKwrVE6SWKMdwCVOYeSGGNWfz0BrODw\n53Kh4zPHJB2nGOMTMcaJEMJy4Hbyf1n1mZSaLMY4HUK4Ffhz4NP4XEpNEUL4L8CeGOPfzTns86im\nMmAqtv3A8jnv0xjjdLMWI52GanNeLyf/l9r5z+VCx2eOSWpACOEXgH8APhVj/Aw+k1IhxBjfBvwf\n5POYls055XMpLZ0rgFeFEL4CXEDe5tY757zPo5acAVOxfQO4BCCEcCHw3eYuRzrt/HMI4WX11xcD\nXwO+DbwkhNAdQlgB/BL5EMXZ53XOtZKOUwjhXOBLwH+NMW6uH/aZlJoohPDWEML76m+fIg99v+Nz\nKS29GOO6GONLY4wvA+4DLgO+6POoZrLdqtg+T55Kf5N8/sTlTV6PdLrZANwYQugEvg/cHmOshhCG\nyP8nnAL/LcZYDiFsAm4NIXwdmALe0rRVS6eGPwRWAn8UQpiZxfRuYMhnUmqabcDNIYR7gA7g98if\nRf9fKRWDf3dVUyVZlj39VZIkSZIkSdIibJGTJEmSJElSQwyYJEmSJEmS1BADJkmSJEmSJDXEgEmS\nJEmSJEkNMWCSJEmSJElSQ9qbvQBJkqQTJYTwFeCl8w5XgX3AN4H/GmP8wVKva74QQgq8A7gS+CWg\nBnwPuAm4KcZYyG1+QwgvA/4B+NUY43eavBxJklQgVjBJkqRTzTeAvjlfrwT+O/Bi4EshhO4mrm3G\nh4HrgS8AbwT+b+DrwCbguiauS5Ik6bhYwSRJkk414zHGe+cd+2oI4SngRuAVwM6lX1YuhNAFvBv4\n4xjjn8059cUQQg24OoTwpzHG8easUJIk6dgZMEmSpNPFxNw3IYQ/Bt4bYzxrzrELgH8GXh5j/EoI\n4RbgLOBrwHuAXuBe4LdjjN+vf+Y8YIg8uDoD+Efg/THGry6yjrOBbhauJL8B2DP3XL0t7UPArwDj\nwN8AfxhjLNfPrwP+BLgAOADcBmyMMT5RP/8V4F+AtcA68ha83w0h9AIfAV4PdAJ/D7w7xvjQnJ99\nCXAt8Oz6f5cbF/mdJEnSac6ASZIknWqSEMLcv+N0Ay8kD0oeBu45xvv1A88irzpqAz4O3AL8Wv38\nXwOrgMuBMvBeYEcI4Zkxxsfm3yzGuCeE8B3gj0MIzyRvk/tGjPGJGOMo8D9mrg0h/AfgbuAu4M3A\necBHyYOs3wohXAzcCdwO/Cl5iPRh4JdDCC+PMdbqt7oc+AR5W96+EMIy8llKy4DfBZ4C3gfcE0J4\nboxxXwjhQuB/1e/9PuAi8hY+SZKkwxgwSZKkU80lQGXesQPAMPCemcqeY7AceF2M8acAIYRnAB8P\nIZwTY3yUfLbTNTHGv62fv5+82ulM4LCAqe4/AZ8Gfqv+VQ0h3At8irzCqFq/7n3AQ8AbZ47VZ0j9\nlxBCG3nl0rdjjG+euXEI4SHyQOp1wN/WD08AV88ETiGEdwIBeM7M0PMQwpeBXeSB04eAPyCvfLq0\nPnT8rhBCT/28JEnSIRzyLUmSTjVfB361/nU5ecjzt8CvxxgfPI777ZoJl+p+XP9+Zv3714APhRC2\nhhB+E5iMMf5+jPHfFrthjHFXjPHFwPOB99fv8ULgk8BwfU4T5FVDO+cETsQY/2eM8VfJq4+eT94S\nN/fef0e+a97c3fQenFPNBPByYBR4MITQXq/4eqq+jlfWr3kRcNe8He3uWOx3kiRJpzcrmCRJ0qnm\n8Rjjd+qvvxNCeJi8emkSuOw47vfUvPczQc3MP9S9GfgA+U5wvwFUQgifBd4ZYzxwpBvHGO8D7gOu\nDSGcTb7b3SDwFuBm8ta7sUU+3gMkwCMLnBsjn/U09/1c55DPVZpf6QV58ASwEtg779zPFlmLJEk6\nzVnBJEmSTmkxxr8H/gp4awjhDXNOZRz+d6GzOEYxxsdijL8XY1xDXlH0ceA3yYOiw4QQfi+E8JN6\ni9vc++wHfo+84uqX6ocfB1bP+/yqEMKrgP313+HcBX7MecCjR1j248D/x8FKr7lfb6pf8yj5UPO5\nzjnCPSVJ0mnMgEmSJJ0O3kceqnw0hNBZP7YfWFafKzTjJcdy0xDCz4UQHg4hDEBekRRj/H3yWUbP\nXORjPwDWAP/PAuf+N/KZT/fX338TuDiEMPfvbG8mH+ydkVc//fq8Nb0GWAF84whL/zrwvwP/GmP8\nTr3i6x+Bq8l3lYN8CPgb5g1Mv+QI95QkSacxW+QkSdIpL8a4N4TwYeDPyCuLPgJ8kXxHtr8KIfwF\ncAHw28dx31Hyod9nAv9GPlx7LfD5RT72d+Q7x30ihPAC8rDoceD/JN+B7p+Bz9av/TD5XKTbQwg3\nAL9AvhveX8QYJ0IIHwS2hxA+R95S98z6Z0bqv99iNtf/O9wdQvhT8qqpd5BXL81UeV0LfAf4Qgjh\nfwLPBX7naP/bSJKk04sVTJIk6XTxceBfgfeHEFbXd0+7EvgV8jDmjeS7ux2rS4G/B/4HeXj0GuA/\nxxiHF7q4PjT714EN5KHWp4C76+8/B7wyxjhVv/Ze4NXklU1fIB8IPgRsrJ//2/q6/z2wHbgG2Aq8\nZu5g8AXWsB9YR15N9cn6Z9cC62OMO+vXPAC8Cvg5YFv993zXsf7HkSRJp4cky7Knv0qSJEmSJEla\nhBVMkiRJkiRJaogBkyRJkiRJkhpiwCRJkiRJkqSGGDBJkiRJkiSpIQZMkiRJkiRJaogBkyRJkiRJ\nkhpiwCRJkiRJkqSGGDBJkiRJkiSpIf8/0JoWDOz803MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae90902668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(461)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Class A-B-C\",fontsize=20)\n",
    "plt.ylabel(\"Strike Rate\",fontsize=18)\n",
    "plt.xlabel(\"Runs Scored\",fontsize=16)\n",
    "plt.scatter(ClassA_Runs,ClassA_SR,marker='.',c='green',alpha=0.85) \n",
    "plt.scatter(ClassB_Runs,ClassB_SR,marker='.',c='red',alpha=0.85) \n",
    "plt.scatter(ClassC_Runs,ClassC_SR,marker='.',c='blue',alpha=0.85) \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJkpQawyZJkiRJkiSlxrBJkiRJkiRJqTFskiRJkiRJUmoMmyRJkiRJkpQawyZJkiRJ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\nuppfuq50pWfFhRPG9XSaG6f7AQAAQOvYRldDw8cey2hGkyS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uHntwKeUraYRTr02yu/ny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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae90d6ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(461)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs vs Average\",fontsize=20)\n",
    "plt.ylabel(\"Average\",fontsize=18)\n",
    "plt.xlabel(\"Runs Scored\",fontsize=16)\n",
    "plt.scatter(Player_ball_faced,Players_SR,marker='.',c='green')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the Century, Half Century , Sixes and Fours Scored by Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def milestones(name):\n",
    "    Player_Match = []\n",
    "    four = 0\n",
    "    six = 0\n",
    "    for i in range(len(Player_Runs[0:][1])):\n",
    "        if(Player_Runs[0:][1][i]==name and Player_Runs[0:][3][i]==0):\n",
    "            Player_Match.append([[Player_Runs[0:][0][i]],[Player_Runs[0:][1][i]],[Player_Runs[0:][2][i]],[Player_Runs[0:][6][i]]])\n",
    "    for i in range(len(Player_Match)):\n",
    "        if(Player_Match[i][2][0]==4):\n",
    "            four=four+1\n",
    "        elif(Player_Match[i][2][0]==6):\n",
    "            six = six+1\n",
    "            \n",
    "    Player_four.append(four)\n",
    "    Player_six.append(six)\n",
    "    \n",
    "    Player_Match_Runs = []\n",
    "    Runs = []\n",
    "    match_no = Player_Match[0][0]\n",
    "    sum = 0\n",
    "    for i in range(len(Player_Match)):\n",
    "        if(Player_Match[i][0] == match_no):\n",
    "            sum += Player_Match[i][2][0]\n",
    "        else:\n",
    "            Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "            Runs.append(sum)\n",
    "            sum = Player_Match[i][2][0]\n",
    "            match_no =  Player_Match[i][0];\n",
    "    Player_Match_Runs.append([Player_Match[i-1][0],Player_Match[i-1][1],[sum],Player_Match[i-1][3]])\n",
    "    Runs.append(sum)\n",
    "\n",
    "    fifty = 0\n",
    "    century = 0\n",
    "    for i in range(len(Player_Match_Runs)):\n",
    "        if(Player_Match_Runs[i][2][0]>=50 and Player_Match_Runs[i][2][0]<100):\n",
    "            fifty = fifty+1\n",
    "        elif(Player_Match_Runs[i][2][0]>=100):\n",
    "            century = century+1\n",
    "    Player_century.append(century)\n",
    "    Player_fifty.append(fifty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "461\n"
     ]
    }
   ],
   "source": [
    "Player_fifty = []\n",
    "Player_century = []\n",
    "Player_four = []\n",
    "Player_six = []\n",
    "index = 1\n",
    "for player in player_list:\n",
    "    milestones(player)\n",
    "    print(index)\n",
    "    index = index+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14, 8, 0, 0, 0, 0, 0, 0, 10, 0, 21, 5, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 15, 2, 13, 0, 0, 0, 0, 6, 0, 1, 1, 0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 36, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 17, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 2, 4, 0, 0, 0, 2, 0, 0, 3, 0, 0, 13, 1, 0, 0, 3, 0, 1, 0, 0, 0, 7, 5, 0, 0, 0, 5, 2, 0, 10, 0, 0, 0, 3, 1, 1, 1, 0, 3, 1, 1, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 0, 8, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 22, 14, 0, 0, 1, 8, 0, 0, 0, 0, 1, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 5, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 10, 1, 0, 0, 1, 2, 0, 1, 9, 12, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 1, 0, 6, 22, 0, 0, 0, 4, 0, 0, 0, 0, 1, 6, 14, 17, 0, 0, 0, 30, 0, 0, 0, 0, 4, 1, 0, 17, 0, 1, 0, 0, 11, 0, 1, 1, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 35, 0, 0, 1, 0, 0, 16, 4, 0, 1, 3, 0, 0, 6, 0, 0, 0, 0, 0, 0, 32, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 11, 4, 0, 0, 6, 0, 6, 14, 0, 1, 0, 1, 0, 0, 3, 0, 0, 20, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 1, 0, 0, 4, 0, 0, 2, 0, 0, 3, 0, 0, 11, 28, 0, 0, 0, 2, 0, 11, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 5, 1, 0, 0, 0, 0, 0, 25, 1, 0, 0, 0, 0, 3, 0, 0, 1, 4, 0, 0, 0, 13, 31]\n"
     ]
    }
   ],
   "source": [
    "print(Player_fifty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[78, 44, 0, 1, 0, 0, 0, 1, 27, 0, 265, 41, 1, 0, 0, 5, 0, 1, 38, 1, 2, 28, 0, 1, 6, 0, 52, 8, 29, 3, 0, 0, 2, 79, 0, 3, 13, 3, 0, 39, 1, 6, 16, 1, 0, 0, 0, 0, 13, 0, 3, 0, 3, 0, 160, 1, 0, 3, 8, 0, 0, 1, 7, 0, 0, 0, 117, 0, 23, 0, 0, 0, 15, 0, 26, 7, 0, 1, 5, 0, 0, 10, 48, 4, 0, 2, 26, 1, 3, 30, 0, 1, 147, 37, 1, 1, 21, 0, 5, 1, 3, 0, 57, 27, 1, 5, 0, 18, 15, 0, 39, 1, 0, 0, 55, 4, 18, 2, 0, 33, 9, 2, 2, 0, 0, 0, 2, 57, 0, 0, 0, 2, 57, 0, 0, 9, 1, 42, 0, 78, 0, 1, 0, 64, 0, 3, 1, 1, 23, 1, 33, 18, 2, 1, 23, 0, 4, 3, 1, 0, 0, 10, 156, 122, 9, 2, 2, 26, 0, 2, 1, 0, 4, 0, 1, 2, 59, 4, 0, 0, 6, 18, 2, 24, 5, 0, 0, 0, 6, 3, 60, 41, 8, 0, 3, 3, 2, 23, 9, 1, 0, 2, 0, 6, 35, 8, 0, 0, 9, 18, 0, 5, 24, 141, 0, 6, 5, 29, 8, 15, 0, 0, 0, 6, 14, 0, 0, 30, 13, 3, 1, 31, 125, 1, 0, 5, 23, 0, 0, 2, 5, 26, 43, 79, 44, 2, 3, 1, 160, 2, 1, 0, 29, 40, 9, 6, 156, 1, 28, 2, 0, 28, 7, 15, 8, 1, 9, 11, 0, 1, 37, 0, 8, 9, 1, 1, 21, 0, 0, 1, 0, 2, 3, 0, 0, 0, 0, 147, 58, 8, 0, 1, 0, 16, 106, 11, 3, 12, 37, 16, 0, 32, 0, 0, 0, 1, 0, 0, 173, 39, 9, 0, 13, 0, 7, 2, 0, 0, 0, 16, 44, 25, 4, 5, 82, 0, 36, 71, 10, 1, 0, 1, 0, 0, 11, 5, 0, 78, 0, 13, 19, 0, 4, 1, 0, 2, 2, 2, 0, 0, 1, 19, 0, 0, 3, 1, 0, 17, 3, 0, 0, 1, 4, 1, 0, 0, 0, 5, 0, 2, 4, 1, 0, 0, 89, 0, 0, 0, 1, 14, 1, 0, 1, 0, 1, 45, 20, 1, 0, 9, 0, 0, 20, 0, 8, 46, 0, 1, 92, 71, 4, 1, 1, 17, 6, 28, 15, 5, 0, 2, 1, 0, 20, 45, 5, 0, 1, 0, 2, 0, 7, 0, 8, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 1, 0, 13, 2, 0, 21, 20, 1, 0, 0, 0, 0, 60, 8, 6, 5, 0, 0, 35, 0, 1, 4, 34, 0, 1, 1, 124, 173]\n"
     ]
    }
   ],
   "source": [
    "print(Player_six)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "print(Player_century)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[123, 121, 1, 0, 9, 12, 0, 0, 195, 0, 294, 74, 19, 0, 0, 6, 1, 6, 110, 9, 0, 34, 1, 1, 45, 0, 198, 26, 296, 15, 2, 0, 4, 121, 0, 21, 32, 14, 0, 84, 1, 22, 39, 4, 0, 0, 3, 5, 24, 11, 8, 0, 12, 0, 401, 0, 10, 16, 11, 0, 1, 11, 28, 0, 2, 0, 245, 0, 31, 0, 0, 0, 26, 1, 23, 9, 0, 9, 23, 0, 0, 56, 102, 5, 1, 5, 55, 2, 26, 40, 6, 9, 239, 87, 7, 23, 60, 1, 21, 3, 14, 1, 112, 87, 1, 1, 0, 134, 44, 0, 200, 2, 1, 0, 61, 50, 15, 21, 0, 72, 36, 13, 7, 1, 1, 0, 4, 130, 1, 0, 0, 6, 125, 1, 1, 35, 4, 137, 0, 104, 1, 0, 0, 197, 0, 10, 3, 4, 36, 1, 82, 22, 30, 6, 93, 0, 10, 3, 3, 0, 0, 9, 287, 257, 17, 9, 26, 135, 3, 22, 3, 1, 14, 0, 6, 11, 170, 13, 0, 5, 19, 24, 15, 61, 11, 1, 0, 16, 17, 1, 90, 76, 24, 1, 6, 15, 5, 33, 12, 2, 0, 3, 2, 10, 297, 13, 3, 2, 25, 34, 0, 19, 140, 204, 9, 22, 6, 44, 12, 49, 0, 3, 0, 10, 9, 1, 0, 47, 9, 14, 1, 83, 377, 1, 0, 13, 34, 0, 3, 2, 11, 50, 122, 205, 255, 5, 27, 4, 383, 23, 1, 0, 59, 91, 32, 14, 251, 3, 24, 1, 0, 154, 13, 22, 12, 0, 20, 40, 0, 5, 77, 0, 6, 11, 0, 4, 54, 0, 1, 5, 1, 7, 2, 0, 1, 3, 5, 157, 484, 8, 5, 12, 0, 33, 334, 49, 5, 36, 48, 23, 1, 79, 1, 3, 1, 1, 1, 1, 354, 152, 20, 0, 34, 1, 9, 11, 0, 0, 0, 26, 109, 31, 7, 5, 96, 2, 76, 286, 43, 14, 0, 14, 0, 0, 35, 27, 0, 266, 2, 25, 59, 4, 3, 22, 0, 2, 10, 4, 0, 2, 0, 69, 1, 0, 4, 0, 1, 22, 3, 3, 0, 5, 9, 0, 5, 7, 7, 39, 0, 5, 12, 2, 0, 0, 237, 7, 0, 0, 3, 18, 1, 1, 2, 0, 0, 93, 20, 4, 1, 94, 0, 1, 61, 6, 8, 66, 1, 1, 239, 401, 8, 1, 6, 43, 7, 269, 16, 4, 9, 2, 1, 0, 55, 150, 33, 0, 6, 6, 9, 0, 8, 3, 9, 3, 3, 5, 3, 0, 5, 4, 0, 2, 1, 3, 0, 39, 1, 2, 90, 46, 4, 4, 5, 0, 3, 320, 20, 2, 11, 1, 0, 95, 0, 0, 10, 72, 0, 5, 2, 277, 402]\n"
     ]
    }
   ],
   "source": [
    "print(Player_four)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Players_Runs = [1993, 1107, 6, 11, 98, 98, 15, 6, 1687, 4, 3626, 974, 167, 15, 2, 88, 4, 34, 1295, 91, 14, 606, 12, 26, 278, 9, 1977, 241, 2334, 181, 56, 2, 38, 1553, 12, 145, 394, 127, 3, 768, 19, 270, 531, 55, 0, 3, 18, 69, 342, 81, 94, 0, 103, 2, 4014, 11, 96, 106, 159, 5, 6, 125, 280, 3, 11, 16, 2385, 16, 384, 2, 2, 0, 326, 7, 422, 136, 2, 88, 226, 4, 0, 423, 1262, 77, 24, 39, 676, 33, 192, 521, 76, 59, 2904, 1139, 113, 280, 577, 20, 193, 36, 166, 5, 1426, 969, 25, 39, 6, 1112, 498, 1, 1802, 52, 26, 0, 974, 390, 295, 120, 2, 893, 342, 125, 87, 10, 19, 3, 46, 1557, 9, 8, 1, 90, 1732, 10, 22, 375, 35, 1349, 5, 1563, 4, 8, 0, 2215, 5, 131, 42, 46, 527, 20, 814, 420, 193, 73, 798, 4, 92, 54, 29, 6, 10, 124, 3473, 2622, 218, 92, 241, 1158, 31, 231, 41, 5, 163, 1, 67, 117, 1603, 153, 0, 33, 177, 401, 164, 725, 137, 20, 22, 140, 169, 49, 1322, 799, 237, 7, 117, 158, 62, 439, 177, 36, 1, 51, 12, 126, 2322, 128, 31, 21, 327, 368, 0, 217, 1153, 2587, 73, 259, 106, 724, 147, 515, 9, 28, 20, 117, 225, 17, 3, 564, 203, 130, 23, 886, 3778, 15, 0, 179, 506, 1, 15, 39, 111, 527, 1400, 2416, 2427, 49, 196, 36, 4418, 170, 25, 2, 766, 1001, 300, 198, 3560, 34, 406, 19, 9, 1441, 194, 316, 205, 7, 304, 339, 3, 79, 985, 10, 99, 187, 15, 34, 608, 7, 23, 42, 26, 64, 47, 3, 4, 18, 38, 2344, 4132, 124, 47, 101, 0, 405, 2728, 417, 66, 271, 574, 304, 7, 807, 11, 35, 10, 28, 14, 6, 4207, 1648, 285, 13, 319, 13, 161, 121, 3, 2, 1, 353, 1079, 437, 87, 76, 1228, 19, 954, 2903, 514, 159, 0, 121, 1, 0, 411, 340, 10, 2477, 26, 302, 663, 29, 52, 183, 4, 24, 99, 48, 4, 20, 12, 604, 7, 1, 53, 26, 18, 340, 53, 30, 6, 40, 123, 13, 23, 59, 40, 409, 0, 91, 127, 18, 1, 7, 2511, 53, 2, 4, 32, 317, 18, 12, 36, 10, 8, 1276, 373, 63, 12, 739, 2, 5, 505, 37, 95, 1017, 15, 20, 2069, 3561, 112, 19, 43, 391, 81, 2174, 280, 127, 49, 46, 21, 23, 672, 1703, 282, 8, 63, 34, 75, 1, 151, 35, 116, 14, 45, 52, 19, 3, 50, 20, 12, 81, 19, 42, 4, 388, 36, 14, 726, 480, 51, 25, 39, 5, 18, 3057, 189, 63, 126, 7, 8, 957, 3, 11, 83, 854, 2, 55, 24, 2753, 4540]\n",
    "\n",
    "Players_SR = [124.87468671679198, 137.51552795031057, 46.15384615384615, 122.22222222222223, 83.76068376068376, 104.25531914893618, 55.55555555555556, 85.71428571428571, 121.19252873563218, 30.76923076923077, 151.209341117598, 129.86666666666667, 126.51515151515152, 53.57142857142857, 40.0, 88.88888888888889, 133.33333333333331, 100.0, 127.96442687747036, 112.34567901234568, 233.33333333333334, 128.1183932346723, 100.0, 108.33333333333333, 143.29896907216494, 75.0, 122.64267990074443, 129.56989247311827, 119.81519507186857, 126.57342657342659, 74.66666666666667, 66.66666666666666, 122.58064516129032, 118.6401833460657, 66.66666666666666, 133.0275229357798, 127.50809061488673, 127.0, 150.0, 144.36090225563908, 73.07692307692307, 116.88311688311688, 117.21854304635761, 79.71014492753623, 0.0, 60.0, 72.0, 81.17647058823529, 120.4225352112676, 119.11764705882352, 104.44444444444446, 0.0, 100.98039215686273, 25.0, 142.1388101983003, 68.75, 97.95918367346938, 176.66666666666666, 112.7659574468085, 38.46153846153847, 120.0, 97.65625, 111.55378486055776, 50.0, 61.111111111111114, 35.55555555555556, 135.20408163265304, 69.56521739130434, 158.02469135802468, 100.0, 50.0, 0.0, 138.72340425531914, 100.0, 137.45928338762215, 120.35398230088497, 20.0, 104.76190476190477, 134.52380952380955, 57.14285714285714, 0.0, 124.7787610619469, 126.32632632632632, 140.0, 68.57142857142857, 169.56521739130434, 117.56521739130434, 76.74418604651163, 107.86516853932584, 129.92518703241896, 78.35051546391753, 147.5, 145.49098196392785, 120.40169133192389, 104.62962962962963, 89.45686900958466, 141.76904176904176, 66.66666666666666, 137.85714285714286, 124.13793103448276, 105.0632911392405, 62.5, 124.43280977312392, 128.1746031746032, 125.0, 205.26315789473685, 50.0, 120.8695652173913, 130.36649214659687, 33.33333333333333, 123.25581395348837, 68.42105263157895, 74.28571428571429, 0.0, 141.98250728862973, 99.74424552429667, 122.40663900414938, 133.33333333333331, 66.66666666666666, 134.89425981873111, 125.73529411764706, 110.61946902654867, 114.4736842105263, 71.42857142857143, 79.16666666666666, 50.0, 102.22222222222221, 130.73047858942067, 90.0, 72.72727272727273, 100.0, 93.75, 122.92405961674946, 111.11111111111111, 100.0, 118.29652996845425, 145.83333333333331, 106.80918448139352, 100.0, 141.44796380090497, 200.0, 72.72727272727273, 0.0, 120.11930585683297, 62.5, 98.49624060150376, 91.30434782608695, 97.87234042553192, 135.47557840616966, 105.26315789473684, 123.33333333333334, 123.16715542521995, 119.87577639751552, 97.33333333333334, 113.67521367521367, 44.44444444444444, 93.87755102040816, 154.2857142857143, 111.53846153846155, 60.0, 58.82352941176471, 187.87878787878788, 148.1655290102389, 138.65679534637758, 94.37229437229438, 101.0989010989011, 96.01593625498009, 126.9736842105263, 119.23076923076923, 96.25, 66.12903225806451, 166.66666666666669, 123.48484848484848, 50.0, 104.6875, 82.97872340425532, 130.43124491456467, 113.33333333333333, 0.0, 137.5, 173.52941176470588, 112.64044943820224, 102.49999999999999, 125.64991334488735, 135.64356435643566, 71.42857142857143, 66.66666666666666, 99.29078014184397, 112.66666666666667, 84.48275862068965, 122.97674418604652, 142.42424242424244, 113.9423076923077, 50.0, 104.46428571428572, 100.63694267515923, 116.98113207547169, 165.66037735849056, 143.90243902439025, 73.46938775510205, 50.0, 102.0, 109.09090909090908, 136.95652173913044, 117.21352852094901, 164.1025641025641, 86.11111111111111, 80.76923076923077, 108.63787375415282, 126.02739726027397, 0.0, 103.33333333333334, 114.49851042701091, 131.1866125760649, 94.8051948051948, 103.60000000000001, 98.14814814814815, 125.91304347826086, 132.43243243243242, 113.68653421633555, 69.23076923076923, 96.55172413793103, 76.92307692307693, 144.44444444444443, 114.79591836734696, 85.0, 37.5, 151.20643431635386, 130.12820512820514, 107.43801652892562, 88.46153846153845, 109.78934324659231, 131.6376306620209, 115.38461538461537, 0.0, 116.23376623376625, 130.0771208226221, 33.33333333333333, 78.94736842105263, 73.58490566037736, 109.9009900990099, 145.98337950138506, 125.22361359570662, 126.16187989556136, 109.22592259225922, 125.64102564102564, 118.7878787878788, 138.46153846153845, 129.8266235674405, 124.08759124087592, 96.15384615384616, 50.0, 126.82119205298012, 134.72409152086138, 100.0, 92.5233644859813, 136.7127496159754, 154.54545454545453, 142.4561403508772, 111.76470588235294, 64.28571428571429, 118.8943894389439, 116.1676646706587, 142.98642533936652, 138.51351351351352, 77.77777777777779, 112.1771217712177, 124.17582417582418, 42.857142857142854, 101.28205128205127, 117.82296650717703, 58.82352941176471, 139.43661971830986, 152.03252032520325, 83.33333333333334, 130.76923076923077, 130.752688172043, 58.333333333333336, 74.19354838709677, 100.0, 37.142857142857146, 77.10843373493977, 138.23529411764704, 75.0, 80.0, 150.0, 122.58064516129032, 146.59161976235146, 124.60796139927623, 127.83505154639174, 71.21212121212122, 134.66666666666666, 0.0, 115.71428571428572, 155.44159544159544, 136.27450980392157, 88.0, 147.2826086956522, 173.41389728096678, 116.47509578544062, 175.0, 128.50318471337582, 122.22222222222223, 74.46808510638297, 83.33333333333334, 100.0, 51.85185185185185, 85.71428571428571, 130.8960796515246, 117.29537366548041, 110.03861003861005, 56.52173913043478, 129.1497975708502, 100.0, 93.6046511627907, 107.07964601769913, 100.0, 100.0, 100.0, 129.77941176470588, 126.6431924882629, 128.90855457227138, 129.8507462686567, 138.1818181818182, 164.3908969210174, 82.6086956521739, 126.35761589403972, 125.94360086767895, 109.36170212765957, 92.98245614035088, 0.0, 101.68067226890756, 16.666666666666664, 0.0, 129.24528301886792, 93.66391184573003, 38.46153846153847, 132.7438370846731, 54.166666666666664, 122.76422764227641, 121.875, 111.53846153846155, 104.0, 105.78034682080926, 133.33333333333331, 171.42857142857142, 111.23595505617978, 150.0, 80.0, 90.9090909090909, 133.33333333333331, 131.87772925764193, 87.5, 50.0, 176.66666666666666, 118.18181818181819, 46.15384615384615, 108.28025477707006, 103.921568627451, 100.0, 42.857142857142854, 153.84615384615387, 123.0, 162.5, 100.0, 103.50877192982458, 114.28571428571428, 113.9275766016713, 0.0, 71.09375, 141.11111111111111, 163.63636363636365, 100.0, 58.333333333333336, 123.39066339066338, 74.64788732394366, 100.0, 100.0, 106.66666666666667, 115.69343065693431, 112.5, 70.58823529411765, 92.3076923076923, 76.92307692307693, 88.88888888888889, 119.58762886597938, 139.17910447761196, 84.0, 133.33333333333331, 110.62874251497006, 28.57142857142857, 166.66666666666669, 120.8133971291866, 137.03703703703704, 179.24528301886792, 123.72262773722629, 93.75, 111.11111111111111, 138.39464882943145, 121.86858316221767, 109.80392156862746, 146.15384615384613, 138.70967741935485, 146.44194756554307, 176.08695652173913, 115.5154091392136, 145.0777202072539, 92.02898550724638, 84.48275862068965, 83.63636363636363, 91.30434782608695, 85.18518518518519, 116.46447140381282, 131.7092034029389, 105.61797752808988, 53.333333333333336, 95.45454545454545, 61.81818181818181, 129.31034482758622, 50.0, 98.69281045751634, 94.5945945945946, 170.58823529411765, 107.6923076923077, 77.58620689655173, 110.63829787234043, 111.76470588235294, 60.0, 98.0392156862745, 95.23809523809523, 60.0, 110.95890410958904, 86.36363636363636, 71.1864406779661, 57.14285714285714, 128.05280528052805, 120.0, 107.6923076923077, 132.24043715846994, 158.41584158415841, 100.0, 80.64516129032258, 95.1219512195122, 35.714285714285715, 100.0, 120.59171597633136, 132.16783216783216, 143.1818181818182, 140.0, 77.77777777777779, 72.72727272727273, 132.73231622746187, 50.0, 57.89473684210527, 113.69863013698631, 121.13475177304964, 20.0, 85.9375, 109.09090909090908, 131.22020972354625, 139.09313725490196]\n",
    "\n",
    "#Players_avg = [39.86, 35.70967741935484, 6.0, 5.5, 16.333333333333332, 16.333333333333332, 7.5, 6.0, 25.953846153846154, 4.0, 40.741573033707866, 36.074074074074076, 55.666666666666664, 5.0, 2.0, 5.5, 2.0, 6.8, 30.11627906976744, 22.75, 14.0, 20.2, inf, 13.0, 23.166666666666668, 9.0, 38.76470588235294, 24.1, 33.82608695652174, 45.25, 9.333333333333334, 2.0, 7.6, 20.986486486486488, 6.0, 18.125, 28.142857142857142, 21.166666666666668, inf, 27.428571428571427, 6.333333333333333, 15.882352941176471, 29.5, 11.0, nan, 1.5, 4.5, 13.8, 11.4, 20.25, 13.428571428571429, 0.0, 14.714285714285714, 2.0, 40.14, 5.5, 13.714285714285714, 21.2, 14.454545454545455, 5.0, inf, 17.857142857142858, 18.666666666666668, 1.5, 11.0, 1.3333333333333333, 28.392857142857142, 5.333333333333333, 38.4, 2.0, 2.0, 0.0, 23.285714285714285, 7.0, 19.181818181818183, 17.0, 0.5, 44.0, 22.6, 4.0, nan, 21.15, 22.945454545454545, 12.833333333333334, 4.0, 7.8, 21.125, 8.25, 24.0, 27.42105263157895, 15.2, 29.5, 29.632653061224488, 21.49056603773585, 16.142857142857142, 15.555555555555555, 44.38461538461539, 3.3333333333333335, 27.571428571428573, 12.0, 7.545454545454546, 2.5, 25.464285714285715, 28.5, 25.0, 13.0, 6.0, 20.21818181818182, 20.75, 1.0, 28.15625, 3.466666666666667, 26.0, 0.0, 24.35, 20.526315789473685, 19.666666666666668, 17.142857142857142, 1.0, 23.5, 16.285714285714285, 25.0, 21.75, 10.0, 19.0, 3.0, 6.571428571428571, 25.524590163934427, 9.0, 8.0, inf, 11.25, 24.055555555555557, 10.0, 7.333333333333333, 19.736842105263158, 17.5, 25.452830188679247, 5.0, 34.733333333333334, inf, 2.6666666666666665, 0.0, 28.397435897435898, inf, 18.714285714285715, 14.0, 11.5, 20.26923076923077, 10.0, 17.695652173913043, 18.26086956521739, 27.571428571428573, 18.25, 21.0, 4.0, 13.142857142857142, 9.0, 14.5, 3.0, 10.0, 24.8, 38.16483516483517, 30.847058823529412, 14.533333333333333, 11.5, 16.066666666666666, 25.17391304347826, 10.333333333333334, 10.5, 5.857142857142857, 5.0, 23.285714285714285, 1.0, 22.333333333333332, 8.357142857142858, 27.16949152542373, 21.857142857142858, 0.0, 16.5, 29.5, 19.095238095238095, 16.4, 30.208333333333332, 17.125, 5.0, 7.333333333333333, 14.0, 16.9, 7.0, 26.979591836734695, 15.666666666666666, 14.8125, 7.0, 9.75, 8.31578947368421, 31.0, 36.583333333333336, 17.7, 4.5, 1.0, 10.2, 6.0, 15.75, 21.700934579439252, 25.6, 15.5, 10.5, 29.727272727272727, 18.4, nan, 16.692307692307693, 26.813953488372093, 25.613861386138613, 18.25, 14.38888888888889, 13.25, 23.35483870967742, 18.375, 12.261904761904763, inf, 28.0, 10.0, 16.714285714285715, 18.75, 8.5, 3.0, 25.636363636363637, 40.6, 16.25, 7.666666666666667, 27.6875, 29.515625, 7.5, 0.0, 17.9, 33.733333333333334, 1.0, 15.0, 13.0, 27.75, 23.954545454545453, 33.333333333333336, 27.146067415730336, 28.55294117647059, 9.8, 21.77777777777778, 36.0, 37.440677966101696, 17.0, 12.5, 2.0, 20.157894736842106, 35.75, 15.0, 9.9, 37.87234042553192, 17.0, 21.36842105263158, 6.333333333333333, inf, 30.659574468085108, 21.555555555555557, 31.6, 29.285714285714285, 3.5, 10.857142857142858, 14.125, 3.0, 8.777777777777779, 22.386363636363637, 10.0, 9.9, 23.375, 5.0, 6.8, 20.96551724137931, 3.5, 11.5, 21.0, 2.1666666666666665, 9.142857142857142, 5.222222222222222, 3.0, 4.0, 4.5, 19.0, 28.585365853658537, 31.541984732824428, 12.4, 11.75, 50.5, nan, 15.0, 27.555555555555557, 26.0625, 4.714285714285714, 13.55, 26.09090909090909, 21.714285714285715, inf, 27.82758620689655, 11.0, 11.666666666666666, 5.0, inf, 7.0, 6.0, 32.6124031007752, 29.428571428571427, 20.357142857142858, 2.6, 21.266666666666666, 6.5, 12.384615384615385, 15.125, 3.0, inf, inf, 17.65, 39.96296296296296, 31.214285714285715, 12.428571428571429, 12.666666666666666, 25.06122448979592, 19.0, 26.5, 24.811965811965813, 22.347826086956523, 19.875, 0.0, 30.25, 0.5, 0.0, 31.615384615384617, 11.724137931034482, 5.0, 39.317460317460316, 2.6, 27.454545454545453, 24.555555555555557, 14.5, 6.5, 18.3, inf, 12.0, 14.142857142857142, 48.0, 4.0, 10.0, inf, 21.571428571428573, 3.5, inf, 26.5, 26.0, 9.0, 8.947368421052632, 10.6, 10.0, 3.0, 20.0, 24.6, 13.0, 7.666666666666667, 14.75, 13.333333333333334, 20.45, 0.0, 10.11111111111111, 31.75, 18.0, inf, 3.5, 26.431578947368422, 8.833333333333334, inf, inf, 10.666666666666666, 15.095238095238095, 9.0, 12.0, 12.0, 10.0, 4.0, 28.355555555555554, 14.346153846153847, 7.0, inf, 28.423076923076923, 2.0, inf, 22.954545454545453, 12.333333333333334, 11.875, 25.425, 15.0, 20.0, 27.223684210526315, 32.669724770642205, 8.0, 19.0, 10.75, 27.928571428571427, 10.125, 28.233766233766232, 18.666666666666668, 11.545454545454545, 9.8, 11.5, 21.0, 7.666666666666667, 17.68421052631579, 37.02173913043478, 14.842105263157896, 8.0, 15.75, 8.5, 15.0, inf, 11.615384615384615, 35.0, 12.88888888888889, 7.0, 7.5, 13.0, 19.0, 3.0, 10.0, 6.666666666666667, 4.0, 10.125, 9.5, 6.0, inf, 20.42105263157895, 9.0, 7.0, 29.04, 34.285714285714285, 10.2, 8.333333333333334, 6.5, 2.5, 9.0, 33.59340659340659, 21.0, 7.875, 11.454545454545455, 2.3333333333333335, 8.0, 24.53846153846154, 1.5, 3.6666666666666665, 13.833333333333334, 21.35, 2.0, 18.333333333333332, 12.0, 28.091836734693878, 33.88059701492537]\n",
    "\n",
    "P_avg = [39.86, 35.70967741935484, 6.0, 5.5, 16.333333333333332, 16.333333333333332, 7.5, 6.0, 25.953846153846154, 4.0, 40.741573033707866, 36.074074074074076, 55.666666666666664, 5.0, 2.0, 5.5, 2.0, 6.8, 30.11627906976744, 22.75, 14.0, 20.2, 0, 13.0, 23.166666666666668, 9.0, 38.76470588235294, 24.1, 33.82608695652174, 45.25, 9.333333333333334, 2.0, 7.6, 20.986486486486488, 6.0, 18.125, 28.142857142857142, 21.166666666666668, 0, 27.428571428571427, 6.333333333333333, 15.882352941176471, 29.5, 11.0, 0, 1.5, 4.5, 13.8, 11.4, 20.25, 13.428571428571429, 0.0, 14.714285714285714, 2.0, 40.14, 5.5, 13.714285714285714, 21.2, 14.454545454545455, 5.0, 0, 17.857142857142858, 18.666666666666668, 1.5, 11.0, 1.3333333333333333, 28.392857142857142, 5.333333333333333, 38.4, 2.0, 2.0, 0.0, 23.285714285714285, 7.0, 19.181818181818183, 17.0, 0.5, 44.0, 22.6, 4.0, 0, 21.15, 22.945454545454545, 12.833333333333334, 4.0, 7.8, 21.125, 8.25, 24.0, 27.42105263157895, 15.2, 29.5, 29.632653061224488, 21.49056603773585, 16.142857142857142, 15.555555555555555, 44.38461538461539, 3.3333333333333335, 27.571428571428573, 12.0, 7.545454545454546, 2.5, 25.464285714285715, 28.5, 25.0, 13.0, 6.0, 20.21818181818182, 20.75, 1.0, 28.15625, 3.466666666666667, 26.0, 0.0, 24.35, 20.526315789473685, 19.666666666666668, 17.142857142857142, 1.0, 23.5, 16.285714285714285, 25.0, 21.75, 10.0, 19.0, 3.0, 6.571428571428571, 25.524590163934427, 9.0, 8.0, 0, 11.25, 24.055555555555557, 10.0, 7.333333333333333, 19.736842105263158, 17.5, 25.452830188679247, 5.0, 34.733333333333334, 0, 2.6666666666666665, 0.0, 28.397435897435898, 0, 18.714285714285715, 14.0, 11.5, 20.26923076923077, 10.0, 17.695652173913043, 18.26086956521739, 27.571428571428573, 18.25, 21.0, 4.0, 13.142857142857142, 9.0, 14.5, 3.0, 10.0, 24.8, 38.16483516483517, 30.847058823529412, 14.533333333333333, 11.5, 16.066666666666666, 25.17391304347826, 10.333333333333334, 10.5, 5.857142857142857, 5.0, 23.285714285714285, 1.0, 22.333333333333332, 8.357142857142858, 27.16949152542373, 21.857142857142858, 0.0, 16.5, 29.5, 19.095238095238095, 16.4, 30.208333333333332, 17.125, 5.0, 7.333333333333333, 14.0, 16.9, 7.0, 26.979591836734695, 15.666666666666666, 14.8125, 7.0, 9.75, 8.31578947368421, 31.0, 36.583333333333336, 17.7, 4.5, 1.0, 10.2, 6.0, 15.75, 21.700934579439252, 25.6, 15.5, 10.5, 29.727272727272727, 18.4, 0, 16.692307692307693, 26.813953488372093, 25.613861386138613, 18.25, 14.38888888888889, 13.25, 23.35483870967742, 18.375, 12.261904761904763, 0, 28.0, 10.0, 16.714285714285715, 18.75, 8.5, 3.0, 25.636363636363637, 40.6, 16.25, 7.666666666666667, 27.6875, 29.515625, 7.5, 0.0, 17.9, 33.733333333333334, 1.0, 15.0, 13.0, 27.75, 23.954545454545453, 33.333333333333336, 27.146067415730336, 28.55294117647059, 9.8, 21.77777777777778, 36.0, 37.440677966101696, 17.0, 12.5, 2.0, 20.157894736842106, 35.75, 15.0, 9.9, 37.87234042553192, 17.0, 21.36842105263158, 6.333333333333333, 0, 30.659574468085108, 21.555555555555557, 31.6, 29.285714285714285, 3.5, 10.857142857142858, 14.125, 3.0, 8.777777777777779, 22.386363636363637, 10.0, 9.9, 23.375, 5.0, 6.8, 20.96551724137931, 3.5, 11.5, 21.0, 2.1666666666666665, 9.142857142857142, 5.222222222222222, 3.0, 4.0, 4.5, 19.0, 28.585365853658537, 31.541984732824428, 12.4, 11.75, 50.5, 0, 15.0, 27.555555555555557, 26.0625, 4.714285714285714, 13.55, 26.09090909090909, 21.714285714285715, 0, 27.82758620689655, 11.0, 11.666666666666666, 5.0, 0, 7.0, 6.0, 32.6124031007752, 29.428571428571427, 20.357142857142858, 2.6, 21.266666666666666, 6.5, 12.384615384615385, 15.125, 3.0, 0, 0, 17.65, 39.96296296296296, 31.214285714285715, 12.428571428571429, 12.666666666666666, 25.06122448979592, 19.0, 26.5, 24.811965811965813, 22.347826086956523, 19.875, 0.0, 30.25, 0.5, 0.0, 31.615384615384617, 11.724137931034482, 5.0, 39.317460317460316, 2.6, 27.454545454545453, 24.555555555555557, 14.5, 6.5, 18.3, 0, 12.0, 14.142857142857142, 48.0, 4.0, 10.0, 0, 21.571428571428573, 3.5, 0, 26.5, 26.0, 9.0, 8.947368421052632, 10.6, 10.0, 3.0, 20.0, 24.6, 13.0, 7.666666666666667, 14.75, 13.333333333333334, 20.45, 0.0, 10.11111111111111, 31.75, 18.0, 0, 3.5, 26.431578947368422, 8.833333333333334, 0, 0, 10.666666666666666, 15.095238095238095, 9.0, 12.0, 12.0, 10.0, 4.0, 28.355555555555554, 14.346153846153847, 7.0, 0, 28.423076923076923, 2.0, 0, 22.954545454545453, 12.333333333333334, 11.875, 25.425, 15.0, 20.0, 27.223684210526315, 32.669724770642205, 8.0, 19.0, 10.75, 27.928571428571427, 10.125, 28.233766233766232, 18.666666666666668, 11.545454545454545, 9.8, 11.5, 21.0, 7.666666666666667, 17.68421052631579, 37.02173913043478, 14.842105263157896, 8.0, 15.75, 8.5, 15.0, 0, 11.615384615384615, 35.0, 12.88888888888889, 7.0, 7.5, 13.0, 19.0, 3.0, 10.0, 6.666666666666667, 4.0, 10.125, 9.5, 6.0, 0, 20.42105263157895, 9.0, 7.0, 29.04, 34.285714285714285, 10.2, 8.333333333333334, 6.5, 2.5, 9.0, 33.59340659340659, 21.0, 7.875, 11.454545454545455, 2.3333333333333335, 8.0, 24.53846153846154, 1.5, 3.6666666666666665, 13.833333333333334, 21.35, 2.0, 18.333333333333332, 12.0, 28.091836734693878, 33.88059701492537]\n",
    "\n",
    "Player_fifty = [14, 8, 0, 0, 0, 0, 0, 0, 10, 0, 21, 5, 1, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 1, 0, 15, 2, 13, 0, 0, 0, 0, 6, 0, 1, 1, 0, 0, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 36, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 17, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 2, 4, 0, 0, 0, 2, 0, 0, 3, 0, 0, 13, 1, 0, 0, 3, 0, 1, 0, 0, 0, 7, 5, 0, 0, 0, 5, 2, 0, 10, 0, 0, 0, 3, 1, 1, 1, 0, 3, 1, 1, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 0, 8, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 22, 14, 0, 0, 1, 8, 0, 0, 0, 0, 1, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 5, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 10, 1, 0, 0, 1, 2, 0, 1, 9, 12, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 1, 0, 6, 22, 0, 0, 0, 4, 0, 0, 0, 0, 1, 6, 14, 17, 0, 0, 0, 30, 0, 0, 0, 0, 4, 1, 0, 17, 0, 1, 0, 0, 11, 0, 1, 1, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 35, 0, 0, 1, 0, 0, 16, 4, 0, 1, 3, 0, 0, 6, 0, 0, 0, 0, 0, 0, 32, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 11, 4, 0, 0, 6, 0, 6, 14, 0, 1, 0, 1, 0, 0, 3, 0, 0, 20, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 1, 0, 0, 4, 0, 0, 2, 0, 0, 3, 0, 0, 11, 28, 0, 0, 0, 2, 0, 11, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 5, 1, 0, 0, 0, 0, 0, 25, 1, 0, 0, 0, 0, 3, 0, 0, 1, 4, 0, 0, 0, 13, 31]\n",
    "\n",
    "Player_six = [78, 44, 0, 1, 0, 0, 0, 1, 27, 0, 265, 41, 1, 0, 0, 5, 0, 1, 38, 1, 2, 28, 0, 1, 6, 0, 52, 8, 29, 3, 0, 0, 2, 79, 0, 3, 13, 3, 0, 39, 1, 6, 16, 1, 0, 0, 0, 0, 13, 0, 3, 0, 3, 0, 160, 1, 0, 3, 8, 0, 0, 1, 7, 0, 0, 0, 117, 0, 23, 0, 0, 0, 15, 0, 26, 7, 0, 1, 5, 0, 0, 10, 48, 4, 0, 2, 26, 1, 3, 30, 0, 1, 147, 37, 1, 1, 21, 0, 5, 1, 3, 0, 57, 27, 1, 5, 0, 18, 15, 0, 39, 1, 0, 0, 55, 4, 18, 2, 0, 33, 9, 2, 2, 0, 0, 0, 2, 57, 0, 0, 0, 2, 57, 0, 0, 9, 1, 42, 0, 78, 0, 1, 0, 64, 0, 3, 1, 1, 23, 1, 33, 18, 2, 1, 23, 0, 4, 3, 1, 0, 0, 10, 156, 122, 9, 2, 2, 26, 0, 2, 1, 0, 4, 0, 1, 2, 59, 4, 0, 0, 6, 18, 2, 24, 5, 0, 0, 0, 6, 3, 60, 41, 8, 0, 3, 3, 2, 23, 9, 1, 0, 2, 0, 6, 35, 8, 0, 0, 9, 18, 0, 5, 24, 141, 0, 6, 5, 29, 8, 15, 0, 0, 0, 6, 14, 0, 0, 30, 13, 3, 1, 31, 125, 1, 0, 5, 23, 0, 0, 2, 5, 26, 43, 79, 44, 2, 3, 1, 160, 2, 1, 0, 29, 40, 9, 6, 156, 1, 28, 2, 0, 28, 7, 15, 8, 1, 9, 11, 0, 1, 37, 0, 8, 9, 1, 1, 21, 0, 0, 1, 0, 2, 3, 0, 0, 0, 0, 147, 58, 8, 0, 1, 0, 16, 106, 11, 3, 12, 37, 16, 0, 32, 0, 0, 0, 1, 0, 0, 173, 39, 9, 0, 13, 0, 7, 2, 0, 0, 0, 16, 44, 25, 4, 5, 82, 0, 36, 71, 10, 1, 0, 1, 0, 0, 11, 5, 0, 78, 0, 13, 19, 0, 4, 1, 0, 2, 2, 2, 0, 0, 1, 19, 0, 0, 3, 1, 0, 17, 3, 0, 0, 1, 4, 1, 0, 0, 0, 5, 0, 2, 4, 1, 0, 0, 89, 0, 0, 0, 1, 14, 1, 0, 1, 0, 1, 45, 20, 1, 0, 9, 0, 0, 20, 0, 8, 46, 0, 1, 92, 71, 4, 1, 1, 17, 6, 28, 15, 5, 0, 2, 1, 0, 20, 45, 5, 0, 1, 0, 2, 0, 7, 0, 8, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 1, 0, 13, 2, 0, 21, 20, 1, 0, 0, 0, 0, 60, 8, 6, 5, 0, 0, 35, 0, 1, 4, 34, 0, 1, 1, 124, 173]\n",
    "\n",
    "Player_century = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1]\n",
    "\n",
    "Player_four = [123, 121, 1, 0, 9, 12, 0, 0, 195, 0, 294, 74, 19, 0, 0, 6, 1, 6, 110, 9, 0, 34, 1, 1, 45, 0, 198, 26, 296, 15, 2, 0, 4, 121, 0, 21, 32, 14, 0, 84, 1, 22, 39, 4, 0, 0, 3, 5, 24, 11, 8, 0, 12, 0, 401, 0, 10, 16, 11, 0, 1, 11, 28, 0, 2, 0, 245, 0, 31, 0, 0, 0, 26, 1, 23, 9, 0, 9, 23, 0, 0, 56, 102, 5, 1, 5, 55, 2, 26, 40, 6, 9, 239, 87, 7, 23, 60, 1, 21, 3, 14, 1, 112, 87, 1, 1, 0, 134, 44, 0, 200, 2, 1, 0, 61, 50, 15, 21, 0, 72, 36, 13, 7, 1, 1, 0, 4, 130, 1, 0, 0, 6, 125, 1, 1, 35, 4, 137, 0, 104, 1, 0, 0, 197, 0, 10, 3, 4, 36, 1, 82, 22, 30, 6, 93, 0, 10, 3, 3, 0, 0, 9, 287, 257, 17, 9, 26, 135, 3, 22, 3, 1, 14, 0, 6, 11, 170, 13, 0, 5, 19, 24, 15, 61, 11, 1, 0, 16, 17, 1, 90, 76, 24, 1, 6, 15, 5, 33, 12, 2, 0, 3, 2, 10, 297, 13, 3, 2, 25, 34, 0, 19, 140, 204, 9, 22, 6, 44, 12, 49, 0, 3, 0, 10, 9, 1, 0, 47, 9, 14, 1, 83, 377, 1, 0, 13, 34, 0, 3, 2, 11, 50, 122, 205, 255, 5, 27, 4, 383, 23, 1, 0, 59, 91, 32, 14, 251, 3, 24, 1, 0, 154, 13, 22, 12, 0, 20, 40, 0, 5, 77, 0, 6, 11, 0, 4, 54, 0, 1, 5, 1, 7, 2, 0, 1, 3, 5, 157, 484, 8, 5, 12, 0, 33, 334, 49, 5, 36, 48, 23, 1, 79, 1, 3, 1, 1, 1, 1, 354, 152, 20, 0, 34, 1, 9, 11, 0, 0, 0, 26, 109, 31, 7, 5, 96, 2, 76, 286, 43, 14, 0, 14, 0, 0, 35, 27, 0, 266, 2, 25, 59, 4, 3, 22, 0, 2, 10, 4, 0, 2, 0, 69, 1, 0, 4, 0, 1, 22, 3, 3, 0, 5, 9, 0, 5, 7, 7, 39, 0, 5, 12, 2, 0, 0, 237, 7, 0, 0, 3, 18, 1, 1, 2, 0, 0, 93, 20, 4, 1, 94, 0, 1, 61, 6, 8, 66, 1, 1, 239, 401, 8, 1, 6, 43, 7, 269, 16, 4, 9, 2, 1, 0, 55, 150, 33, 0, 6, 6, 9, 0, 8, 3, 9, 3, 3, 5, 3, 0, 5, 4, 0, 2, 1, 3, 0, 39, 1, 2, 90, 46, 4, 4, 5, 0, 3, 320, 20, 2, 11, 1, 0, 95, 0, 0, 10, 72, 0, 5, 2, 277, 402]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the Data in Tabular Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ListTable(list):\n",
    "    def _repr_html_(self):\n",
    "        html = [\"<table width=100%>\"]\n",
    "        row_count = 0\n",
    "        \n",
    "        for row in self:\n",
    "            row_count = row_count + 1\n",
    "            \n",
    "            if(row_count == 1):\n",
    "                html.append(\"<tr>\")\n",
    "                \n",
    "                for col in row:\n",
    "                    html.append(\"<th align = 'center'>{0}</th>\".format(col))\n",
    "                \n",
    "                html.append(\"</tr>\")\n",
    "            \n",
    "            else:\n",
    "                html.append(\"<tr>\")\n",
    "                col_count = 0\n",
    "                \n",
    "                for col in row:\n",
    "                    if(col_count == 0):\n",
    "                        html.append(\"<th>{0}</th>\".format(col))\n",
    "                    else:\n",
    "                        html.append(\"<td>{0}</td>\".format(col))\n",
    "                        \n",
    "                    col_count = col_count + 1\n",
    "                html.append(\"</tr>\")\n",
    "        \n",
    "        html.append(\"</table>\")\n",
    "        return ''.join(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table with Name, Runs, Average, Strike Rate, Fifty, Century, Four, Sixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width=100%><tr><th align = 'center'>Name</th><th align = 'center'>Runs</th><th align = 'center'>Average</th><th align = 'center'>Strike rate</th><th align = 'center'>Fifty</th><th align = 'center'>Century</th><th align = 'center'>Fours</th><th align = 'center'>Sixes</th></tr><tr><th>JP Duminy</th><td>1993</td><td>39.86</td><td>124.87468671679198</td><td>14</td><td>0</td><td>123</td><td>78</td></tr><tr><th>ML Hayden</th><td>1107</td><td>35.70967741935484</td><td>137.51552795031057</td><td>8</td><td>0</td><td>121</td><td>44</td></tr><tr><th>AS Rajpoot</th><td>6</td><td>6.0</td><td>46.15384615384615</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Rashid Khan</th><td>11</td><td>5.5</td><td>122.22222222222223</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>CJ Ferguson</th><td>98</td><td>16.333333333333332</td><td>83.76068376068376</td><td>0</td><td>0</td><td>9</td><td>0</td></tr><tr><th>MJ Clarke</th><td>98</td><td>16.333333333333332</td><td>104.25531914893618</td><td>0</td><td>0</td><td>12</td><td>0</td></tr><tr><th>VY Mahesh</th><td>15</td><td>7.5</td><td>55.55555555555556</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>BA Bhatt</th><td>6</td><td>6.0</td><td>85.71428571428571</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>KC Sangakkara</th><td>1687</td><td>25.953846153846154</td><td>121.19252873563218</td><td>10</td><td>0</td><td>195</td><td>27</td></tr><tr><th>DP Nannes</th><td>4</td><td>4.0</td><td>30.76923076923077</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>CH Gayle</th><td>3626</td><td>40.741573033707866</td><td>151.209341117598</td><td>21</td><td>5</td><td>294</td><td>265</td></tr><tr><th>A Symonds</th><td>974</td><td>36.074074074074076</td><td>129.86666666666667</td><td>5</td><td>1</td><td>74</td><td>41</td></tr><tr><th>MN van Wyk</th><td>167</td><td>55.666666666666664</td><td>126.51515151515152</td><td>1</td><td>0</td><td>19</td><td>1</td></tr><tr><th>YS Chahal</th><td>15</td><td>5.0</td><td>53.57142857142857</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>H Das</th><td>2</td><td>2.0</td><td>40.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>SL Malinga</th><td>88</td><td>5.5</td><td>88.88888888888889</td><td>0</td><td>0</td><td>6</td><td>5</td></tr><tr><th>VRV Singh</th><td>4</td><td>2.0</td><td>133.33333333333331</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>L Ronchi</th><td>34</td><td>6.8</td><td>100.0</td><td>0</td><td>0</td><td>6</td><td>1</td></tr><tr><th>F du Plessis</th><td>1295</td><td>30.11627906976744</td><td>127.96442687747036</td><td>8</td><td>0</td><td>110</td><td>38</td></tr><tr><th>S Rana</th><td>91</td><td>22.75</td><td>112.34567901234568</td><td>0</td><td>0</td><td>9</td><td>1</td></tr><tr><th>DL Chahar</th><td>14</td><td>14.0</td><td>233.33333333333334</td><td>0</td><td>0</td><td>0</td><td>2</td></tr><tr><th>AR Patel</th><td>606</td><td>20.2</td><td>128.1183932346723</td><td>0</td><td>0</td><td>34</td><td>28</td></tr><tr><th>AR Bawne</th><td>12</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>JM Kemp</th><td>26</td><td>13.0</td><td>108.33333333333333</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>MJ Lumb</th><td>278</td><td>23.166666666666668</td><td>143.29896907216494</td><td>1</td><td>0</td><td>45</td><td>6</td></tr><tr><th>Washington Sundar</th><td>9</td><td>9.0</td><td>75.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>MEK Hussey</th><td>1977</td><td>38.76470588235294</td><td>122.64267990074443</td><td>15</td><td>1</td><td>198</td><td>52</td></tr><tr><th>SM Katich</th><td>241</td><td>24.1</td><td>129.56989247311827</td><td>2</td><td>0</td><td>26</td><td>8</td></tr><tr><th>SR Tendulkar</th><td>2334</td><td>33.82608695652174</td><td>119.81519507186857</td><td>13</td><td>1</td><td>296</td><td>29</td></tr><tr><th>AC Voges</th><td>181</td><td>45.25</td><td>126.57342657342659</td><td>0</td><td>0</td><td>15</td><td>3</td></tr><tr><th>WA Mota</th><td>56</td><td>9.333333333333334</td><td>74.66666666666667</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>Mohammad Nabi</th><td>2</td><td>2.0</td><td>66.66666666666666</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>JO Holder</th><td>38</td><td>7.6</td><td>122.58064516129032</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><th>NV Ojha</th><td>1553</td><td>20.986486486486488</td><td>118.6401833460657</td><td>6</td><td>0</td><td>121</td><td>79</td></tr><tr><th>T Thushara</th><td>12</td><td>6.0</td><td>66.66666666666666</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>S Vidyut</th><td>145</td><td>18.125</td><td>133.0275229357798</td><td>1</td><td>0</td><td>21</td><td>3</td></tr><tr><th>MV Boucher</th><td>394</td><td>28.142857142857142</td><td>127.50809061488673</td><td>1</td><td>0</td><td>32</td><td>13</td></tr><tr><th>UT Khawaja</th><td>127</td><td>21.166666666666668</td><td>127.0</td><td>0</td><td>0</td><td>14</td><td>3</td></tr><tr><th>RD Chahar</th><td>3</td><td>0</td><td>150.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>ST Jayasuriya</th><td>768</td><td>27.428571428571427</td><td>144.36090225563908</td><td>4</td><td>1</td><td>84</td><td>39</td></tr><tr><th>Vishnu Vinod</th><td>19</td><td>6.333333333333333</td><td>73.07692307692307</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>R Sathish</th><td>270</td><td>15.882352941176471</td><td>116.88311688311688</td><td>0</td><td>0</td><td>22</td><td>6</td></tr><tr><th>RS Bopara</th><td>531</td><td>29.5</td><td>117.21854304635761</td><td>3</td><td>0</td><td>39</td><td>16</td></tr><tr><th>M Rawat</th><td>55</td><td>11.0</td><td>79.71014492753623</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>L Ablish</th><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>CJ Jordan</th><td>3</td><td>1.5</td><td>60.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Imran Tahir</th><td>18</td><td>4.5</td><td>72.0</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>AA Bilakhia</th><td>69</td><td>13.8</td><td>81.17647058823529</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>R Bhatia</th><td>342</td><td>11.4</td><td>120.4225352112676</td><td>0</td><td>0</td><td>24</td><td>13</td></tr><tr><th>LA Carseldine</th><td>81</td><td>20.25</td><td>119.11764705882352</td><td>0</td><td>0</td><td>11</td><td>0</td></tr><tr><th>UT Yadav</th><td>94</td><td>13.428571428571429</td><td>104.44444444444446</td><td>0</td><td>0</td><td>8</td><td>3</td></tr><tr><th>Sunny Gupta</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RJ Quiney</th><td>103</td><td>14.714285714285714</td><td>100.98039215686273</td><td>1</td><td>0</td><td>12</td><td>3</td></tr><tr><th>RS Gavaskar</th><td>2</td><td>2.0</td><td>25.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DA Warner</th><td>4014</td><td>40.14</td><td>142.1388101983003</td><td>36</td><td>3</td><td>401</td><td>160</td></tr><tr><th>T Henderson</th><td>11</td><td>5.5</td><td>68.75</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>MA Starc</th><td>96</td><td>13.714285714285714</td><td>97.95918367346938</td><td>0</td><td>0</td><td>10</td><td>0</td></tr><tr><th>LJ Wright</th><td>106</td><td>21.2</td><td>176.66666666666666</td><td>0</td><td>0</td><td>16</td><td>3</td></tr><tr><th>RE van der Merwe</th><td>159</td><td>14.454545454545455</td><td>112.7659574468085</td><td>0</td><td>0</td><td>11</td><td>8</td></tr><tr><th>JD Unadkat</th><td>5</td><td>5.0</td><td>38.46153846153847</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DM Bravo</th><td>6</td><td>0</td><td>120.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>MC Juneja</th><td>125</td><td>17.857142857142858</td><td>97.65625</td><td>0</td><td>0</td><td>11</td><td>1</td></tr><tr><th>B Chipli</th><td>280</td><td>18.666666666666668</td><td>111.55378486055776</td><td>1</td><td>0</td><td>28</td><td>7</td></tr><tr><th>Mohammad Asif</th><td>3</td><td>1.5</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>M Ntini</th><td>11</td><td>11.0</td><td>61.111111111111114</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>PP Ojha</th><td>16</td><td>1.3333333333333333</td><td>35.55555555555556</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DR Smith</th><td>2385</td><td>28.392857142857142</td><td>135.20408163265304</td><td>17</td><td>0</td><td>245</td><td>117</td></tr><tr><th>CK Kapugedera</th><td>16</td><td>5.333333333333333</td><td>69.56521739130434</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>CA Lynn</th><td>384</td><td>38.4</td><td>158.02469135802468</td><td>3</td><td>0</td><td>31</td><td>23</td></tr><tr><th>VS Yeligati</th><td>2</td><td>2.0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>KP Appanna</th><td>2</td><td>2.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>C Nanda</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RN ten Doeschate</th><td>326</td><td>23.285714285714285</td><td>138.72340425531914</td><td>1</td><td>0</td><td>26</td><td>15</td></tr><tr><th>VS Malik</th><td>7</td><td>7.0</td><td>100.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>NLTC Perera</th><td>422</td><td>19.181818181818183</td><td>137.45928338762215</td><td>0</td><td>0</td><td>23</td><td>26</td></tr><tr><th>S Anirudha</th><td>136</td><td>17.0</td><td>120.35398230088497</td><td>1</td><td>0</td><td>9</td><td>7</td></tr><tr><th>A Singh</th><td>2</td><td>0.5</td><td>20.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Iqbal Abdulla</th><td>88</td><td>44.0</td><td>104.76190476190477</td><td>0</td><td>0</td><td>9</td><td>1</td></tr><tr><th>SW Billings</th><td>226</td><td>22.6</td><td>134.52380952380955</td><td>2</td><td>0</td><td>23</td><td>5</td></tr><tr><th>A Uniyal</th><td>4</td><td>4.0</td><td>57.14285714285714</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Abdur Razzak</th><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>SA Asnodkar</th><td>423</td><td>21.15</td><td>124.7787610619469</td><td>2</td><td>0</td><td>56</td><td>10</td></tr><tr><th>DJ Bravo</th><td>1262</td><td>22.945454545454545</td><td>126.32632632632632</td><td>4</td><td>0</td><td>102</td><td>48</td></tr><tr><th>PJ Cummins</th><td>77</td><td>12.833333333333334</td><td>140.0</td><td>0</td><td>0</td><td>5</td><td>4</td></tr><tr><th>PJ Sangwan</th><td>24</td><td>4.0</td><td>68.57142857142857</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>AUK Pathan</th><td>39</td><td>7.8</td><td>169.56521739130434</td><td>0</td><td>0</td><td>5</td><td>2</td></tr><tr><th>TL Suman</th><td>676</td><td>21.125</td><td>117.56521739130434</td><td>2</td><td>0</td><td>55</td><td>26</td></tr><tr><th>Anureet Singh</th><td>33</td><td>8.25</td><td>76.74418604651163</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>YV Takawale</th><td>192</td><td>24.0</td><td>107.86516853932584</td><td>0</td><td>0</td><td>26</td><td>3</td></tr><tr><th>CJ Anderson</th><td>521</td><td>27.42105263157895</td><td>129.92518703241896</td><td>3</td><td>0</td><td>40</td><td>30</td></tr><tr><th>IR Jaggi</th><td>76</td><td>15.2</td><td>78.35051546391753</td><td>0</td><td>0</td><td>6</td><td>0</td></tr><tr><th>JJ Roy</th><td>59</td><td>29.5</td><td>147.5</td><td>0</td><td>0</td><td>9</td><td>1</td></tr><tr><th>YK Pathan</th><td>2904</td><td>29.632653061224488</td><td>145.49098196392785</td><td>13</td><td>1</td><td>239</td><td>147</td></tr><tr><th>IK Pathan</th><td>1139</td><td>21.49056603773585</td><td>120.40169133192389</td><td>1</td><td>0</td><td>87</td><td>37</td></tr><tr><th>M Kartik</th><td>113</td><td>16.142857142857142</td><td>104.62962962962963</td><td>0</td><td>0</td><td>7</td><td>1</td></tr><tr><th>GH Vihari</th><td>280</td><td>15.555555555555555</td><td>89.45686900958466</td><td>0</td><td>0</td><td>23</td><td>1</td></tr><tr><th>HM Amla</th><td>577</td><td>44.38461538461539</td><td>141.76904176904176</td><td>3</td><td>2</td><td>60</td><td>21</td></tr><tr><th>M Muralitharan</th><td>20</td><td>3.3333333333333335</td><td>66.66666666666666</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>BJ Rohrer</th><td>193</td><td>27.571428571428573</td><td>137.85714285714286</td><td>1</td><td>0</td><td>21</td><td>5</td></tr><tr><th>Sohail Tanvir</th><td>36</td><td>12.0</td><td>124.13793103448276</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>DW Steyn</th><td>166</td><td>7.545454545454546</td><td>105.0632911392405</td><td>0</td><td>0</td><td>14</td><td>3</td></tr><tr><th>A Zampa</th><td>5</td><td>2.5</td><td>62.5</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>SV Samson</th><td>1426</td><td>25.464285714285715</td><td>124.43280977312392</td><td>7</td><td>1</td><td>112</td><td>57</td></tr><tr><th>MC Henriques</th><td>969</td><td>28.5</td><td>128.1746031746032</td><td>5</td><td>0</td><td>87</td><td>27</td></tr><tr><th>A Choudhary</th><td>25</td><td>25.0</td><td>125.0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>Umar Gul</th><td>39</td><td>13.0</td><td>205.26315789473685</td><td>0</td><td>0</td><td>1</td><td>5</td></tr><tr><th>PSP Handscomb</th><td>6</td><td>6.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Mandeep Singh</th><td>1112</td><td>20.21818181818182</td><td>120.8695652173913</td><td>5</td><td>0</td><td>134</td><td>18</td></tr><tr><th>Shakib Al Hasan</th><td>498</td><td>20.75</td><td>130.36649214659687</td><td>2</td><td>0</td><td>44</td><td>15</td></tr><tr><th>BE Hendricks</th><td>1</td><td>1.0</td><td>33.33333333333333</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DPMD Jayawardene</th><td>1802</td><td>28.15625</td><td>123.25581395348837</td><td>10</td><td>1</td><td>200</td><td>39</td></tr><tr><th>RP Singh</th><td>52</td><td>3.466666666666667</td><td>68.42105263157895</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>Sandeep Sharma</th><td>26</td><td>26.0</td><td>74.28571428571429</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>M Ashwin</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>JA Morkel</th><td>974</td><td>24.35</td><td>141.98250728862973</td><td>3</td><td>0</td><td>61</td><td>55</td></tr><tr><th>CA Pujara</th><td>390</td><td>20.526315789473685</td><td>99.74424552429667</td><td>1</td><td>0</td><td>50</td><td>4</td></tr><tr><th>DJG Sammy</th><td>295</td><td>19.666666666666668</td><td>122.40663900414938</td><td>1</td><td>0</td><td>15</td><td>18</td></tr><tr><th>AC Blizzard</th><td>120</td><td>17.142857142857142</td><td>133.33333333333331</td><td>1</td><td>0</td><td>21</td><td>2</td></tr><tr><th>SB Wagh</th><td>2</td><td>1.0</td><td>66.66666666666666</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>KM Jadhav</th><td>893</td><td>23.5</td><td>134.89425981873111</td><td>3</td><td>0</td><td>72</td><td>33</td></tr><tr><th>Gurkeerat Singh</th><td>342</td><td>16.285714285714285</td><td>125.73529411764706</td><td>1</td><td>0</td><td>36</td><td>9</td></tr><tr><th>BB Samantray</th><td>125</td><td>25.0</td><td>110.61946902654867</td><td>1</td><td>0</td><td>13</td><td>2</td></tr><tr><th>AP Majumdar</th><td>87</td><td>21.75</td><td>114.4736842105263</td><td>0</td><td>0</td><td>7</td><td>2</td></tr><tr><th>AG Murtaza</th><td>10</td><td>10.0</td><td>71.42857142857143</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>DR Martyn</th><td>19</td><td>19.0</td><td>79.16666666666666</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>R Ninan</th><td>3</td><td>3.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Mohammed Shami</th><td>46</td><td>6.571428571428571</td><td>102.22222222222221</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><th>WP Saha</th><td>1557</td><td>25.524590163934427</td><td>130.73047858942067</td><td>6</td><td>1</td><td>130</td><td>57</td></tr><tr><th>AA Noffke</th><td>9</td><td>9.0</td><td>90.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Jaskaran Singh</th><td>8</td><td>8.0</td><td>72.72727272727273</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>LH Ferguson</th><td>1</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DS Kulkarni</th><td>90</td><td>11.25</td><td>93.75</td><td>0</td><td>0</td><td>6</td><td>2</td></tr><tr><th>RA Jadeja</th><td>1732</td><td>24.055555555555557</td><td>122.92405961674946</td><td>0</td><td>0</td><td>125</td><td>57</td></tr><tr><th>KAJ Roach</th><td>10</td><td>10.0</td><td>111.11111111111111</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>GB Hogg</th><td>22</td><td>7.333333333333333</td><td>100.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>DB Ravi Teja</th><td>375</td><td>19.736842105263158</td><td>118.29652996845425</td><td>1</td><td>0</td><td>35</td><td>9</td></tr><tr><th>R Tewatia</th><td>35</td><td>17.5</td><td>145.83333333333331</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>SC Ganguly</th><td>1349</td><td>25.452830188679247</td><td>106.80918448139352</td><td>7</td><td>0</td><td>137</td><td>42</td></tr><tr><th>SJ Srivastava</th><td>5</td><td>5.0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DA Miller</th><td>1563</td><td>34.733333333333334</td><td>141.44796380090497</td><td>8</td><td>1</td><td>104</td><td>78</td></tr><tr><th>RS Sodhi</th><td>4</td><td>0</td><td>200.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>TS Mills</th><td>8</td><td>2.6666666666666665</td><td>72.72727272727273</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>S Ladda</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>MK Pandey</th><td>2215</td><td>28.397435897435898</td><td>120.11930585683297</td><td>9</td><td>1</td><td>197</td><td>64</td></tr><tr><th>TA Boult</th><td>5</td><td>0</td><td>62.5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>SB Styris</th><td>131</td><td>18.714285714285715</td><td>98.49624060150376</td><td>0</td><td>0</td><td>10</td><td>3</td></tr><tr><th>JDS Neesham</th><td>42</td><td>14.0</td><td>91.30434782608695</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>CR Woakes</th><td>46</td><td>11.5</td><td>97.87234042553192</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>JP Faulkner</th><td>527</td><td>20.26923076923077</td><td>135.47557840616966</td><td>0</td><td>0</td><td>36</td><td>23</td></tr><tr><th>AC Thomas</th><td>20</td><td>10.0</td><td>105.26315789473684</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>MA Agarwal</th><td>814</td><td>17.695652173913043</td><td>123.33333333333334</td><td>3</td><td>0</td><td>82</td><td>33</td></tr><tr><th>DT Christian</th><td>420</td><td>18.26086956521739</td><td>123.16715542521995</td><td>0</td><td>0</td><td>22</td><td>18</td></tr><tr><th>Salman Butt</th><td>193</td><td>27.571428571428573</td><td>119.87577639751552</td><td>1</td><td>0</td><td>30</td><td>2</td></tr><tr><th>RR Sarwan</th><td>73</td><td>18.25</td><td>97.33333333333334</td><td>0</td><td>0</td><td>6</td><td>1</td></tr><tr><th>MS Bisla</th><td>798</td><td>21.0</td><td>113.67521367521367</td><td>4</td><td>0</td><td>93</td><td>23</td></tr><tr><th>B Laughlin</th><td>4</td><td>4.0</td><td>44.44444444444444</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DJ Jacobs</th><td>92</td><td>13.142857142857142</td><td>93.87755102040816</td><td>0</td><td>0</td><td>10</td><td>4</td></tr><tr><th>TG Southee</th><td>54</td><td>9.0</td><td>154.2857142857143</td><td>0</td><td>0</td><td>3</td><td>3</td></tr><tr><th>VH Zol</th><td>29</td><td>14.5</td><td>111.53846153846155</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>J Yadav</th><td>6</td><td>3.0</td><td>60.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>X Thalaivan Sargunam</th><td>10</td><td>10.0</td><td>58.82352941176471</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>BCJ Cutting</th><td>124</td><td>24.8</td><td>187.87878787878788</td><td>0</td><td>0</td><td>9</td><td>10</td></tr><tr><th>AB de Villiers</th><td>3473</td><td>38.16483516483517</td><td>148.1655290102389</td><td>22</td><td>3</td><td>287</td><td>156</td></tr><tr><th>SR Watson</th><td>2622</td><td>30.847058823529412</td><td>138.65679534637758</td><td>14</td><td>2</td><td>257</td><td>122</td></tr><tr><th>K Goel</th><td>218</td><td>14.533333333333333</td><td>94.37229437229438</td><td>0</td><td>0</td><td>17</td><td>9</td></tr><tr><th>PR Shah</th><td>92</td><td>11.5</td><td>101.0989010989011</td><td>0</td><td>0</td><td>9</td><td>2</td></tr><tr><th>SP Goswami</th><td>241</td><td>16.066666666666666</td><td>96.01593625498009</td><td>1</td><td>0</td><td>26</td><td>2</td></tr><tr><th>KK Nair</th><td>1158</td><td>25.17391304347826</td><td>126.9736842105263</td><td>8</td><td>0</td><td>135</td><td>26</td></tr><tr><th>T Taibu</th><td>31</td><td>10.333333333333334</td><td>119.23076923076923</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>R Ashwin</th><td>231</td><td>10.5</td><td>96.25</td><td>0</td><td>0</td><td>22</td><td>2</td></tr><tr><th>A Nehra</th><td>41</td><td>5.857142857142857</td><td>66.12903225806451</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>Shivam Sharma</th><td>5</td><td>5.0</td><td>166.66666666666669</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>MP Stoinis</th><td>163</td><td>23.285714285714285</td><td>123.48484848484848</td><td>1</td><td>0</td><td>14</td><td>4</td></tr><tr><th>M de Lange</th><td>1</td><td>1.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RR Powar</th><td>67</td><td>22.333333333333332</td><td>104.6875</td><td>0</td><td>0</td><td>6</td><td>1</td></tr><tr><th>Z Khan</th><td>117</td><td>8.357142857142858</td><td>82.97872340425532</td><td>0</td><td>0</td><td>11</td><td>2</td></tr><tr><th>AJ Finch</th><td>1603</td><td>27.16949152542373</td><td>130.43124491456467</td><td>13</td><td>0</td><td>170</td><td>59</td></tr><tr><th>R Dhawan</th><td>153</td><td>21.857142857142858</td><td>113.33333333333333</td><td>0</td><td>0</td><td>13</td><td>4</td></tr><tr><th>S Kaushik</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>D Salunkhe</th><td>33</td><td>16.5</td><td>137.5</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>SN Khan</th><td>177</td><td>29.5</td><td>173.52941176470588</td><td>0</td><td>0</td><td>19</td><td>6</td></tr><tr><th>AL Menaria</th><td>401</td><td>19.095238095238095</td><td>112.64044943820224</td><td>0</td><td>0</td><td>24</td><td>18</td></tr><tr><th>PA Reddy</th><td>164</td><td>16.4</td><td>102.49999999999999</td><td>0</td><td>0</td><td>15</td><td>2</td></tr><tr><th>KL Rahul</th><td>725</td><td>30.208333333333332</td><td>125.64991334488735</td><td>4</td><td>0</td><td>61</td><td>24</td></tr><tr><th>Sachin Baby</th><td>137</td><td>17.125</td><td>135.64356435643566</td><td>0</td><td>0</td><td>11</td><td>5</td></tr><tr><th>AD Nath</th><td>20</td><td>5.0</td><td>71.42857142857143</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>MS Wade</th><td>22</td><td>7.333333333333333</td><td>66.66666666666666</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>N Saini</th><td>140</td><td>14.0</td><td>99.29078014184397</td><td>1</td><td>0</td><td>16</td><td>0</td></tr><tr><th>CM Gautam</th><td>169</td><td>16.9</td><td>112.66666666666667</td><td>0</td><td>0</td><td>17</td><td>6</td></tr><tr><th>SB Bangar</th><td>49</td><td>7.0</td><td>84.48275862068965</td><td>0</td><td>0</td><td>1</td><td>3</td></tr><tr><th>DJ Hussey</th><td>1322</td><td>26.979591836734695</td><td>122.97674418604652</td><td>5</td><td>0</td><td>90</td><td>60</td></tr><tr><th>Harbhajan Singh</th><td>799</td><td>15.666666666666666</td><td>142.42424242424244</td><td>1</td><td>0</td><td>76</td><td>41</td></tr><tr><th>MD Mishra</th><td>237</td><td>14.8125</td><td>113.9423076923077</td><td>0</td><td>0</td><td>24</td><td>8</td></tr><tr><th>UA Birla</th><td>7</td><td>7.0</td><td>50.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>RJ Harris</th><td>117</td><td>9.75</td><td>104.46428571428572</td><td>0</td><td>0</td><td>6</td><td>3</td></tr><tr><th>B Kumar</th><td>158</td><td>8.31578947368421</td><td>100.63694267515923</td><td>0</td><td>0</td><td>15</td><td>3</td></tr><tr><th>A Flintoff</th><td>62</td><td>31.0</td><td>116.98113207547169</td><td>0</td><td>0</td><td>5</td><td>2</td></tr><tr><th>CH Morris</th><td>439</td><td>36.583333333333336</td><td>165.66037735849056</td><td>2</td><td>0</td><td>33</td><td>23</td></tr><tr><th>MF Maharoof</th><td>177</td><td>17.7</td><td>143.90243902439025</td><td>0</td><td>0</td><td>12</td><td>9</td></tr><tr><th>L Balaji</th><td>36</td><td>4.5</td><td>73.46938775510205</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>SE Bond</th><td>1</td><td>1.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>R Rampaul</th><td>51</td><td>10.2</td><td>102.0</td><td>0</td><td>0</td><td>3</td><td>2</td></tr><tr><th>AA Chavan</th><td>12</td><td>6.0</td><td>109.09090909090908</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>C de Grandhomme</th><td>126</td><td>15.75</td><td>136.95652173913044</td><td>0</td><td>0</td><td>10</td><td>6</td></tr><tr><th>PA Patel</th><td>2322</td><td>21.700934579439252</td><td>117.21352852094901</td><td>10</td><td>0</td><td>297</td><td>35</td></tr><tr><th>Kamran Akmal</th><td>128</td><td>25.6</td><td>164.1025641025641</td><td>1</td><td>0</td><td>13</td><td>8</td></tr><tr><th>S Sriram</th><td>31</td><td>15.5</td><td>86.11111111111111</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>CA Ingram</th><td>21</td><td>10.5</td><td>80.76923076923077</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>JEC Franklin</th><td>327</td><td>29.727272727272727</td><td>108.63787375415282</td><td>1</td><td>0</td><td>25</td><td>9</td></tr><tr><th>S Sohal</th><td>368</td><td>18.4</td><td>126.02739726027397</td><td>2</td><td>0</td><td>34</td><td>18</td></tr><tr><th>U Kaul</th><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AA Jhunjhunwala</th><td>217</td><td>16.692307692307693</td><td>103.33333333333334</td><td>1</td><td>0</td><td>19</td><td>5</td></tr><tr><th>TM Dilshan</th><td>1153</td><td>26.813953488372093</td><td>114.49851042701091</td><td>9</td><td>0</td><td>140</td><td>24</td></tr><tr><th>Yuvraj Singh</th><td>2587</td><td>25.613861386138613</td><td>131.1866125760649</td><td>12</td><td>0</td><td>204</td><td>141</td></tr><tr><th>M Klinger</th><td>73</td><td>18.25</td><td>94.8051948051948</td><td>0</td><td>0</td><td>9</td><td>0</td></tr><tr><th>M Kaif</th><td>259</td><td>14.38888888888889</td><td>103.60000000000001</td><td>0</td><td>0</td><td>22</td><td>6</td></tr><tr><th>JDP Oram</th><td>106</td><td>13.25</td><td>98.14814814814815</td><td>0</td><td>0</td><td>6</td><td>5</td></tr><tr><th>AD Mathews</th><td>724</td><td>23.35483870967742</td><td>125.91304347826086</td><td>1</td><td>0</td><td>44</td><td>29</td></tr><tr><th>SM Pollock</th><td>147</td><td>18.375</td><td>132.43243243243242</td><td>0</td><td>0</td><td>12</td><td>8</td></tr><tr><th>PP Chawla</th><td>515</td><td>12.261904761904763</td><td>113.68653421633555</td><td>0</td><td>0</td><td>49</td><td>15</td></tr><tr><th>S Kaul</th><td>9</td><td>0</td><td>69.23076923076923</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>SB Jakati</th><td>28</td><td>28.0</td><td>96.55172413793103</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>Harpreet Singh</th><td>20</td><td>10.0</td><td>76.92307692307693</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Misbah-ul-Haq</th><td>117</td><td>16.714285714285715</td><td>144.44444444444443</td><td>0</td><td>0</td><td>10</td><td>6</td></tr><tr><th>MR Marsh</th><td>225</td><td>18.75</td><td>114.79591836734696</td><td>0</td><td>0</td><td>9</td><td>14</td></tr><tr><th>Parvez Rasool</th><td>17</td><td>8.5</td><td>85.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>D Kalyankrishna</th><td>3</td><td>3.0</td><td>37.5</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RR Pant</th><td>564</td><td>25.636363636363637</td><td>151.20643431635386</td><td>3</td><td>0</td><td>47</td><td>30</td></tr><tr><th>PD Collingwood</th><td>203</td><td>40.6</td><td>130.12820512820514</td><td>3</td><td>0</td><td>9</td><td>13</td></tr><tr><th>W Jaffer</th><td>130</td><td>16.25</td><td>107.43801652892562</td><td>1</td><td>0</td><td>14</td><td>3</td></tr><tr><th>SW Tait</th><td>23</td><td>7.666666666666667</td><td>88.46153846153845</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>HH Gibbs</th><td>886</td><td>27.6875</td><td>109.78934324659231</td><td>6</td><td>0</td><td>83</td><td>31</td></tr><tr><th>RV Uthappa</th><td>3778</td><td>29.515625</td><td>131.6376306620209</td><td>22</td><td>0</td><td>377</td><td>125</td></tr><tr><th>SA Abbott</th><td>15</td><td>7.5</td><td>115.38461538461537</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>ND Doshi</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AB Agarkar</th><td>179</td><td>17.9</td><td>116.23376623376625</td><td>0</td><td>0</td><td>13</td><td>5</td></tr><tr><th>OA Shah</th><td>506</td><td>33.733333333333334</td><td>130.0771208226221</td><td>4</td><td>0</td><td>34</td><td>23</td></tr><tr><th>DJ Muthuswami</th><td>1</td><td>1.0</td><td>33.33333333333333</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>C Madan</th><td>15</td><td>15.0</td><td>78.94736842105263</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>DJ Thornely</th><td>39</td><td>13.0</td><td>73.58490566037736</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><th>DJ Harris</th><td>111</td><td>27.75</td><td>109.9009900990099</td><td>0</td><td>0</td><td>11</td><td>5</td></tr><tr><th>JC Buttler</th><td>527</td><td>23.954545454545453</td><td>145.98337950138506</td><td>1</td><td>0</td><td>50</td><td>26</td></tr><tr><th>BJ Hodge</th><td>1400</td><td>33.333333333333336</td><td>125.22361359570662</td><td>6</td><td>0</td><td>122</td><td>43</td></tr><tr><th>AT Rayudu</th><td>2416</td><td>27.146067415730336</td><td>126.16187989556136</td><td>14</td><td>0</td><td>205</td><td>79</td></tr><tr><th>JH Kallis</th><td>2427</td><td>28.55294117647059</td><td>109.22592259225922</td><td>17</td><td>0</td><td>255</td><td>44</td></tr><tr><th>AS Yadav</th><td>49</td><td>9.8</td><td>125.64102564102564</td><td>0</td><td>0</td><td>5</td><td>2</td></tr><tr><th>SP Fleming</th><td>196</td><td>21.77777777777778</td><td>118.7878787878788</td><td>0</td><td>0</td><td>27</td><td>3</td></tr><tr><th>AN Ahmed</th><td>36</td><td>36.0</td><td>138.46153846153845</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>V Kohli</th><td>4418</td><td>37.440677966101696</td><td>129.8266235674405</td><td>30</td><td>4</td><td>383</td><td>160</td></tr><tr><th>DH Yagnik</th><td>170</td><td>17.0</td><td>124.08759124087592</td><td>0</td><td>0</td><td>23</td><td>2</td></tr><tr><th>JJ Bumrah</th><td>25</td><td>12.5</td><td>96.15384615384616</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>S Randiv</th><td>2</td><td>2.0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>STR Binny</th><td>766</td><td>20.157894736842106</td><td>126.82119205298012</td><td>0</td><td>0</td><td>59</td><td>29</td></tr><tr><th>KP Pietersen</th><td>1001</td><td>35.75</td><td>134.72409152086138</td><td>4</td><td>1</td><td>91</td><td>40</td></tr><tr><th>UBT Chand</th><td>300</td><td>15.0</td><td>100.0</td><td>1</td><td>0</td><td>32</td><td>9</td></tr><tr><th>SK Warne</th><td>198</td><td>9.9</td><td>92.5233644859813</td><td>0</td><td>0</td><td>14</td><td>6</td></tr><tr><th>MS Dhoni</th><td>3560</td><td>37.87234042553192</td><td>136.7127496159754</td><td>17</td><td>0</td><td>251</td><td>156</td></tr><tr><th>AP Dole</th><td>34</td><td>17.0</td><td>154.54545454545453</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>HH Pandya</th><td>406</td><td>21.36842105263158</td><td>142.4561403508772</td><td>1</td><td>0</td><td>24</td><td>28</td></tr><tr><th>R Bishnoi</th><td>19</td><td>6.333333333333333</td><td>111.76470588235294</td><td>0</td><td>0</td><td>1</td><td>2</td></tr><tr><th>SM Harwood</th><td>9</td><td>0</td><td>64.28571428571429</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>S Badrinath</th><td>1441</td><td>30.659574468085108</td><td>118.8943894389439</td><td>11</td><td>0</td><td>154</td><td>28</td></tr><tr><th>AS Raut</th><td>194</td><td>21.555555555555557</td><td>116.1676646706587</td><td>0</td><td>0</td><td>13</td><td>7</td></tr><tr><th>BA Stokes</th><td>316</td><td>31.6</td><td>142.98642533936652</td><td>1</td><td>1</td><td>22</td><td>15</td></tr><tr><th>TM Head</th><td>205</td><td>29.285714285714285</td><td>138.51351351351352</td><td>1</td><td>0</td><td>12</td><td>8</td></tr><tr><th>Ankit Soni</th><td>7</td><td>3.5</td><td>77.77777777777779</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>R Vinay Kumar</th><td>304</td><td>10.857142857142858</td><td>112.1771217712177</td><td>0</td><td>0</td><td>20</td><td>9</td></tr><tr><th>AP Tare</th><td>339</td><td>14.125</td><td>124.17582417582418</td><td>1</td><td>0</td><td>40</td><td>11</td></tr><tr><th>Younis Khan</th><td>3</td><td>3.0</td><td>42.857142857142854</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AD Mascarenhas</th><td>79</td><td>8.777777777777779</td><td>101.28205128205127</td><td>0</td><td>0</td><td>5</td><td>1</td></tr><tr><th>Y Venugopal Rao</th><td>985</td><td>22.386363636363637</td><td>117.82296650717703</td><td>3</td><td>0</td><td>77</td><td>37</td></tr><tr><th>SMSM Senanayake</th><td>10</td><td>10.0</td><td>58.82352941176471</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>MS Gony</th><td>99</td><td>9.9</td><td>139.43661971830986</td><td>0</td><td>0</td><td>6</td><td>8</td></tr><tr><th>Bipul Sharma</th><td>187</td><td>23.375</td><td>152.03252032520325</td><td>0</td><td>0</td><td>11</td><td>9</td></tr><tr><th>KC Cariappa</th><td>15</td><td>5.0</td><td>83.33333333333334</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>A Mithun</th><td>34</td><td>6.8</td><td>130.76923076923077</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>SA Yadav</th><td>608</td><td>20.96551724137931</td><td>130.752688172043</td><td>1</td><td>0</td><td>54</td><td>21</td></tr><tr><th>Pankaj Singh</th><td>7</td><td>3.5</td><td>58.333333333333336</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>NS Naik</th><td>23</td><td>11.5</td><td>74.19354838709677</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>AB Barath</th><td>42</td><td>21.0</td><td>100.0</td><td>0</td><td>0</td><td>5</td><td>1</td></tr><tr><th>S Nadeem</th><td>26</td><td>2.1666666666666665</td><td>37.142857142857146</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Mohammad Hafeez</th><td>64</td><td>9.142857142857142</td><td>77.10843373493977</td><td>0</td><td>0</td><td>7</td><td>2</td></tr><tr><th>HV Patel</th><td>47</td><td>5.222222222222222</td><td>138.23529411764704</td><td>0</td><td>0</td><td>2</td><td>3</td></tr><tr><th>S Tyagi</th><td>3</td><td>3.0</td><td>75.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>GD McGrath</th><td>4</td><td>4.0</td><td>80.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Harmeet Singh</th><td>18</td><td>4.5</td><td>150.0</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>SP Jackson</th><td>38</td><td>19.0</td><td>122.58064516129032</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>KA Pollard</th><td>2344</td><td>28.585365853658537</td><td>146.59161976235146</td><td>12</td><td>0</td><td>157</td><td>147</td></tr><tr><th>G Gambhir</th><td>4132</td><td>31.541984732824428</td><td>124.60796139927623</td><td>35</td><td>0</td><td>484</td><td>58</td></tr><tr><th>B Lee</th><td>124</td><td>12.4</td><td>127.83505154639174</td><td>0</td><td>0</td><td>8</td><td>8</td></tr><tr><th>Yashpal Singh</th><td>47</td><td>11.75</td><td>71.21212121212122</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>V Shankar</th><td>101</td><td>50.5</td><td>134.66666666666666</td><td>1</td><td>0</td><td>12</td><td>1</td></tr><tr><th>YA Abdulla</th><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>LR Shukla</th><td>405</td><td>15.0</td><td>115.71428571428572</td><td>0</td><td>0</td><td>33</td><td>16</td></tr><tr><th>V Sehwag</th><td>2728</td><td>27.555555555555557</td><td>155.44159544159544</td><td>16</td><td>2</td><td>334</td><td>106</td></tr><tr><th>JR Hopes</th><td>417</td><td>26.0625</td><td>136.27450980392157</td><td>4</td><td>0</td><td>49</td><td>11</td></tr><tr><th>R Sharma</th><td>66</td><td>4.714285714285714</td><td>88.0</td><td>0</td><td>0</td><td>5</td><td>3</td></tr><tr><th>SP Narine</th><td>271</td><td>13.55</td><td>147.2826086956522</td><td>1</td><td>0</td><td>36</td><td>12</td></tr><tr><th>AD Russell</th><td>574</td><td>26.09090909090909</td><td>173.41389728096678</td><td>3</td><td>0</td><td>48</td><td>37</td></tr><tr><th>DB Das</th><td>304</td><td>21.714285714285715</td><td>116.47509578544062</td><td>0</td><td>0</td><td>23</td><td>16</td></tr><tr><th>I Malhotra</th><td>7</td><td>0</td><td>175.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>SS Iyer</th><td>807</td><td>27.82758620689655</td><td>128.50318471337582</td><td>6</td><td>0</td><td>79</td><td>32</td></tr><tr><th>DNT Zoysa</th><td>11</td><td>11.0</td><td>122.22222222222223</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>A Kumble</th><td>35</td><td>11.666666666666666</td><td>74.46808510638297</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>J Theron</th><td>10</td><td>5.0</td><td>83.33333333333334</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Basil Thampi</th><td>28</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>S Gopal</th><td>14</td><td>7.0</td><td>51.85185185185185</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>SS Shaikh</th><td>6</td><td>6.0</td><td>85.71428571428571</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>RG Sharma</th><td>4207</td><td>32.6124031007752</td><td>130.8960796515246</td><td>32</td><td>1</td><td>354</td><td>173</td></tr><tr><th>MK Tiwary</th><td>1648</td><td>29.428571428571427</td><td>117.29537366548041</td><td>7</td><td>0</td><td>152</td><td>39</td></tr><tr><th>Y Nagar</th><td>285</td><td>20.357142857142858</td><td>110.03861003861005</td><td>0</td><td>0</td><td>20</td><td>9</td></tr><tr><th>S Badree</th><td>13</td><td>2.6</td><td>56.52173913043478</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Ishan Kishan</th><td>319</td><td>21.266666666666666</td><td>129.1497975708502</td><td>1</td><td>0</td><td>34</td><td>13</td></tr><tr><th>DT Patil</th><td>13</td><td>6.5</td><td>100.0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>MN Samuels</th><td>161</td><td>12.384615384615385</td><td>93.6046511627907</td><td>0</td><td>0</td><td>9</td><td>7</td></tr><tr><th>DL Vettori</th><td>121</td><td>15.125</td><td>107.07964601769913</td><td>0</td><td>0</td><td>11</td><td>2</td></tr><tr><th>JE Taylor</th><td>3</td><td>3.0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RG More</th><td>2</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DP Vijaykumar</th><td>1</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>P Negi</th><td>353</td><td>17.65</td><td>129.77941176470588</td><td>0</td><td>0</td><td>26</td><td>16</td></tr><tr><th>LMP Simmons</th><td>1079</td><td>39.96296296296296</td><td>126.6431924882629</td><td>11</td><td>1</td><td>109</td><td>44</td></tr><tr><th>N Rana</th><td>437</td><td>31.214285714285715</td><td>128.90855457227138</td><td>4</td><td>0</td><td>31</td><td>25</td></tr><tr><th>Ankit Sharma</th><td>87</td><td>12.428571428571429</td><td>129.8507462686567</td><td>0</td><td>0</td><td>7</td><td>4</td></tr><tr><th>B Akhil</th><td>76</td><td>12.666666666666666</td><td>138.1818181818182</td><td>0</td><td>0</td><td>5</td><td>5</td></tr><tr><th>GJ Maxwell</th><td>1228</td><td>25.06122448979592</td><td>164.3908969210174</td><td>6</td><td>0</td><td>96</td><td>82</td></tr><tr><th>R Shukla</th><td>19</td><td>19.0</td><td>82.6086956521739</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>CL White</th><td>954</td><td>26.5</td><td>126.35761589403972</td><td>6</td><td>0</td><td>76</td><td>36</td></tr><tr><th>KD Karthik</th><td>2903</td><td>24.811965811965813</td><td>125.94360086767895</td><td>14</td><td>0</td><td>286</td><td>71</td></tr><tr><th>M Manhas</th><td>514</td><td>22.347826086956523</td><td>109.36170212765957</td><td>0</td><td>0</td><td>43</td><td>10</td></tr><tr><th>R McLaren</th><td>159</td><td>19.875</td><td>92.98245614035088</td><td>1</td><td>0</td><td>14</td><td>1</td></tr><tr><th>V Pratap Singh</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Niraj Patel</th><td>121</td><td>30.25</td><td>101.68067226890756</td><td>1</td><td>0</td><td>14</td><td>1</td></tr><tr><th>Shoaib Ahmed</th><td>1</td><td>0.5</td><td>16.666666666666664</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>IC Pandey</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>KS Williamson</th><td>411</td><td>31.615384615384617</td><td>129.24528301886792</td><td>3</td><td>0</td><td>35</td><td>11</td></tr><tr><th>A Mishra</th><td>340</td><td>11.724137931034482</td><td>93.66391184573003</td><td>0</td><td>0</td><td>27</td><td>5</td></tr><tr><th>TD Paine</th><td>10</td><td>5.0</td><td>38.46153846153847</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>SE Marsh</th><td>2477</td><td>39.317460317460316</td><td>132.7438370846731</td><td>20</td><td>1</td><td>266</td><td>78</td></tr><tr><th>AB Dinda</th><td>26</td><td>2.6</td><td>54.166666666666664</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>LA Pomersbach</th><td>302</td><td>27.454545454545453</td><td>122.76422764227641</td><td>1</td><td>0</td><td>25</td><td>13</td></tr><tr><th>GJ Bailey</th><td>663</td><td>24.555555555555557</td><td>121.875</td><td>2</td><td>0</td><td>59</td><td>19</td></tr><tr><th>J Syed Mohammad</th><td>29</td><td>14.5</td><td>111.53846153846155</td><td>0</td><td>0</td><td>4</td><td>0</td></tr><tr><th>NM Coulter-Nile</th><td>52</td><td>6.5</td><td>104.0</td><td>0</td><td>0</td><td>3</td><td>4</td></tr><tr><th>FY Fazal</th><td>183</td><td>18.3</td><td>105.78034682080926</td><td>0</td><td>0</td><td>22</td><td>1</td></tr><tr><th>CRD Fernando</th><td>4</td><td>0</td><td>133.33333333333331</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>ER Dwivedi</th><td>24</td><td>12.0</td><td>171.42857142857142</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><th>SD Chitnis</th><td>99</td><td>14.142857142857142</td><td>111.23595505617978</td><td>0</td><td>0</td><td>10</td><td>2</td></tr><tr><th>J Suchith</th><td>48</td><td>48.0</td><td>150.0</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><th>FH Edwards</th><td>4</td><td>4.0</td><td>80.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Kuldeep Yadav</th><td>20</td><td>10.0</td><td>90.9090909090909</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>K Upadhyay</th><td>12</td><td>0</td><td>133.33333333333331</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>JD Ryder</th><td>604</td><td>21.571428571428573</td><td>131.87772925764193</td><td>4</td><td>0</td><td>69</td><td>19</td></tr><tr><th>AN Ghosh</th><td>7</td><td>3.5</td><td>87.5</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>PM Sarvesh Kumar</th><td>1</td><td>0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AJ Tye</th><td>53</td><td>26.5</td><td>176.66666666666666</td><td>0</td><td>0</td><td>4</td><td>3</td></tr><tr><th>NL McCullum</th><td>26</td><td>26.0</td><td>118.18181818181819</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>PV Tambe</th><td>18</td><td>9.0</td><td>46.15384615384615</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>P Kumar</th><td>340</td><td>8.947368421052632</td><td>108.28025477707006</td><td>0</td><td>0</td><td>22</td><td>17</td></tr><tr><th>RR Rossouw</th><td>53</td><td>10.6</td><td>103.921568627451</td><td>0</td><td>0</td><td>3</td><td>3</td></tr><tr><th>C Munro</th><td>30</td><td>10.0</td><td>100.0</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>SB Joshi</th><td>6</td><td>3.0</td><td>42.857142857142854</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>LPC Silva</th><td>40</td><td>20.0</td><td>153.84615384615387</td><td>0</td><td>0</td><td>5</td><td>1</td></tr><tr><th>AB McDonald</th><td>123</td><td>24.6</td><td>123.0</td><td>0</td><td>0</td><td>9</td><td>4</td></tr><tr><th>KJ Abbott</th><td>13</td><td>13.0</td><td>162.5</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>J Arunkumar</th><td>23</td><td>7.666666666666667</td><td>100.0</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>S Aravind</th><td>59</td><td>14.75</td><td>103.50877192982458</td><td>0</td><td>0</td><td>7</td><td>0</td></tr><tr><th>BR Dunk</th><td>40</td><td>13.333333333333334</td><td>114.28571428571428</td><td>0</td><td>0</td><td>7</td><td>0</td></tr><tr><th>J Botha</th><td>409</td><td>20.45</td><td>113.9275766016713</td><td>1</td><td>0</td><td>39</td><td>5</td></tr><tr><th>RR Bhatkal</th><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RT Ponting</th><td>91</td><td>10.11111111111111</td><td>71.09375</td><td>0</td><td>0</td><td>5</td><td>2</td></tr><tr><th>D Wiese</th><td>127</td><td>31.75</td><td>141.11111111111111</td><td>0</td><td>0</td><td>12</td><td>4</td></tr><tr><th>BJ Haddin</th><td>18</td><td>18.0</td><td>163.63636363636365</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>NJ Rimmington</th><td>1</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AF Milne</th><td>7</td><td>3.5</td><td>58.333333333333336</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>M Vijay</th><td>2511</td><td>26.431578947368422</td><td>123.39066339066338</td><td>13</td><td>2</td><td>237</td><td>89</td></tr><tr><th>A Chopra</th><td>53</td><td>8.833333333333334</td><td>74.64788732394366</td><td>0</td><td>0</td><td>7</td><td>0</td></tr><tr><th>Mashrafe Mortaza</th><td>2</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>BB Sran</th><td>4</td><td>0</td><td>100.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RJ Peterson</th><td>32</td><td>10.666666666666666</td><td>106.66666666666667</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>KV Sharma</th><td>317</td><td>15.095238095238095</td><td>115.69343065693431</td><td>0</td><td>0</td><td>18</td><td>14</td></tr><tr><th>JJ van der Wath</th><td>18</td><td>9.0</td><td>112.5</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>Karanveer Singh</th><td>12</td><td>12.0</td><td>70.58823529411765</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>KW Richardson</th><td>36</td><td>12.0</td><td>92.3076923076923</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>D du Preez</th><td>10</td><td>10.0</td><td>76.92307692307693</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>CK Langeveldt</th><td>8</td><td>4.0</td><td>88.88888888888889</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>SS Tiwary</th><td>1276</td><td>28.355555555555554</td><td>119.58762886597938</td><td>7</td><td>0</td><td>93</td><td>45</td></tr><tr><th>DJ Hooda</th><td>373</td><td>14.346153846153847</td><td>139.17910447761196</td><td>1</td><td>0</td><td>20</td><td>20</td></tr><tr><th>WD Parnell</th><td>63</td><td>7.0</td><td>84.0</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>DAJ Bracewell</th><td>12</td><td>0</td><td>133.33333333333331</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>GC Smith</th><td>739</td><td>28.423076923076923</td><td>110.62874251497006</td><td>4</td><td>0</td><td>94</td><td>9</td></tr><tr><th>Shoaib Akhtar</th><td>2</td><td>2.0</td><td>28.57142857142857</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>KMDN Kulasekara</th><td>5</td><td>0</td><td>166.66666666666669</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>PC Valthaty</th><td>505</td><td>22.954545454545453</td><td>120.8133971291866</td><td>2</td><td>1</td><td>61</td><td>20</td></tr><tr><th>S Narwal</th><td>37</td><td>12.333333333333334</td><td>137.03703703703704</td><td>0</td><td>0</td><td>6</td><td>0</td></tr><tr><th>CR Brathwaite</th><td>95</td><td>11.875</td><td>179.24528301886792</td><td>0</td><td>0</td><td>8</td><td>8</td></tr><tr><th>LRPL Taylor</th><td>1017</td><td>25.425</td><td>123.72262773722629</td><td>3</td><td>0</td><td>66</td><td>46</td></tr><tr><th>GR Napier</th><td>15</td><td>15.0</td><td>93.75</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>RR Raje</th><td>20</td><td>20.0</td><td>111.11111111111111</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>AC Gilchrist</th><td>2069</td><td>27.223684210526315</td><td>138.39464882943145</td><td>11</td><td>2</td><td>239</td><td>92</td></tr><tr><th>S Dhawan</th><td>3561</td><td>32.669724770642205</td><td>121.86858316221767</td><td>28</td><td>0</td><td>401</td><td>71</td></tr><tr><th>MM Sharma</th><td>112</td><td>8.0</td><td>109.80392156862746</td><td>0</td><td>0</td><td>8</td><td>4</td></tr><tr><th>P Sahu</th><td>19</td><td>19.0</td><td>146.15384615384613</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>Sunny Singh</th><td>43</td><td>10.75</td><td>138.70967741935485</td><td>0</td><td>0</td><td>6</td><td>1</td></tr><tr><th>RA Tripathi</th><td>391</td><td>27.928571428571427</td><td>146.44194756554307</td><td>2</td><td>0</td><td>43</td><td>17</td></tr><tr><th>Shahid Afridi</th><td>81</td><td>10.125</td><td>176.08695652173913</td><td>0</td><td>0</td><td>7</td><td>6</td></tr><tr><th>R Dravid</th><td>2174</td><td>28.233766233766232</td><td>115.5154091392136</td><td>11</td><td>0</td><td>269</td><td>28</td></tr><tr><th>A Ashish Reddy</th><td>280</td><td>18.666666666666668</td><td>145.0777202072539</td><td>0</td><td>0</td><td>16</td><td>15</td></tr><tr><th>P Dogra</th><td>127</td><td>11.545454545454545</td><td>92.02898550724638</td><td>0</td><td>0</td><td>4</td><td>5</td></tr><tr><th>AG Paunikar</th><td>49</td><td>9.8</td><td>84.48275862068965</td><td>0</td><td>0</td><td>9</td><td>0</td></tr><tr><th>VR Aaron</th><td>46</td><td>11.5</td><td>83.63636363636363</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><th>DE Bollinger</th><td>21</td><td>21.0</td><td>91.30434782608695</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><th>BMAJ Mendis</th><td>23</td><td>7.666666666666667</td><td>85.18518518518519</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>AM Nayar</th><td>672</td><td>17.68421052631579</td><td>116.46447140381282</td><td>0</td><td>0</td><td>55</td><td>20</td></tr><tr><th>SPD Smith</th><td>1703</td><td>37.02173913043478</td><td>131.7092034029389</td><td>5</td><td>1</td><td>150</td><td>45</td></tr><tr><th>VVS Laxman</th><td>282</td><td>14.842105263157896</td><td>105.61797752808988</td><td>1</td><td>0</td><td>33</td><td>5</td></tr><tr><th>CJ McKay</th><td>8</td><td>8.0</td><td>53.333333333333336</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Anirudh Singh</th><td>63</td><td>15.75</td><td>95.45454545454545</td><td>0</td><td>0</td><td>6</td><td>1</td></tr><tr><th>S Sreesanth</th><td>34</td><td>8.5</td><td>61.81818181818181</td><td>0</td><td>0</td><td>6</td><td>0</td></tr><tr><th>TR Birt</th><td>75</td><td>15.0</td><td>129.31034482758622</td><td>0</td><td>0</td><td>9</td><td>2</td></tr><tr><th>P Parameswaran</th><td>1</td><td>0</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>MG Johnson</th><td>151</td><td>11.615384615384615</td><td>98.69281045751634</td><td>0</td><td>0</td><td>8</td><td>7</td></tr><tr><th>B Sumanth</th><td>35</td><td>35.0</td><td>94.5945945945946</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>KK Cooper</th><td>116</td><td>12.88888888888889</td><td>170.58823529411765</td><td>0</td><td>0</td><td>9</td><td>8</td></tr><tr><th>MDKJ Perera</th><td>14</td><td>7.0</td><td>107.6923076923077</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>I Sharma</th><td>45</td><td>7.5</td><td>77.58620689655173</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>Shoaib Malik</th><td>52</td><td>13.0</td><td>110.63829787234043</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>Y Gnaneswara Rao</th><td>19</td><td>19.0</td><td>111.76470588235294</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>Kamran Khan</th><td>3</td><td>3.0</td><td>60.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>RV Gomez</th><td>50</td><td>10.0</td><td>98.0392156862745</td><td>0</td><td>0</td><td>5</td><td>1</td></tr><tr><th>NJ Maddinson</th><td>20</td><td>6.666666666666667</td><td>95.23809523809523</td><td>0</td><td>0</td><td>4</td><td>0</td></tr><tr><th>Swapnil Singh</th><td>12</td><td>4.0</td><td>60.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>WPUJC Vaas</th><td>81</td><td>10.125</td><td>110.95890410958904</td><td>0</td><td>0</td><td>2</td><td>3</td></tr><tr><th>A Mukund</th><td>19</td><td>9.5</td><td>86.36363636363636</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>SK Trivedi</th><td>42</td><td>6.0</td><td>71.1864406779661</td><td>0</td><td>0</td><td>3</td><td>1</td></tr><tr><th>A Chandila</th><td>4</td><td>0</td><td>57.14285714285714</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Azhar Mahmood</th><td>388</td><td>20.42105263157895</td><td>128.05280528052805</td><td>2</td><td>0</td><td>39</td><td>13</td></tr><tr><th>Joginder Sharma</th><td>36</td><td>9.0</td><td>120.0</td><td>0</td><td>0</td><td>1</td><td>2</td></tr><tr><th>F Behardien</th><td>14</td><td>7.0</td><td>107.6923076923077</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><th>Q de Kock</th><td>726</td><td>29.04</td><td>132.24043715846994</td><td>5</td><td>1</td><td>90</td><td>21</td></tr><tr><th>KH Pandya</th><td>480</td><td>34.285714285714285</td><td>158.41584158415841</td><td>1</td><td>0</td><td>46</td><td>20</td></tr><tr><th>KB Arun Karthik</th><td>51</td><td>10.2</td><td>100.0</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><th>S Chanderpaul</th><td>25</td><td>8.333333333333334</td><td>80.64516129032258</td><td>0</td><td>0</td><td>4</td><td>0</td></tr><tr><th>MM Patel</th><td>39</td><td>6.5</td><td>95.1219512195122</td><td>0</td><td>0</td><td>5</td><td>0</td></tr><tr><th>P Awana</th><td>5</td><td>2.5</td><td>35.714285714285715</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>DS Lehmann</th><td>18</td><td>9.0</td><td>100.0</td><td>0</td><td>0</td><td>3</td><td>0</td></tr><tr><th>AM Rahane</th><td>3057</td><td>33.59340659340659</td><td>120.59171597633136</td><td>25</td><td>1</td><td>320</td><td>60</td></tr><tr><th>MJ Guptill</th><td>189</td><td>21.0</td><td>132.16783216783216</td><td>1</td><td>0</td><td>20</td><td>8</td></tr><tr><th>MJ McClenaghan</th><td>63</td><td>7.875</td><td>143.1818181818182</td><td>0</td><td>0</td><td>2</td><td>6</td></tr><tr><th>M Morkel</th><td>126</td><td>11.454545454545455</td><td>140.0</td><td>0</td><td>0</td><td>11</td><td>5</td></tr><tr><th>SN Thakur</th><td>7</td><td>2.3333333333333335</td><td>77.77777777777779</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>TM Srivastava</th><td>8</td><td>8.0</td><td>72.72727272727273</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>M Vohra</th><td>957</td><td>24.53846153846154</td><td>132.73231622746187</td><td>3</td><td>0</td><td>95</td><td>35</td></tr><tr><th>BAW Mendis</th><td>3</td><td>1.5</td><td>50.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>T Kohli</th><td>11</td><td>3.6666666666666665</td><td>57.89473684210527</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th>RE Levi</th><td>83</td><td>13.833333333333334</td><td>113.69863013698631</td><td>1</td><td>0</td><td>10</td><td>4</td></tr><tr><th>EJG Morgan</th><td>854</td><td>21.35</td><td>121.13475177304964</td><td>4</td><td>0</td><td>72</td><td>34</td></tr><tr><th>Mohammad Ashraful</th><td>2</td><td>2.0</td><td>20.0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>K Rabada</th><td>55</td><td>18.333333333333332</td><td>85.9375</td><td>0</td><td>0</td><td>5</td><td>1</td></tr><tr><th>MA Khote</th><td>24</td><td>12.0</td><td>109.09090909090908</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><th>BB McCullum</th><td>2753</td><td>28.091836734693878</td><td>131.22020972354625</td><td>13</td><td>2</td><td>277</td><td>124</td></tr><tr><th>SK Raina</th><td>4540</td><td>33.88059701492537</td><td>139.09313725490196</td><td>31</td><td>1</td><td>402</td><td>173</td></tr></table>"
      ],
      "text/plain": [
       "[['Name',\n",
       "  'Runs',\n",
       "  'Average',\n",
       "  'Strike rate',\n",
       "  'Fifty',\n",
       "  'Century',\n",
       "  'Fours',\n",
       "  'Sixes'],\n",
       " ['JP Duminy', 1993, 39.86, 124.87468671679198, 14, 0, 123, 78],\n",
       " ['ML Hayden', 1107, 35.70967741935484, 137.51552795031057, 8, 0, 121, 44],\n",
       " ['AS Rajpoot', 6, 6.0, 46.15384615384615, 0, 0, 1, 0],\n",
       " ['Rashid Khan', 11, 5.5, 122.22222222222223, 0, 0, 0, 1],\n",
       " ['CJ Ferguson', 98, 16.333333333333332, 83.76068376068376, 0, 0, 9, 0],\n",
       " ['MJ Clarke', 98, 16.333333333333332, 104.25531914893618, 0, 0, 12, 0],\n",
       " ['VY Mahesh', 15, 7.5, 55.55555555555556, 0, 0, 0, 0],\n",
       " ['BA Bhatt', 6, 6.0, 85.71428571428571, 0, 0, 0, 1],\n",
       " ['KC Sangakkara',\n",
       "  1687,\n",
       "  25.953846153846154,\n",
       "  121.19252873563218,\n",
       "  10,\n",
       "  0,\n",
       "  195,\n",
       "  27],\n",
       " ['DP Nannes', 4, 4.0, 30.76923076923077, 0, 0, 0, 0],\n",
       " ['CH Gayle', 3626, 40.741573033707866, 151.209341117598, 21, 5, 294, 265],\n",
       " ['A Symonds', 974, 36.074074074074076, 129.86666666666667, 5, 1, 74, 41],\n",
       " ['MN van Wyk', 167, 55.666666666666664, 126.51515151515152, 1, 0, 19, 1],\n",
       " ['YS Chahal', 15, 5.0, 53.57142857142857, 0, 0, 0, 0],\n",
       " ['H Das', 2, 2.0, 40.0, 0, 0, 0, 0],\n",
       " ['SL Malinga', 88, 5.5, 88.88888888888889, 0, 0, 6, 5],\n",
       " ['VRV Singh', 4, 2.0, 133.33333333333331, 0, 0, 1, 0],\n",
       " ['L Ronchi', 34, 6.8, 100.0, 0, 0, 6, 1],\n",
       " ['F du Plessis', 1295, 30.11627906976744, 127.96442687747036, 8, 0, 110, 38],\n",
       " ['S Rana', 91, 22.75, 112.34567901234568, 0, 0, 9, 1],\n",
       " ['DL Chahar', 14, 14.0, 233.33333333333334, 0, 0, 0, 2],\n",
       " ['AR Patel', 606, 20.2, 128.1183932346723, 0, 0, 34, 28],\n",
       " ['AR Bawne', 12, 0, 100.0, 0, 0, 1, 0],\n",
       " ['JM Kemp', 26, 13.0, 108.33333333333333, 0, 0, 1, 1],\n",
       " ['MJ Lumb', 278, 23.166666666666668, 143.29896907216494, 1, 0, 45, 6],\n",
       " ['Washington Sundar', 9, 9.0, 75.0, 0, 0, 0, 0],\n",
       " ['MEK Hussey', 1977, 38.76470588235294, 122.64267990074443, 15, 1, 198, 52],\n",
       " ['SM Katich', 241, 24.1, 129.56989247311827, 2, 0, 26, 8],\n",
       " ['SR Tendulkar', 2334, 33.82608695652174, 119.81519507186857, 13, 1, 296, 29],\n",
       " ['AC Voges', 181, 45.25, 126.57342657342659, 0, 0, 15, 3],\n",
       " ['WA Mota', 56, 9.333333333333334, 74.66666666666667, 0, 0, 2, 0],\n",
       " ['Mohammad Nabi', 2, 2.0, 66.66666666666666, 0, 0, 0, 0],\n",
       " ['JO Holder', 38, 7.6, 122.58064516129032, 0, 0, 4, 2],\n",
       " ['NV Ojha', 1553, 20.986486486486488, 118.6401833460657, 6, 0, 121, 79],\n",
       " ['T Thushara', 12, 6.0, 66.66666666666666, 0, 0, 0, 0],\n",
       " ['S Vidyut', 145, 18.125, 133.0275229357798, 1, 0, 21, 3],\n",
       " ['MV Boucher', 394, 28.142857142857142, 127.50809061488673, 1, 0, 32, 13],\n",
       " ['UT Khawaja', 127, 21.166666666666668, 127.0, 0, 0, 14, 3],\n",
       " ['RD Chahar', 3, 0, 150.0, 0, 0, 0, 0],\n",
       " ['ST Jayasuriya', 768, 27.428571428571427, 144.36090225563908, 4, 1, 84, 39],\n",
       " ['Vishnu Vinod', 19, 6.333333333333333, 73.07692307692307, 0, 0, 1, 1],\n",
       " ['R Sathish', 270, 15.882352941176471, 116.88311688311688, 0, 0, 22, 6],\n",
       " ['RS Bopara', 531, 29.5, 117.21854304635761, 3, 0, 39, 16],\n",
       " ['M Rawat', 55, 11.0, 79.71014492753623, 0, 0, 4, 1],\n",
       " ['L Ablish', 0, 0, 0.0, 0, 0, 0, 0],\n",
       " ['CJ Jordan', 3, 1.5, 60.0, 0, 0, 0, 0],\n",
       " ['Imran Tahir', 18, 4.5, 72.0, 0, 0, 3, 0],\n",
       " ['AA Bilakhia', 69, 13.8, 81.17647058823529, 0, 0, 5, 0],\n",
       " ['R Bhatia', 342, 11.4, 120.4225352112676, 0, 0, 24, 13],\n",
       " ['LA Carseldine', 81, 20.25, 119.11764705882352, 0, 0, 11, 0],\n",
       " ['UT Yadav', 94, 13.428571428571429, 104.44444444444446, 0, 0, 8, 3],\n",
       " ['Sunny Gupta', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['RJ Quiney', 103, 14.714285714285714, 100.98039215686273, 1, 0, 12, 3],\n",
       " ['RS Gavaskar', 2, 2.0, 25.0, 0, 0, 0, 0],\n",
       " ['DA Warner', 4014, 40.14, 142.1388101983003, 36, 3, 401, 160],\n",
       " ['T Henderson', 11, 5.5, 68.75, 0, 0, 0, 1],\n",
       " ['MA Starc', 96, 13.714285714285714, 97.95918367346938, 0, 0, 10, 0],\n",
       " ['LJ Wright', 106, 21.2, 176.66666666666666, 0, 0, 16, 3],\n",
       " ['RE van der Merwe', 159, 14.454545454545455, 112.7659574468085, 0, 0, 11, 8],\n",
       " ['JD Unadkat', 5, 5.0, 38.46153846153847, 0, 0, 0, 0],\n",
       " ['DM Bravo', 6, 0, 120.0, 0, 0, 1, 0],\n",
       " ['MC Juneja', 125, 17.857142857142858, 97.65625, 0, 0, 11, 1],\n",
       " ['B Chipli', 280, 18.666666666666668, 111.55378486055776, 1, 0, 28, 7],\n",
       " ['Mohammad Asif', 3, 1.5, 50.0, 0, 0, 0, 0],\n",
       " ['M Ntini', 11, 11.0, 61.111111111111114, 0, 0, 2, 0],\n",
       " ['PP Ojha', 16, 1.3333333333333333, 35.55555555555556, 0, 0, 0, 0],\n",
       " ['DR Smith', 2385, 28.392857142857142, 135.20408163265304, 17, 0, 245, 117],\n",
       " ['CK Kapugedera', 16, 5.333333333333333, 69.56521739130434, 0, 0, 0, 0],\n",
       " ['CA Lynn', 384, 38.4, 158.02469135802468, 3, 0, 31, 23],\n",
       " ['VS Yeligati', 2, 2.0, 100.0, 0, 0, 0, 0],\n",
       " ['KP Appanna', 2, 2.0, 50.0, 0, 0, 0, 0],\n",
       " ['C Nanda', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['RN ten Doeschate',\n",
       "  326,\n",
       "  23.285714285714285,\n",
       "  138.72340425531914,\n",
       "  1,\n",
       "  0,\n",
       "  26,\n",
       "  15],\n",
       " ['VS Malik', 7, 7.0, 100.0, 0, 0, 1, 0],\n",
       " ['NLTC Perera', 422, 19.181818181818183, 137.45928338762215, 0, 0, 23, 26],\n",
       " ['S Anirudha', 136, 17.0, 120.35398230088497, 1, 0, 9, 7],\n",
       " ['A Singh', 2, 0.5, 20.0, 0, 0, 0, 0],\n",
       " ['Iqbal Abdulla', 88, 44.0, 104.76190476190477, 0, 0, 9, 1],\n",
       " ['SW Billings', 226, 22.6, 134.52380952380955, 2, 0, 23, 5],\n",
       " ['A Uniyal', 4, 4.0, 57.14285714285714, 0, 0, 0, 0],\n",
       " ['Abdur Razzak', 0, 0, 0.0, 0, 0, 0, 0],\n",
       " ['SA Asnodkar', 423, 21.15, 124.7787610619469, 2, 0, 56, 10],\n",
       " ['DJ Bravo', 1262, 22.945454545454545, 126.32632632632632, 4, 0, 102, 48],\n",
       " ['PJ Cummins', 77, 12.833333333333334, 140.0, 0, 0, 5, 4],\n",
       " ['PJ Sangwan', 24, 4.0, 68.57142857142857, 0, 0, 1, 0],\n",
       " ['AUK Pathan', 39, 7.8, 169.56521739130434, 0, 0, 5, 2],\n",
       " ['TL Suman', 676, 21.125, 117.56521739130434, 2, 0, 55, 26],\n",
       " ['Anureet Singh', 33, 8.25, 76.74418604651163, 0, 0, 2, 1],\n",
       " ['YV Takawale', 192, 24.0, 107.86516853932584, 0, 0, 26, 3],\n",
       " ['CJ Anderson', 521, 27.42105263157895, 129.92518703241896, 3, 0, 40, 30],\n",
       " ['IR Jaggi', 76, 15.2, 78.35051546391753, 0, 0, 6, 0],\n",
       " ['JJ Roy', 59, 29.5, 147.5, 0, 0, 9, 1],\n",
       " ['YK Pathan', 2904, 29.632653061224488, 145.49098196392785, 13, 1, 239, 147],\n",
       " ['IK Pathan', 1139, 21.49056603773585, 120.40169133192389, 1, 0, 87, 37],\n",
       " ['M Kartik', 113, 16.142857142857142, 104.62962962962963, 0, 0, 7, 1],\n",
       " ['GH Vihari', 280, 15.555555555555555, 89.45686900958466, 0, 0, 23, 1],\n",
       " ['HM Amla', 577, 44.38461538461539, 141.76904176904176, 3, 2, 60, 21],\n",
       " ['M Muralitharan', 20, 3.3333333333333335, 66.66666666666666, 0, 0, 1, 0],\n",
       " ['BJ Rohrer', 193, 27.571428571428573, 137.85714285714286, 1, 0, 21, 5],\n",
       " ['Sohail Tanvir', 36, 12.0, 124.13793103448276, 0, 0, 3, 1],\n",
       " ['DW Steyn', 166, 7.545454545454546, 105.0632911392405, 0, 0, 14, 3],\n",
       " ['A Zampa', 5, 2.5, 62.5, 0, 0, 1, 0],\n",
       " ['SV Samson', 1426, 25.464285714285715, 124.43280977312392, 7, 1, 112, 57],\n",
       " ['MC Henriques', 969, 28.5, 128.1746031746032, 5, 0, 87, 27],\n",
       " ['A Choudhary', 25, 25.0, 125.0, 0, 0, 1, 1],\n",
       " ['Umar Gul', 39, 13.0, 205.26315789473685, 0, 0, 1, 5],\n",
       " ['PSP Handscomb', 6, 6.0, 50.0, 0, 0, 0, 0],\n",
       " ['Mandeep Singh', 1112, 20.21818181818182, 120.8695652173913, 5, 0, 134, 18],\n",
       " ['Shakib Al Hasan', 498, 20.75, 130.36649214659687, 2, 0, 44, 15],\n",
       " ['BE Hendricks', 1, 1.0, 33.33333333333333, 0, 0, 0, 0],\n",
       " ['DPMD Jayawardene', 1802, 28.15625, 123.25581395348837, 10, 1, 200, 39],\n",
       " ['RP Singh', 52, 3.466666666666667, 68.42105263157895, 0, 0, 2, 1],\n",
       " ['Sandeep Sharma', 26, 26.0, 74.28571428571429, 0, 0, 1, 0],\n",
       " ['M Ashwin', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['JA Morkel', 974, 24.35, 141.98250728862973, 3, 0, 61, 55],\n",
       " ['CA Pujara', 390, 20.526315789473685, 99.74424552429667, 1, 0, 50, 4],\n",
       " ['DJG Sammy', 295, 19.666666666666668, 122.40663900414938, 1, 0, 15, 18],\n",
       " ['AC Blizzard', 120, 17.142857142857142, 133.33333333333331, 1, 0, 21, 2],\n",
       " ['SB Wagh', 2, 1.0, 66.66666666666666, 0, 0, 0, 0],\n",
       " ['KM Jadhav', 893, 23.5, 134.89425981873111, 3, 0, 72, 33],\n",
       " ['Gurkeerat Singh', 342, 16.285714285714285, 125.73529411764706, 1, 0, 36, 9],\n",
       " ['BB Samantray', 125, 25.0, 110.61946902654867, 1, 0, 13, 2],\n",
       " ['AP Majumdar', 87, 21.75, 114.4736842105263, 0, 0, 7, 2],\n",
       " ['AG Murtaza', 10, 10.0, 71.42857142857143, 0, 0, 1, 0],\n",
       " ['DR Martyn', 19, 19.0, 79.16666666666666, 0, 0, 1, 0],\n",
       " ['R Ninan', 3, 3.0, 50.0, 0, 0, 0, 0],\n",
       " ['Mohammed Shami', 46, 6.571428571428571, 102.22222222222221, 0, 0, 4, 2],\n",
       " ['WP Saha', 1557, 25.524590163934427, 130.73047858942067, 6, 1, 130, 57],\n",
       " ['AA Noffke', 9, 9.0, 90.0, 0, 0, 1, 0],\n",
       " ['Jaskaran Singh', 8, 8.0, 72.72727272727273, 0, 0, 0, 0],\n",
       " ['LH Ferguson', 1, 0, 100.0, 0, 0, 0, 0],\n",
       " ['DS Kulkarni', 90, 11.25, 93.75, 0, 0, 6, 2],\n",
       " ['RA Jadeja', 1732, 24.055555555555557, 122.92405961674946, 0, 0, 125, 57],\n",
       " ['KAJ Roach', 10, 10.0, 111.11111111111111, 0, 0, 1, 0],\n",
       " ['GB Hogg', 22, 7.333333333333333, 100.0, 0, 0, 1, 0],\n",
       " ['DB Ravi Teja', 375, 19.736842105263158, 118.29652996845425, 1, 0, 35, 9],\n",
       " ['R Tewatia', 35, 17.5, 145.83333333333331, 0, 0, 4, 1],\n",
       " ['SC Ganguly', 1349, 25.452830188679247, 106.80918448139352, 7, 0, 137, 42],\n",
       " ['SJ Srivastava', 5, 5.0, 100.0, 0, 0, 0, 0],\n",
       " ['DA Miller', 1563, 34.733333333333334, 141.44796380090497, 8, 1, 104, 78],\n",
       " ['RS Sodhi', 4, 0, 200.0, 0, 0, 1, 0],\n",
       " ['TS Mills', 8, 2.6666666666666665, 72.72727272727273, 0, 0, 0, 1],\n",
       " ['S Ladda', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['MK Pandey', 2215, 28.397435897435898, 120.11930585683297, 9, 1, 197, 64],\n",
       " ['TA Boult', 5, 0, 62.5, 0, 0, 0, 0],\n",
       " ['SB Styris', 131, 18.714285714285715, 98.49624060150376, 0, 0, 10, 3],\n",
       " ['JDS Neesham', 42, 14.0, 91.30434782608695, 0, 0, 3, 1],\n",
       " ['CR Woakes', 46, 11.5, 97.87234042553192, 0, 0, 4, 1],\n",
       " ['JP Faulkner', 527, 20.26923076923077, 135.47557840616966, 0, 0, 36, 23],\n",
       " ['AC Thomas', 20, 10.0, 105.26315789473684, 0, 0, 1, 1],\n",
       " ['MA Agarwal', 814, 17.695652173913043, 123.33333333333334, 3, 0, 82, 33],\n",
       " ['DT Christian', 420, 18.26086956521739, 123.16715542521995, 0, 0, 22, 18],\n",
       " ['Salman Butt', 193, 27.571428571428573, 119.87577639751552, 1, 0, 30, 2],\n",
       " ['RR Sarwan', 73, 18.25, 97.33333333333334, 0, 0, 6, 1],\n",
       " ['MS Bisla', 798, 21.0, 113.67521367521367, 4, 0, 93, 23],\n",
       " ['B Laughlin', 4, 4.0, 44.44444444444444, 0, 0, 0, 0],\n",
       " ['DJ Jacobs', 92, 13.142857142857142, 93.87755102040816, 0, 0, 10, 4],\n",
       " ['TG Southee', 54, 9.0, 154.2857142857143, 0, 0, 3, 3],\n",
       " ['VH Zol', 29, 14.5, 111.53846153846155, 0, 0, 3, 1],\n",
       " ['J Yadav', 6, 3.0, 60.0, 0, 0, 0, 0],\n",
       " ['X Thalaivan Sargunam', 10, 10.0, 58.82352941176471, 0, 0, 0, 0],\n",
       " ['BCJ Cutting', 124, 24.8, 187.87878787878788, 0, 0, 9, 10],\n",
       " ['AB de Villiers',\n",
       "  3473,\n",
       "  38.16483516483517,\n",
       "  148.1655290102389,\n",
       "  22,\n",
       "  3,\n",
       "  287,\n",
       "  156],\n",
       " ['SR Watson', 2622, 30.847058823529412, 138.65679534637758, 14, 2, 257, 122],\n",
       " ['K Goel', 218, 14.533333333333333, 94.37229437229438, 0, 0, 17, 9],\n",
       " ['PR Shah', 92, 11.5, 101.0989010989011, 0, 0, 9, 2],\n",
       " ['SP Goswami', 241, 16.066666666666666, 96.01593625498009, 1, 0, 26, 2],\n",
       " ['KK Nair', 1158, 25.17391304347826, 126.9736842105263, 8, 0, 135, 26],\n",
       " ['T Taibu', 31, 10.333333333333334, 119.23076923076923, 0, 0, 3, 0],\n",
       " ['R Ashwin', 231, 10.5, 96.25, 0, 0, 22, 2],\n",
       " ['A Nehra', 41, 5.857142857142857, 66.12903225806451, 0, 0, 3, 1],\n",
       " ['Shivam Sharma', 5, 5.0, 166.66666666666669, 0, 0, 1, 0],\n",
       " ['MP Stoinis', 163, 23.285714285714285, 123.48484848484848, 1, 0, 14, 4],\n",
       " ['M de Lange', 1, 1.0, 50.0, 0, 0, 0, 0],\n",
       " ['RR Powar', 67, 22.333333333333332, 104.6875, 0, 0, 6, 1],\n",
       " ['Z Khan', 117, 8.357142857142858, 82.97872340425532, 0, 0, 11, 2],\n",
       " ['AJ Finch', 1603, 27.16949152542373, 130.43124491456467, 13, 0, 170, 59],\n",
       " ['R Dhawan', 153, 21.857142857142858, 113.33333333333333, 0, 0, 13, 4],\n",
       " ['S Kaushik', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['D Salunkhe', 33, 16.5, 137.5, 0, 0, 5, 0],\n",
       " ['SN Khan', 177, 29.5, 173.52941176470588, 0, 0, 19, 6],\n",
       " ['AL Menaria', 401, 19.095238095238095, 112.64044943820224, 0, 0, 24, 18],\n",
       " ['PA Reddy', 164, 16.4, 102.49999999999999, 0, 0, 15, 2],\n",
       " ['KL Rahul', 725, 30.208333333333332, 125.64991334488735, 4, 0, 61, 24],\n",
       " ['Sachin Baby', 137, 17.125, 135.64356435643566, 0, 0, 11, 5],\n",
       " ['AD Nath', 20, 5.0, 71.42857142857143, 0, 0, 1, 0],\n",
       " ['MS Wade', 22, 7.333333333333333, 66.66666666666666, 0, 0, 0, 0],\n",
       " ['N Saini', 140, 14.0, 99.29078014184397, 1, 0, 16, 0],\n",
       " ['CM Gautam', 169, 16.9, 112.66666666666667, 0, 0, 17, 6],\n",
       " ['SB Bangar', 49, 7.0, 84.48275862068965, 0, 0, 1, 3],\n",
       " ['DJ Hussey', 1322, 26.979591836734695, 122.97674418604652, 5, 0, 90, 60],\n",
       " ['Harbhajan Singh',\n",
       "  799,\n",
       "  15.666666666666666,\n",
       "  142.42424242424244,\n",
       "  1,\n",
       "  0,\n",
       "  76,\n",
       "  41],\n",
       " ['MD Mishra', 237, 14.8125, 113.9423076923077, 0, 0, 24, 8],\n",
       " ['UA Birla', 7, 7.0, 50.0, 0, 0, 1, 0],\n",
       " ['RJ Harris', 117, 9.75, 104.46428571428572, 0, 0, 6, 3],\n",
       " ['B Kumar', 158, 8.31578947368421, 100.63694267515923, 0, 0, 15, 3],\n",
       " ['A Flintoff', 62, 31.0, 116.98113207547169, 0, 0, 5, 2],\n",
       " ['CH Morris', 439, 36.583333333333336, 165.66037735849056, 2, 0, 33, 23],\n",
       " ['MF Maharoof', 177, 17.7, 143.90243902439025, 0, 0, 12, 9],\n",
       " ['L Balaji', 36, 4.5, 73.46938775510205, 0, 0, 2, 1],\n",
       " ['SE Bond', 1, 1.0, 50.0, 0, 0, 0, 0],\n",
       " ['R Rampaul', 51, 10.2, 102.0, 0, 0, 3, 2],\n",
       " ['AA Chavan', 12, 6.0, 109.09090909090908, 0, 0, 2, 0],\n",
       " ['C de Grandhomme', 126, 15.75, 136.95652173913044, 0, 0, 10, 6],\n",
       " ['PA Patel', 2322, 21.700934579439252, 117.21352852094901, 10, 0, 297, 35],\n",
       " ['Kamran Akmal', 128, 25.6, 164.1025641025641, 1, 0, 13, 8],\n",
       " ['S Sriram', 31, 15.5, 86.11111111111111, 0, 0, 3, 0],\n",
       " ['CA Ingram', 21, 10.5, 80.76923076923077, 0, 0, 2, 0],\n",
       " ['JEC Franklin', 327, 29.727272727272727, 108.63787375415282, 1, 0, 25, 9],\n",
       " ['S Sohal', 368, 18.4, 126.02739726027397, 2, 0, 34, 18],\n",
       " ['U Kaul', 0, 0, 0.0, 0, 0, 0, 0],\n",
       " ['AA Jhunjhunwala', 217, 16.692307692307693, 103.33333333333334, 1, 0, 19, 5],\n",
       " ['TM Dilshan', 1153, 26.813953488372093, 114.49851042701091, 9, 0, 140, 24],\n",
       " ['Yuvraj Singh',\n",
       "  2587,\n",
       "  25.613861386138613,\n",
       "  131.1866125760649,\n",
       "  12,\n",
       "  0,\n",
       "  204,\n",
       "  141],\n",
       " ['M Klinger', 73, 18.25, 94.8051948051948, 0, 0, 9, 0],\n",
       " ['M Kaif', 259, 14.38888888888889, 103.60000000000001, 0, 0, 22, 6],\n",
       " ['JDP Oram', 106, 13.25, 98.14814814814815, 0, 0, 6, 5],\n",
       " ['AD Mathews', 724, 23.35483870967742, 125.91304347826086, 1, 0, 44, 29],\n",
       " ['SM Pollock', 147, 18.375, 132.43243243243242, 0, 0, 12, 8],\n",
       " ['PP Chawla', 515, 12.261904761904763, 113.68653421633555, 0, 0, 49, 15],\n",
       " ['S Kaul', 9, 0, 69.23076923076923, 0, 0, 0, 0],\n",
       " ['SB Jakati', 28, 28.0, 96.55172413793103, 0, 0, 3, 0],\n",
       " ['Harpreet Singh', 20, 10.0, 76.92307692307693, 0, 0, 0, 0],\n",
       " ['Misbah-ul-Haq', 117, 16.714285714285715, 144.44444444444443, 0, 0, 10, 6],\n",
       " ['MR Marsh', 225, 18.75, 114.79591836734696, 0, 0, 9, 14],\n",
       " ['Parvez Rasool', 17, 8.5, 85.0, 0, 0, 1, 0],\n",
       " ['D Kalyankrishna', 3, 3.0, 37.5, 0, 0, 0, 0],\n",
       " ['RR Pant', 564, 25.636363636363637, 151.20643431635386, 3, 0, 47, 30],\n",
       " ['PD Collingwood', 203, 40.6, 130.12820512820514, 3, 0, 9, 13],\n",
       " ['W Jaffer', 130, 16.25, 107.43801652892562, 1, 0, 14, 3],\n",
       " ['SW Tait', 23, 7.666666666666667, 88.46153846153845, 0, 0, 1, 1],\n",
       " ['HH Gibbs', 886, 27.6875, 109.78934324659231, 6, 0, 83, 31],\n",
       " ['RV Uthappa', 3778, 29.515625, 131.6376306620209, 22, 0, 377, 125],\n",
       " ['SA Abbott', 15, 7.5, 115.38461538461537, 0, 0, 1, 1],\n",
       " ['ND Doshi', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['AB Agarkar', 179, 17.9, 116.23376623376625, 0, 0, 13, 5],\n",
       " ['OA Shah', 506, 33.733333333333334, 130.0771208226221, 4, 0, 34, 23],\n",
       " ['DJ Muthuswami', 1, 1.0, 33.33333333333333, 0, 0, 0, 0],\n",
       " ['C Madan', 15, 15.0, 78.94736842105263, 0, 0, 3, 0],\n",
       " ['DJ Thornely', 39, 13.0, 73.58490566037736, 0, 0, 2, 2],\n",
       " ['DJ Harris', 111, 27.75, 109.9009900990099, 0, 0, 11, 5],\n",
       " ['JC Buttler', 527, 23.954545454545453, 145.98337950138506, 1, 0, 50, 26],\n",
       " ['BJ Hodge', 1400, 33.333333333333336, 125.22361359570662, 6, 0, 122, 43],\n",
       " ['AT Rayudu', 2416, 27.146067415730336, 126.16187989556136, 14, 0, 205, 79],\n",
       " ['JH Kallis', 2427, 28.55294117647059, 109.22592259225922, 17, 0, 255, 44],\n",
       " ['AS Yadav', 49, 9.8, 125.64102564102564, 0, 0, 5, 2],\n",
       " ['SP Fleming', 196, 21.77777777777778, 118.7878787878788, 0, 0, 27, 3],\n",
       " ['AN Ahmed', 36, 36.0, 138.46153846153845, 0, 0, 4, 1],\n",
       " ['V Kohli', 4418, 37.440677966101696, 129.8266235674405, 30, 4, 383, 160],\n",
       " ['DH Yagnik', 170, 17.0, 124.08759124087592, 0, 0, 23, 2],\n",
       " ['JJ Bumrah', 25, 12.5, 96.15384615384616, 0, 0, 1, 1],\n",
       " ['S Randiv', 2, 2.0, 50.0, 0, 0, 0, 0],\n",
       " ['STR Binny', 766, 20.157894736842106, 126.82119205298012, 0, 0, 59, 29],\n",
       " ['KP Pietersen', 1001, 35.75, 134.72409152086138, 4, 1, 91, 40],\n",
       " ['UBT Chand', 300, 15.0, 100.0, 1, 0, 32, 9],\n",
       " ['SK Warne', 198, 9.9, 92.5233644859813, 0, 0, 14, 6],\n",
       " ['MS Dhoni', 3560, 37.87234042553192, 136.7127496159754, 17, 0, 251, 156],\n",
       " ['AP Dole', 34, 17.0, 154.54545454545453, 0, 0, 3, 1],\n",
       " ['HH Pandya', 406, 21.36842105263158, 142.4561403508772, 1, 0, 24, 28],\n",
       " ['R Bishnoi', 19, 6.333333333333333, 111.76470588235294, 0, 0, 1, 2],\n",
       " ['SM Harwood', 9, 0, 64.28571428571429, 0, 0, 0, 0],\n",
       " ['S Badrinath', 1441, 30.659574468085108, 118.8943894389439, 11, 0, 154, 28],\n",
       " ['AS Raut', 194, 21.555555555555557, 116.1676646706587, 0, 0, 13, 7],\n",
       " ['BA Stokes', 316, 31.6, 142.98642533936652, 1, 1, 22, 15],\n",
       " ['TM Head', 205, 29.285714285714285, 138.51351351351352, 1, 0, 12, 8],\n",
       " ['Ankit Soni', 7, 3.5, 77.77777777777779, 0, 0, 0, 1],\n",
       " ['R Vinay Kumar', 304, 10.857142857142858, 112.1771217712177, 0, 0, 20, 9],\n",
       " ['AP Tare', 339, 14.125, 124.17582417582418, 1, 0, 40, 11],\n",
       " ['Younis Khan', 3, 3.0, 42.857142857142854, 0, 0, 0, 0],\n",
       " ['AD Mascarenhas', 79, 8.777777777777779, 101.28205128205127, 0, 0, 5, 1],\n",
       " ['Y Venugopal Rao',\n",
       "  985,\n",
       "  22.386363636363637,\n",
       "  117.82296650717703,\n",
       "  3,\n",
       "  0,\n",
       "  77,\n",
       "  37],\n",
       " ['SMSM Senanayake', 10, 10.0, 58.82352941176471, 0, 0, 0, 0],\n",
       " ['MS Gony', 99, 9.9, 139.43661971830986, 0, 0, 6, 8],\n",
       " ['Bipul Sharma', 187, 23.375, 152.03252032520325, 0, 0, 11, 9],\n",
       " ['KC Cariappa', 15, 5.0, 83.33333333333334, 0, 0, 0, 1],\n",
       " ['A Mithun', 34, 6.8, 130.76923076923077, 0, 0, 4, 1],\n",
       " ['SA Yadav', 608, 20.96551724137931, 130.752688172043, 1, 0, 54, 21],\n",
       " ['Pankaj Singh', 7, 3.5, 58.333333333333336, 0, 0, 0, 0],\n",
       " ['NS Naik', 23, 11.5, 74.19354838709677, 0, 0, 1, 0],\n",
       " ['AB Barath', 42, 21.0, 100.0, 0, 0, 5, 1],\n",
       " ['S Nadeem', 26, 2.1666666666666665, 37.142857142857146, 0, 0, 1, 0],\n",
       " ['Mohammad Hafeez', 64, 9.142857142857142, 77.10843373493977, 0, 0, 7, 2],\n",
       " ['HV Patel', 47, 5.222222222222222, 138.23529411764704, 0, 0, 2, 3],\n",
       " ['S Tyagi', 3, 3.0, 75.0, 0, 0, 0, 0],\n",
       " ['GD McGrath', 4, 4.0, 80.0, 0, 0, 1, 0],\n",
       " ['Harmeet Singh', 18, 4.5, 150.0, 0, 0, 3, 0],\n",
       " ['SP Jackson', 38, 19.0, 122.58064516129032, 0, 0, 5, 0],\n",
       " ['KA Pollard', 2344, 28.585365853658537, 146.59161976235146, 12, 0, 157, 147],\n",
       " ['G Gambhir', 4132, 31.541984732824428, 124.60796139927623, 35, 0, 484, 58],\n",
       " ['B Lee', 124, 12.4, 127.83505154639174, 0, 0, 8, 8],\n",
       " ['Yashpal Singh', 47, 11.75, 71.21212121212122, 0, 0, 5, 0],\n",
       " ['V Shankar', 101, 50.5, 134.66666666666666, 1, 0, 12, 1],\n",
       " ['YA Abdulla', 0, 0, 0.0, 0, 0, 0, 0],\n",
       " ['LR Shukla', 405, 15.0, 115.71428571428572, 0, 0, 33, 16],\n",
       " ['V Sehwag', 2728, 27.555555555555557, 155.44159544159544, 16, 2, 334, 106],\n",
       " ['JR Hopes', 417, 26.0625, 136.27450980392157, 4, 0, 49, 11],\n",
       " ['R Sharma', 66, 4.714285714285714, 88.0, 0, 0, 5, 3],\n",
       " ['SP Narine', 271, 13.55, 147.2826086956522, 1, 0, 36, 12],\n",
       " ['AD Russell', 574, 26.09090909090909, 173.41389728096678, 3, 0, 48, 37],\n",
       " ['DB Das', 304, 21.714285714285715, 116.47509578544062, 0, 0, 23, 16],\n",
       " ['I Malhotra', 7, 0, 175.0, 0, 0, 1, 0],\n",
       " ['SS Iyer', 807, 27.82758620689655, 128.50318471337582, 6, 0, 79, 32],\n",
       " ['DNT Zoysa', 11, 11.0, 122.22222222222223, 0, 0, 1, 0],\n",
       " ['A Kumble', 35, 11.666666666666666, 74.46808510638297, 0, 0, 3, 0],\n",
       " ['J Theron', 10, 5.0, 83.33333333333334, 0, 0, 1, 0],\n",
       " ['Basil Thampi', 28, 0, 100.0, 0, 0, 1, 1],\n",
       " ['S Gopal', 14, 7.0, 51.85185185185185, 0, 0, 1, 0],\n",
       " ['SS Shaikh', 6, 6.0, 85.71428571428571, 0, 0, 1, 0],\n",
       " ['RG Sharma', 4207, 32.6124031007752, 130.8960796515246, 32, 1, 354, 173],\n",
       " ['MK Tiwary', 1648, 29.428571428571427, 117.29537366548041, 7, 0, 152, 39],\n",
       " ['Y Nagar', 285, 20.357142857142858, 110.03861003861005, 0, 0, 20, 9],\n",
       " ['S Badree', 13, 2.6, 56.52173913043478, 0, 0, 0, 0],\n",
       " ['Ishan Kishan', 319, 21.266666666666666, 129.1497975708502, 1, 0, 34, 13],\n",
       " ['DT Patil', 13, 6.5, 100.0, 0, 0, 1, 0],\n",
       " ['MN Samuels', 161, 12.384615384615385, 93.6046511627907, 0, 0, 9, 7],\n",
       " ['DL Vettori', 121, 15.125, 107.07964601769913, 0, 0, 11, 2],\n",
       " ['JE Taylor', 3, 3.0, 100.0, 0, 0, 0, 0],\n",
       " ['RG More', 2, 0, 100.0, 0, 0, 0, 0],\n",
       " ['DP Vijaykumar', 1, 0, 100.0, 0, 0, 0, 0],\n",
       " ['P Negi', 353, 17.65, 129.77941176470588, 0, 0, 26, 16],\n",
       " ['LMP Simmons', 1079, 39.96296296296296, 126.6431924882629, 11, 1, 109, 44],\n",
       " ['N Rana', 437, 31.214285714285715, 128.90855457227138, 4, 0, 31, 25],\n",
       " ['Ankit Sharma', 87, 12.428571428571429, 129.8507462686567, 0, 0, 7, 4],\n",
       " ['B Akhil', 76, 12.666666666666666, 138.1818181818182, 0, 0, 5, 5],\n",
       " ['GJ Maxwell', 1228, 25.06122448979592, 164.3908969210174, 6, 0, 96, 82],\n",
       " ['R Shukla', 19, 19.0, 82.6086956521739, 0, 0, 2, 0],\n",
       " ['CL White', 954, 26.5, 126.35761589403972, 6, 0, 76, 36],\n",
       " ['KD Karthik', 2903, 24.811965811965813, 125.94360086767895, 14, 0, 286, 71],\n",
       " ['M Manhas', 514, 22.347826086956523, 109.36170212765957, 0, 0, 43, 10],\n",
       " ['R McLaren', 159, 19.875, 92.98245614035088, 1, 0, 14, 1],\n",
       " ['V Pratap Singh', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['Niraj Patel', 121, 30.25, 101.68067226890756, 1, 0, 14, 1],\n",
       " ['Shoaib Ahmed', 1, 0.5, 16.666666666666664, 0, 0, 0, 0],\n",
       " ['IC Pandey', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['KS Williamson', 411, 31.615384615384617, 129.24528301886792, 3, 0, 35, 11],\n",
       " ['A Mishra', 340, 11.724137931034482, 93.66391184573003, 0, 0, 27, 5],\n",
       " ['TD Paine', 10, 5.0, 38.46153846153847, 0, 0, 0, 0],\n",
       " ['SE Marsh', 2477, 39.317460317460316, 132.7438370846731, 20, 1, 266, 78],\n",
       " ['AB Dinda', 26, 2.6, 54.166666666666664, 0, 0, 2, 0],\n",
       " ['LA Pomersbach', 302, 27.454545454545453, 122.76422764227641, 1, 0, 25, 13],\n",
       " ['GJ Bailey', 663, 24.555555555555557, 121.875, 2, 0, 59, 19],\n",
       " ['J Syed Mohammad', 29, 14.5, 111.53846153846155, 0, 0, 4, 0],\n",
       " ['NM Coulter-Nile', 52, 6.5, 104.0, 0, 0, 3, 4],\n",
       " ['FY Fazal', 183, 18.3, 105.78034682080926, 0, 0, 22, 1],\n",
       " ['CRD Fernando', 4, 0, 133.33333333333331, 0, 0, 0, 0],\n",
       " ['ER Dwivedi', 24, 12.0, 171.42857142857142, 0, 0, 2, 2],\n",
       " ['SD Chitnis', 99, 14.142857142857142, 111.23595505617978, 0, 0, 10, 2],\n",
       " ['J Suchith', 48, 48.0, 150.0, 0, 0, 4, 2],\n",
       " ['FH Edwards', 4, 4.0, 80.0, 0, 0, 0, 0],\n",
       " ['Kuldeep Yadav', 20, 10.0, 90.9090909090909, 0, 0, 2, 0],\n",
       " ['K Upadhyay', 12, 0, 133.33333333333331, 0, 0, 0, 1],\n",
       " ['JD Ryder', 604, 21.571428571428573, 131.87772925764193, 4, 0, 69, 19],\n",
       " ['AN Ghosh', 7, 3.5, 87.5, 0, 0, 1, 0],\n",
       " ['PM Sarvesh Kumar', 1, 0, 50.0, 0, 0, 0, 0],\n",
       " ['AJ Tye', 53, 26.5, 176.66666666666666, 0, 0, 4, 3],\n",
       " ['NL McCullum', 26, 26.0, 118.18181818181819, 0, 0, 0, 1],\n",
       " ['PV Tambe', 18, 9.0, 46.15384615384615, 0, 0, 1, 0],\n",
       " ['P Kumar', 340, 8.947368421052632, 108.28025477707006, 0, 0, 22, 17],\n",
       " ['RR Rossouw', 53, 10.6, 103.921568627451, 0, 0, 3, 3],\n",
       " ['C Munro', 30, 10.0, 100.0, 0, 0, 3, 0],\n",
       " ['SB Joshi', 6, 3.0, 42.857142857142854, 0, 0, 0, 0],\n",
       " ['LPC Silva', 40, 20.0, 153.84615384615387, 0, 0, 5, 1],\n",
       " ['AB McDonald', 123, 24.6, 123.0, 0, 0, 9, 4],\n",
       " ['KJ Abbott', 13, 13.0, 162.5, 0, 0, 0, 1],\n",
       " ['J Arunkumar', 23, 7.666666666666667, 100.0, 0, 0, 5, 0],\n",
       " ['S Aravind', 59, 14.75, 103.50877192982458, 0, 0, 7, 0],\n",
       " ['BR Dunk', 40, 13.333333333333334, 114.28571428571428, 0, 0, 7, 0],\n",
       " ['J Botha', 409, 20.45, 113.9275766016713, 1, 0, 39, 5],\n",
       " ['RR Bhatkal', 0, 0.0, 0.0, 0, 0, 0, 0],\n",
       " ['RT Ponting', 91, 10.11111111111111, 71.09375, 0, 0, 5, 2],\n",
       " ['D Wiese', 127, 31.75, 141.11111111111111, 0, 0, 12, 4],\n",
       " ['BJ Haddin', 18, 18.0, 163.63636363636365, 0, 0, 2, 1],\n",
       " ['NJ Rimmington', 1, 0, 100.0, 0, 0, 0, 0],\n",
       " ['AF Milne', 7, 3.5, 58.333333333333336, 0, 0, 0, 0],\n",
       " ['M Vijay', 2511, 26.431578947368422, 123.39066339066338, 13, 2, 237, 89],\n",
       " ['A Chopra', 53, 8.833333333333334, 74.64788732394366, 0, 0, 7, 0],\n",
       " ['Mashrafe Mortaza', 2, 0, 100.0, 0, 0, 0, 0],\n",
       " ['BB Sran', 4, 0, 100.0, 0, 0, 0, 0],\n",
       " ['RJ Peterson', 32, 10.666666666666666, 106.66666666666667, 0, 0, 3, 1],\n",
       " ['KV Sharma', 317, 15.095238095238095, 115.69343065693431, 0, 0, 18, 14],\n",
       " ['JJ van der Wath', 18, 9.0, 112.5, 0, 0, 1, 1],\n",
       " ['Karanveer Singh', 12, 12.0, 70.58823529411765, 0, 0, 1, 0],\n",
       " ['KW Richardson', 36, 12.0, 92.3076923076923, 0, 0, 2, 1],\n",
       " ['D du Preez', 10, 10.0, 76.92307692307693, 0, 0, 0, 0],\n",
       " ['CK Langeveldt', 8, 4.0, 88.88888888888889, 0, 0, 0, 1],\n",
       " ['SS Tiwary', 1276, 28.355555555555554, 119.58762886597938, 7, 0, 93, 45],\n",
       " ['DJ Hooda', 373, 14.346153846153847, 139.17910447761196, 1, 0, 20, 20],\n",
       " ['WD Parnell', 63, 7.0, 84.0, 0, 0, 4, 1],\n",
       " ['DAJ Bracewell', 12, 0, 133.33333333333331, 0, 0, 1, 0],\n",
       " ['GC Smith', 739, 28.423076923076923, 110.62874251497006, 4, 0, 94, 9],\n",
       " ['Shoaib Akhtar', 2, 2.0, 28.57142857142857, 0, 0, 0, 0],\n",
       " ['KMDN Kulasekara', 5, 0, 166.66666666666669, 0, 0, 1, 0],\n",
       " ['PC Valthaty', 505, 22.954545454545453, 120.8133971291866, 2, 1, 61, 20],\n",
       " ['S Narwal', 37, 12.333333333333334, 137.03703703703704, 0, 0, 6, 0],\n",
       " ['CR Brathwaite', 95, 11.875, 179.24528301886792, 0, 0, 8, 8],\n",
       " ['LRPL Taylor', 1017, 25.425, 123.72262773722629, 3, 0, 66, 46],\n",
       " ['GR Napier', 15, 15.0, 93.75, 0, 0, 1, 0],\n",
       " ['RR Raje', 20, 20.0, 111.11111111111111, 0, 0, 1, 1],\n",
       " ['AC Gilchrist',\n",
       "  2069,\n",
       "  27.223684210526315,\n",
       "  138.39464882943145,\n",
       "  11,\n",
       "  2,\n",
       "  239,\n",
       "  92],\n",
       " ['S Dhawan', 3561, 32.669724770642205, 121.86858316221767, 28, 0, 401, 71],\n",
       " ['MM Sharma', 112, 8.0, 109.80392156862746, 0, 0, 8, 4],\n",
       " ['P Sahu', 19, 19.0, 146.15384615384613, 0, 0, 1, 1],\n",
       " ['Sunny Singh', 43, 10.75, 138.70967741935485, 0, 0, 6, 1],\n",
       " ['RA Tripathi', 391, 27.928571428571427, 146.44194756554307, 2, 0, 43, 17],\n",
       " ['Shahid Afridi', 81, 10.125, 176.08695652173913, 0, 0, 7, 6],\n",
       " ['R Dravid', 2174, 28.233766233766232, 115.5154091392136, 11, 0, 269, 28],\n",
       " ['A Ashish Reddy', 280, 18.666666666666668, 145.0777202072539, 0, 0, 16, 15],\n",
       " ['P Dogra', 127, 11.545454545454545, 92.02898550724638, 0, 0, 4, 5],\n",
       " ['AG Paunikar', 49, 9.8, 84.48275862068965, 0, 0, 9, 0],\n",
       " ['VR Aaron', 46, 11.5, 83.63636363636363, 0, 0, 2, 2],\n",
       " ['DE Bollinger', 21, 21.0, 91.30434782608695, 0, 0, 1, 1],\n",
       " ['BMAJ Mendis', 23, 7.666666666666667, 85.18518518518519, 0, 0, 0, 0],\n",
       " ['AM Nayar', 672, 17.68421052631579, 116.46447140381282, 0, 0, 55, 20],\n",
       " ['SPD Smith', 1703, 37.02173913043478, 131.7092034029389, 5, 1, 150, 45],\n",
       " ['VVS Laxman', 282, 14.842105263157896, 105.61797752808988, 1, 0, 33, 5],\n",
       " ['CJ McKay', 8, 8.0, 53.333333333333336, 0, 0, 0, 0],\n",
       " ['Anirudh Singh', 63, 15.75, 95.45454545454545, 0, 0, 6, 1],\n",
       " ['S Sreesanth', 34, 8.5, 61.81818181818181, 0, 0, 6, 0],\n",
       " ['TR Birt', 75, 15.0, 129.31034482758622, 0, 0, 9, 2],\n",
       " ['P Parameswaran', 1, 0, 50.0, 0, 0, 0, 0],\n",
       " ['MG Johnson', 151, 11.615384615384615, 98.69281045751634, 0, 0, 8, 7],\n",
       " ['B Sumanth', 35, 35.0, 94.5945945945946, 0, 0, 3, 0],\n",
       " ['KK Cooper', 116, 12.88888888888889, 170.58823529411765, 0, 0, 9, 8],\n",
       " ['MDKJ Perera', 14, 7.0, 107.6923076923077, 0, 0, 3, 0],\n",
       " ['I Sharma', 45, 7.5, 77.58620689655173, 0, 0, 3, 1],\n",
       " ['Shoaib Malik', 52, 13.0, 110.63829787234043, 0, 0, 5, 0],\n",
       " ['Y Gnaneswara Rao', 19, 19.0, 111.76470588235294, 0, 0, 3, 0],\n",
       " ['Kamran Khan', 3, 3.0, 60.0, 0, 0, 0, 0],\n",
       " ['RV Gomez', 50, 10.0, 98.0392156862745, 0, 0, 5, 1],\n",
       " ['NJ Maddinson', 20, 6.666666666666667, 95.23809523809523, 0, 0, 4, 0],\n",
       " ['Swapnil Singh', 12, 4.0, 60.0, 0, 0, 0, 0],\n",
       " ['WPUJC Vaas', 81, 10.125, 110.95890410958904, 0, 0, 2, 3],\n",
       " ['A Mukund', 19, 9.5, 86.36363636363636, 0, 0, 1, 0],\n",
       " ['SK Trivedi', 42, 6.0, 71.1864406779661, 0, 0, 3, 1],\n",
       " ['A Chandila', 4, 0, 57.14285714285714, 0, 0, 0, 0],\n",
       " ['Azhar Mahmood', 388, 20.42105263157895, 128.05280528052805, 2, 0, 39, 13],\n",
       " ['Joginder Sharma', 36, 9.0, 120.0, 0, 0, 1, 2],\n",
       " ['F Behardien', 14, 7.0, 107.6923076923077, 0, 0, 2, 0],\n",
       " ['Q de Kock', 726, 29.04, 132.24043715846994, 5, 1, 90, 21],\n",
       " ['KH Pandya', 480, 34.285714285714285, 158.41584158415841, 1, 0, 46, 20],\n",
       " ['KB Arun Karthik', 51, 10.2, 100.0, 0, 0, 4, 1],\n",
       " ['S Chanderpaul', 25, 8.333333333333334, 80.64516129032258, 0, 0, 4, 0],\n",
       " ['MM Patel', 39, 6.5, 95.1219512195122, 0, 0, 5, 0],\n",
       " ['P Awana', 5, 2.5, 35.714285714285715, 0, 0, 0, 0],\n",
       " ['DS Lehmann', 18, 9.0, 100.0, 0, 0, 3, 0],\n",
       " ['AM Rahane', 3057, 33.59340659340659, 120.59171597633136, 25, 1, 320, 60],\n",
       " ['MJ Guptill', 189, 21.0, 132.16783216783216, 1, 0, 20, 8],\n",
       " ['MJ McClenaghan', 63, 7.875, 143.1818181818182, 0, 0, 2, 6],\n",
       " ['M Morkel', 126, 11.454545454545455, 140.0, 0, 0, 11, 5],\n",
       " ['SN Thakur', 7, 2.3333333333333335, 77.77777777777779, 0, 0, 1, 0],\n",
       " ['TM Srivastava', 8, 8.0, 72.72727272727273, 0, 0, 0, 0],\n",
       " ['M Vohra', 957, 24.53846153846154, 132.73231622746187, 3, 0, 95, 35],\n",
       " ['BAW Mendis', 3, 1.5, 50.0, 0, 0, 0, 0],\n",
       " ['T Kohli', 11, 3.6666666666666665, 57.89473684210527, 0, 0, 0, 1],\n",
       " ['RE Levi', 83, 13.833333333333334, 113.69863013698631, 1, 0, 10, 4],\n",
       " ['EJG Morgan', 854, 21.35, 121.13475177304964, 4, 0, 72, 34],\n",
       " ['Mohammad Ashraful', 2, 2.0, 20.0, 0, 0, 0, 0],\n",
       " ['K Rabada', 55, 18.333333333333332, 85.9375, 0, 0, 5, 1],\n",
       " ['MA Khote', 24, 12.0, 109.09090909090908, 0, 0, 2, 1],\n",
       " ['BB McCullum',\n",
       "  2753,\n",
       "  28.091836734693878,\n",
       "  131.22020972354625,\n",
       "  13,\n",
       "  2,\n",
       "  277,\n",
       "  124],\n",
       " ['SK Raina', 4540, 33.88059701492537, 139.09313725490196, 31, 1, 402, 173]]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_condition = ListTable()\n",
    "\n",
    "analysis_condition.append(['Name', 'Runs', 'Average', 'Strike rate','Fifty', 'Century', 'Fours','Sixes'])\n",
    "for i in range(len(player_list)):\n",
    "    analysis_condition.append([player_list[i], Players_Runs[i], P_avg[i], Players_SR[i],Player_fifty[i], Player_century[i], Player_four[i],Player_six[i]])\n",
    "analysis_condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Data Dictionary for All players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.376923</td>\n",
       "      <td>0.535704</td>\n",
       "      <td>0.567525</td>\n",
       "      <td>0.585589</td>\n",
       "      <td>0.544384</td>\n",
       "      <td>0.595474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century</th>\n",
       "      <td>0.376923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.612013</td>\n",
       "      <td>0.597991</td>\n",
       "      <td>0.618807</td>\n",
       "      <td>0.703608</td>\n",
       "      <td>0.189443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fifty</th>\n",
       "      <td>0.535704</td>\n",
       "      <td>0.612013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957205</td>\n",
       "      <td>0.954166</td>\n",
       "      <td>0.851429</td>\n",
       "      <td>0.261085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Four</th>\n",
       "      <td>0.567525</td>\n",
       "      <td>0.597991</td>\n",
       "      <td>0.957205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981999</td>\n",
       "      <td>0.853202</td>\n",
       "      <td>0.305469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>0.585589</td>\n",
       "      <td>0.618807</td>\n",
       "      <td>0.954166</td>\n",
       "      <td>0.981999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919069</td>\n",
       "      <td>0.320494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Six</th>\n",
       "      <td>0.544384</td>\n",
       "      <td>0.703608</td>\n",
       "      <td>0.851429</td>\n",
       "      <td>0.853202</td>\n",
       "      <td>0.919069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strike Rate</th>\n",
       "      <td>0.595474</td>\n",
       "      <td>0.189443</td>\n",
       "      <td>0.261085</td>\n",
       "      <td>0.305469</td>\n",
       "      <td>0.320494</td>\n",
       "      <td>0.336450</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Average   Century     Fifty      Four      Runs       Six  \\\n",
       "Average      1.000000  0.376923  0.535704  0.567525  0.585589  0.544384   \n",
       "Century      0.376923  1.000000  0.612013  0.597991  0.618807  0.703608   \n",
       "Fifty        0.535704  0.612013  1.000000  0.957205  0.954166  0.851429   \n",
       "Four         0.567525  0.597991  0.957205  1.000000  0.981999  0.853202   \n",
       "Runs         0.585589  0.618807  0.954166  0.981999  1.000000  0.919069   \n",
       "Six          0.544384  0.703608  0.851429  0.853202  0.919069  1.000000   \n",
       "Strike Rate  0.595474  0.189443  0.261085  0.305469  0.320494  0.336450   \n",
       "\n",
       "             Strike Rate  \n",
       "Average         0.595474  \n",
       "Century         0.189443  \n",
       "Fifty           0.261085  \n",
       "Four            0.305469  \n",
       "Runs            0.320494  \n",
       "Six             0.336450  \n",
       "Strike Rate     1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_details = {\"Name\":player_list,\"Runs\":Players_Runs, \"Strike Rate\":Players_SR ,\"Average\":P_avg,\"Century\": Player_century,\"Fifty\": Player_fifty,\"Six\": Player_six,\"Four\": Player_four}\n",
    "batsman_details_dataframe = pd.DataFrame(batsman_details)\n",
    "batsman_details_dataframe.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Name</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.860000</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>123</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>1993</td>\n",
       "      <td>78</td>\n",
       "      <td>124.874687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.709677</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>121</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>1107</td>\n",
       "      <td>44</td>\n",
       "      <td>137.515528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AS Rajpoot</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>46.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>122.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>CJ Ferguson</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>83.760684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>MJ Clarke</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>104.255319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>VY Mahesh</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>55.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BA Bhatt</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25.953846</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>195</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>1687</td>\n",
       "      <td>27</td>\n",
       "      <td>121.192529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DP Nannes</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40.741573</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>294</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>3626</td>\n",
       "      <td>265</td>\n",
       "      <td>151.209341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36.074074</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>74</td>\n",
       "      <td>A Symonds</td>\n",
       "      <td>974</td>\n",
       "      <td>41</td>\n",
       "      <td>129.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>MN van Wyk</td>\n",
       "      <td>167</td>\n",
       "      <td>1</td>\n",
       "      <td>126.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>53.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>H Das</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>88</td>\n",
       "      <td>5</td>\n",
       "      <td>88.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>VRV Singh</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>L Ronchi</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30.116279</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>1295</td>\n",
       "      <td>38</td>\n",
       "      <td>127.964427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>S Rana</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>112.345679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>233.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>606</td>\n",
       "      <td>28</td>\n",
       "      <td>128.118393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AR Bawne</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>JM Kemp</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>108.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.166667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>MJ Lumb</td>\n",
       "      <td>278</td>\n",
       "      <td>6</td>\n",
       "      <td>143.298969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington Sundar</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>38.764706</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>198</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>1977</td>\n",
       "      <td>52</td>\n",
       "      <td>122.642680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>24.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>SM Katich</td>\n",
       "      <td>241</td>\n",
       "      <td>8</td>\n",
       "      <td>129.569892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>33.826087</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>296</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "      <td>2334</td>\n",
       "      <td>29</td>\n",
       "      <td>119.815195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>45.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>AC Voges</td>\n",
       "      <td>181</td>\n",
       "      <td>3</td>\n",
       "      <td>126.573427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>10.125000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>110.958904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A Mukund</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>86.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>SK Trivedi</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>71.186441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>57.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>20.421053</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>Azhar Mahmood</td>\n",
       "      <td>388</td>\n",
       "      <td>13</td>\n",
       "      <td>128.052805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joginder Sharma</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>F Behardien</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>107.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>29.040000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>726</td>\n",
       "      <td>21</td>\n",
       "      <td>132.240437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>34.285714</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>480</td>\n",
       "      <td>20</td>\n",
       "      <td>158.415842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>10.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>KB Arun Karthik</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>8.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>S Chanderpaul</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>80.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>MM Patel</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>95.121951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>P Awana</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>DS Lehmann</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>33.593407</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>320</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>3057</td>\n",
       "      <td>60</td>\n",
       "      <td>120.591716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>132.167832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>7.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>MJ McClenaghan</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>143.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>11.454545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>M Morkel</td>\n",
       "      <td>126</td>\n",
       "      <td>5</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TM Srivastava</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>24.538462</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>957</td>\n",
       "      <td>35</td>\n",
       "      <td>132.732316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BAW Mendis</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>T Kohli</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>57.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>13.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>RE Levi</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>113.698630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>854</td>\n",
       "      <td>34</td>\n",
       "      <td>121.134752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mohammad Ashraful</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>18.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>K Rabada</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>85.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>MA Khote</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>109.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>28.091837</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>277</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>2753</td>\n",
       "      <td>124</td>\n",
       "      <td>131.220210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>33.880597</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>402</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>4540</td>\n",
       "      <td>173</td>\n",
       "      <td>139.093137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Average  Century  Fifty  Four               Name  Runs  Six  \\\n",
       "0    39.860000        0     14   123          JP Duminy  1993   78   \n",
       "1    35.709677        0      8   121          ML Hayden  1107   44   \n",
       "2     6.000000        0      0     1         AS Rajpoot     6    0   \n",
       "3     5.500000        0      0     0        Rashid Khan    11    1   \n",
       "4    16.333333        0      0     9        CJ Ferguson    98    0   \n",
       "5    16.333333        0      0    12          MJ Clarke    98    0   \n",
       "6     7.500000        0      0     0          VY Mahesh    15    0   \n",
       "7     6.000000        0      0     0           BA Bhatt     6    1   \n",
       "8    25.953846        0     10   195      KC Sangakkara  1687   27   \n",
       "9     4.000000        0      0     0          DP Nannes     4    0   \n",
       "10   40.741573        5     21   294           CH Gayle  3626  265   \n",
       "11   36.074074        1      5    74          A Symonds   974   41   \n",
       "12   55.666667        0      1    19         MN van Wyk   167    1   \n",
       "13    5.000000        0      0     0          YS Chahal    15    0   \n",
       "14    2.000000        0      0     0              H Das     2    0   \n",
       "15    5.500000        0      0     6         SL Malinga    88    5   \n",
       "16    2.000000        0      0     1          VRV Singh     4    0   \n",
       "17    6.800000        0      0     6           L Ronchi    34    1   \n",
       "18   30.116279        0      8   110       F du Plessis  1295   38   \n",
       "19   22.750000        0      0     9             S Rana    91    1   \n",
       "20   14.000000        0      0     0          DL Chahar    14    2   \n",
       "21   20.200000        0      0    34           AR Patel   606   28   \n",
       "22    0.000000        0      0     1           AR Bawne    12    0   \n",
       "23   13.000000        0      0     1            JM Kemp    26    1   \n",
       "24   23.166667        0      1    45            MJ Lumb   278    6   \n",
       "25    9.000000        0      0     0  Washington Sundar     9    0   \n",
       "26   38.764706        1     15   198         MEK Hussey  1977   52   \n",
       "27   24.100000        0      2    26          SM Katich   241    8   \n",
       "28   33.826087        1     13   296       SR Tendulkar  2334   29   \n",
       "29   45.250000        0      0    15           AC Voges   181    3   \n",
       "..         ...      ...    ...   ...                ...   ...  ...   \n",
       "431  10.125000        0      0     2         WPUJC Vaas    81    3   \n",
       "432   9.500000        0      0     1           A Mukund    19    0   \n",
       "433   6.000000        0      0     3         SK Trivedi    42    1   \n",
       "434   0.000000        0      0     0         A Chandila     4    0   \n",
       "435  20.421053        0      2    39      Azhar Mahmood   388   13   \n",
       "436   9.000000        0      0     1    Joginder Sharma    36    2   \n",
       "437   7.000000        0      0     2        F Behardien    14    0   \n",
       "438  29.040000        1      5    90          Q de Kock   726   21   \n",
       "439  34.285714        0      1    46          KH Pandya   480   20   \n",
       "440  10.200000        0      0     4    KB Arun Karthik    51    1   \n",
       "441   8.333333        0      0     4      S Chanderpaul    25    0   \n",
       "442   6.500000        0      0     5           MM Patel    39    0   \n",
       "443   2.500000        0      0     0            P Awana     5    0   \n",
       "444   9.000000        0      0     3         DS Lehmann    18    0   \n",
       "445  33.593407        1     25   320          AM Rahane  3057   60   \n",
       "446  21.000000        0      1    20         MJ Guptill   189    8   \n",
       "447   7.875000        0      0     2     MJ McClenaghan    63    6   \n",
       "448  11.454545        0      0    11           M Morkel   126    5   \n",
       "449   2.333333        0      0     1          SN Thakur     7    0   \n",
       "450   8.000000        0      0     0      TM Srivastava     8    0   \n",
       "451  24.538462        0      3    95            M Vohra   957   35   \n",
       "452   1.500000        0      0     0         BAW Mendis     3    0   \n",
       "453   3.666667        0      0     0            T Kohli    11    1   \n",
       "454  13.833333        0      1    10            RE Levi    83    4   \n",
       "455  21.350000        0      4    72         EJG Morgan   854   34   \n",
       "456   2.000000        0      0     0  Mohammad Ashraful     2    0   \n",
       "457  18.333333        0      0     5           K Rabada    55    1   \n",
       "458  12.000000        0      0     2           MA Khote    24    1   \n",
       "459  28.091837        2     13   277        BB McCullum  2753  124   \n",
       "460  33.880597        1     31   402           SK Raina  4540  173   \n",
       "\n",
       "     Strike Rate  \n",
       "0     124.874687  \n",
       "1     137.515528  \n",
       "2      46.153846  \n",
       "3     122.222222  \n",
       "4      83.760684  \n",
       "5     104.255319  \n",
       "6      55.555556  \n",
       "7      85.714286  \n",
       "8     121.192529  \n",
       "9      30.769231  \n",
       "10    151.209341  \n",
       "11    129.866667  \n",
       "12    126.515152  \n",
       "13     53.571429  \n",
       "14     40.000000  \n",
       "15     88.888889  \n",
       "16    133.333333  \n",
       "17    100.000000  \n",
       "18    127.964427  \n",
       "19    112.345679  \n",
       "20    233.333333  \n",
       "21    128.118393  \n",
       "22    100.000000  \n",
       "23    108.333333  \n",
       "24    143.298969  \n",
       "25     75.000000  \n",
       "26    122.642680  \n",
       "27    129.569892  \n",
       "28    119.815195  \n",
       "29    126.573427  \n",
       "..           ...  \n",
       "431   110.958904  \n",
       "432    86.363636  \n",
       "433    71.186441  \n",
       "434    57.142857  \n",
       "435   128.052805  \n",
       "436   120.000000  \n",
       "437   107.692308  \n",
       "438   132.240437  \n",
       "439   158.415842  \n",
       "440   100.000000  \n",
       "441    80.645161  \n",
       "442    95.121951  \n",
       "443    35.714286  \n",
       "444   100.000000  \n",
       "445   120.591716  \n",
       "446   132.167832  \n",
       "447   143.181818  \n",
       "448   140.000000  \n",
       "449    77.777778  \n",
       "450    72.727273  \n",
       "451   132.732316  \n",
       "452    50.000000  \n",
       "453    57.894737  \n",
       "454   113.698630  \n",
       "455   121.134752  \n",
       "456    20.000000  \n",
       "457    85.937500  \n",
       "458   109.090909  \n",
       "459   131.220210  \n",
       "460   139.093137  \n",
       "\n",
       "[461 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_details_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering the Player based of StrikeRate and Runs Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ClassA_name,ClassB_name,ClassC_name = [],[],[]\n",
    "ClassA_Runs,ClassB_Runs,ClassC_Runs = [],[],[]\n",
    "ClassA_SR,ClassB_SR,ClassC_SR = [],[],[]\n",
    "ClassA_avg,ClassB_avg,ClassC_avg = [],[],[]\n",
    "ClassA_cent,ClassB_cent,ClassC_cent = [],[],[]\n",
    "ClassA_fifty,ClassB_fifty,ClassC_fifty = [],[],[]\n",
    "ClassA_six,ClassB_six,ClassC_six = [],[],[]\n",
    "ClassA_four,ClassB_four,ClassC_four = [],[],[]\n",
    "\n",
    "for i in range(len(Players_Runs)-1):\n",
    "    if(Players_Runs[i]>1500 and Players_SR[i]<130):\n",
    "        ClassB_name.append(player_list[i])\n",
    "        ClassB_Runs.append(Players_Runs[i])\n",
    "        ClassB_SR.append(Players_SR[i])\n",
    "        ClassB_avg.append(P_avg[i])\n",
    "        ClassB_cent.append(Player_century[i])\n",
    "        ClassB_fifty.append(Player_fifty[i])\n",
    "        ClassB_six.append(Player_six[i])\n",
    "        ClassB_four.append(Player_four[i])\n",
    "    elif(Players_Runs[i]>1500 and Players_SR[i]>130):\n",
    "        ClassA_name.append(player_list[i])\n",
    "        ClassA_Runs.append(Players_Runs[i])\n",
    "        ClassA_SR.append(Players_SR[i])\n",
    "        ClassA_avg.append(P_avg[i])\n",
    "        ClassA_cent.append(Player_century[i])\n",
    "        ClassA_fifty.append(Player_fifty[i])\n",
    "        ClassA_six.append(Player_six[i])\n",
    "        ClassA_four.append(Player_four[i])\n",
    "    else:\n",
    "        ClassC_name.append(player_list[i])\n",
    "        ClassC_Runs.append(Players_Runs[i])\n",
    "        ClassC_SR.append(Players_SR[i])\n",
    "        ClassC_avg.append(P_avg[i])\n",
    "        ClassC_cent.append(Player_century[i])\n",
    "        ClassC_fifty.append(Player_fifty[i])\n",
    "        ClassC_six.append(Player_six[i])\n",
    "        ClassC_four.append(Player_four[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Data Dictionary for Class A player (More Runs and High Strike rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Name</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.741573</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>294</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>3626</td>\n",
       "      <td>265</td>\n",
       "      <td>151.209341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.140000</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>401</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>4014</td>\n",
       "      <td>160</td>\n",
       "      <td>142.138810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.392857</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>245</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>2385</td>\n",
       "      <td>117</td>\n",
       "      <td>135.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.632653</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>239</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>2904</td>\n",
       "      <td>147</td>\n",
       "      <td>145.490982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.524590</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>130</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>1557</td>\n",
       "      <td>57</td>\n",
       "      <td>130.730479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34.733333</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>104</td>\n",
       "      <td>DA Miller</td>\n",
       "      <td>1563</td>\n",
       "      <td>78</td>\n",
       "      <td>141.447964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38.164835</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>287</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>3473</td>\n",
       "      <td>156</td>\n",
       "      <td>148.165529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.847059</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>257</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>2622</td>\n",
       "      <td>122</td>\n",
       "      <td>138.656795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.169492</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>170</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>1603</td>\n",
       "      <td>59</td>\n",
       "      <td>130.431245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25.613861</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>204</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2587</td>\n",
       "      <td>141</td>\n",
       "      <td>131.186613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29.515625</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>3778</td>\n",
       "      <td>125</td>\n",
       "      <td>131.637631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>37.872340</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>251</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>3560</td>\n",
       "      <td>156</td>\n",
       "      <td>136.712750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.585366</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>157</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>2344</td>\n",
       "      <td>147</td>\n",
       "      <td>146.591620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27.555556</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>334</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>2728</td>\n",
       "      <td>106</td>\n",
       "      <td>155.441595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32.612403</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>354</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>4207</td>\n",
       "      <td>173</td>\n",
       "      <td>130.896080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>39.317460</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>266</td>\n",
       "      <td>SE Marsh</td>\n",
       "      <td>2477</td>\n",
       "      <td>78</td>\n",
       "      <td>132.743837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27.223684</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>239</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>2069</td>\n",
       "      <td>92</td>\n",
       "      <td>138.394649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>37.021739</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>150</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>1703</td>\n",
       "      <td>45</td>\n",
       "      <td>131.709203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28.091837</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>277</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>2753</td>\n",
       "      <td>124</td>\n",
       "      <td>131.220210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Average  Century  Fifty  Four            Name  Runs  Six  Strike Rate\n",
       "0   40.741573        5     21   294        CH Gayle  3626  265   151.209341\n",
       "1   40.140000        3     36   401       DA Warner  4014  160   142.138810\n",
       "2   28.392857        0     17   245        DR Smith  2385  117   135.204082\n",
       "3   29.632653        1     13   239       YK Pathan  2904  147   145.490982\n",
       "4   25.524590        1      6   130         WP Saha  1557   57   130.730479\n",
       "5   34.733333        1      8   104       DA Miller  1563   78   141.447964\n",
       "6   38.164835        3     22   287  AB de Villiers  3473  156   148.165529\n",
       "7   30.847059        2     14   257       SR Watson  2622  122   138.656795\n",
       "8   27.169492        0     13   170        AJ Finch  1603   59   130.431245\n",
       "9   25.613861        0     12   204    Yuvraj Singh  2587  141   131.186613\n",
       "10  29.515625        0     22   377      RV Uthappa  3778  125   131.637631\n",
       "11  37.872340        0     17   251        MS Dhoni  3560  156   136.712750\n",
       "12  28.585366        0     12   157      KA Pollard  2344  147   146.591620\n",
       "13  27.555556        2     16   334        V Sehwag  2728  106   155.441595\n",
       "14  32.612403        1     32   354       RG Sharma  4207  173   130.896080\n",
       "15  39.317460        1     20   266        SE Marsh  2477   78   132.743837\n",
       "16  27.223684        2     11   239    AC Gilchrist  2069   92   138.394649\n",
       "17  37.021739        1      5   150       SPD Smith  1703   45   131.709203\n",
       "18  28.091837        2     13   277     BB McCullum  2753  124   131.220210"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dict_A = {\"Name\":ClassA_name,\"Runs\":ClassA_Runs, \"Strike Rate\":ClassA_SR ,\"Average\":ClassA_avg,\"Century\": ClassA_cent,\"Fifty\": ClassA_fifty,\"Six\": ClassA_six,\"Four\": ClassA_four}\n",
    "batsman_dataframe_A = pd.DataFrame(batsman_dict_A)\n",
    "batsman_dataframe_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix for Class A Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502680</td>\n",
       "      <td>0.469557</td>\n",
       "      <td>0.277737</td>\n",
       "      <td>0.436654</td>\n",
       "      <td>0.377328</td>\n",
       "      <td>0.256072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century</th>\n",
       "      <td>0.502680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329552</td>\n",
       "      <td>0.380185</td>\n",
       "      <td>0.343764</td>\n",
       "      <td>0.525647</td>\n",
       "      <td>0.566521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fifty</th>\n",
       "      <td>0.469557</td>\n",
       "      <td>0.329552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.856935</td>\n",
       "      <td>0.864320</td>\n",
       "      <td>0.581754</td>\n",
       "      <td>0.137611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Four</th>\n",
       "      <td>0.277737</td>\n",
       "      <td>0.380185</td>\n",
       "      <td>0.856935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867993</td>\n",
       "      <td>0.531737</td>\n",
       "      <td>0.194929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>0.436654</td>\n",
       "      <td>0.343764</td>\n",
       "      <td>0.864320</td>\n",
       "      <td>0.867993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.781292</td>\n",
       "      <td>0.223305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Six</th>\n",
       "      <td>0.377328</td>\n",
       "      <td>0.525647</td>\n",
       "      <td>0.581754</td>\n",
       "      <td>0.531737</td>\n",
       "      <td>0.781292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.475118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strike Rate</th>\n",
       "      <td>0.256072</td>\n",
       "      <td>0.566521</td>\n",
       "      <td>0.137611</td>\n",
       "      <td>0.194929</td>\n",
       "      <td>0.223305</td>\n",
       "      <td>0.475118</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Average   Century     Fifty      Four      Runs       Six  \\\n",
       "Average      1.000000  0.502680  0.469557  0.277737  0.436654  0.377328   \n",
       "Century      0.502680  1.000000  0.329552  0.380185  0.343764  0.525647   \n",
       "Fifty        0.469557  0.329552  1.000000  0.856935  0.864320  0.581754   \n",
       "Four         0.277737  0.380185  0.856935  1.000000  0.867993  0.531737   \n",
       "Runs         0.436654  0.343764  0.864320  0.867993  1.000000  0.781292   \n",
       "Six          0.377328  0.525647  0.581754  0.531737  0.781292  1.000000   \n",
       "Strike Rate  0.256072  0.566521  0.137611  0.194929  0.223305  0.475118   \n",
       "\n",
       "             Strike Rate  \n",
       "Average         0.256072  \n",
       "Century         0.566521  \n",
       "Fifty           0.137611  \n",
       "Four            0.194929  \n",
       "Runs            0.223305  \n",
       "Six             0.475118  \n",
       "Strike Rate     1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dataframe_A.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Data Dictionary for Class B players (More Runs and Low Strike rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Name</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.860000</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>123</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>1993</td>\n",
       "      <td>78</td>\n",
       "      <td>124.874687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.953846</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>195</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>1687</td>\n",
       "      <td>27</td>\n",
       "      <td>121.192529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.764706</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>198</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>1977</td>\n",
       "      <td>52</td>\n",
       "      <td>122.642680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.826087</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>296</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "      <td>2334</td>\n",
       "      <td>29</td>\n",
       "      <td>119.815195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.986486</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>121</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>1553</td>\n",
       "      <td>79</td>\n",
       "      <td>118.640183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.156250</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>DPMD Jayawardene</td>\n",
       "      <td>1802</td>\n",
       "      <td>39</td>\n",
       "      <td>123.255814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>1732</td>\n",
       "      <td>57</td>\n",
       "      <td>122.924060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28.397436</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>197</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>2215</td>\n",
       "      <td>64</td>\n",
       "      <td>120.119306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.700935</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>297</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>2322</td>\n",
       "      <td>35</td>\n",
       "      <td>117.213529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27.146067</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>205</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>2416</td>\n",
       "      <td>79</td>\n",
       "      <td>126.161880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28.552941</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>255</td>\n",
       "      <td>JH Kallis</td>\n",
       "      <td>2427</td>\n",
       "      <td>44</td>\n",
       "      <td>109.225923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>37.440678</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>383</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>4418</td>\n",
       "      <td>160</td>\n",
       "      <td>129.826624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31.541985</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>484</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>4132</td>\n",
       "      <td>58</td>\n",
       "      <td>124.607961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29.428571</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>152</td>\n",
       "      <td>MK Tiwary</td>\n",
       "      <td>1648</td>\n",
       "      <td>39</td>\n",
       "      <td>117.295374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.811966</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>286</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>2903</td>\n",
       "      <td>71</td>\n",
       "      <td>125.943601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26.431579</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>237</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>2511</td>\n",
       "      <td>89</td>\n",
       "      <td>123.390663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32.669725</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>401</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>3561</td>\n",
       "      <td>71</td>\n",
       "      <td>121.868583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28.233766</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>269</td>\n",
       "      <td>R Dravid</td>\n",
       "      <td>2174</td>\n",
       "      <td>28</td>\n",
       "      <td>115.515409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33.593407</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>320</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>3057</td>\n",
       "      <td>60</td>\n",
       "      <td>120.591716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Average  Century  Fifty  Four              Name  Runs  Six  Strike Rate\n",
       "0   39.860000        0     14   123         JP Duminy  1993   78   124.874687\n",
       "1   25.953846        0     10   195     KC Sangakkara  1687   27   121.192529\n",
       "2   38.764706        1     15   198        MEK Hussey  1977   52   122.642680\n",
       "3   33.826087        1     13   296      SR Tendulkar  2334   29   119.815195\n",
       "4   20.986486        0      6   121           NV Ojha  1553   79   118.640183\n",
       "5   28.156250        1     10   200  DPMD Jayawardene  1802   39   123.255814\n",
       "6   24.055556        0      0   125         RA Jadeja  1732   57   122.924060\n",
       "7   28.397436        1      9   197         MK Pandey  2215   64   120.119306\n",
       "8   21.700935        0     10   297          PA Patel  2322   35   117.213529\n",
       "9   27.146067        0     14   205         AT Rayudu  2416   79   126.161880\n",
       "10  28.552941        0     17   255         JH Kallis  2427   44   109.225923\n",
       "11  37.440678        4     30   383           V Kohli  4418  160   129.826624\n",
       "12  31.541985        0     35   484         G Gambhir  4132   58   124.607961\n",
       "13  29.428571        0      7   152         MK Tiwary  1648   39   117.295374\n",
       "14  24.811966        0     14   286        KD Karthik  2903   71   125.943601\n",
       "15  26.431579        2     13   237           M Vijay  2511   89   123.390663\n",
       "16  32.669725        0     28   401          S Dhawan  3561   71   121.868583\n",
       "17  28.233766        0     11   269          R Dravid  2174   28   115.515409\n",
       "18  33.593407        1     25   320         AM Rahane  3057   60   120.591716"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dict_B = {\"Name\":ClassB_name,\"Runs\":ClassB_Runs, \"Strike Rate\":ClassB_SR ,\"Average\":ClassB_avg,\"Century\": ClassB_cent,\"Fifty\": ClassB_fifty,\"Six\": ClassB_six,\"Four\": ClassB_four}\n",
    "batsman_dataframe_B = pd.DataFrame(batsman_dict_B)\n",
    "batsman_dataframe_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix for Class B Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407470</td>\n",
       "      <td>0.546154</td>\n",
       "      <td>0.247848</td>\n",
       "      <td>0.377414</td>\n",
       "      <td>0.289301</td>\n",
       "      <td>0.315933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century</th>\n",
       "      <td>0.407470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341871</td>\n",
       "      <td>0.258795</td>\n",
       "      <td>0.463352</td>\n",
       "      <td>0.693932</td>\n",
       "      <td>0.452694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fifty</th>\n",
       "      <td>0.546154</td>\n",
       "      <td>0.341871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872121</td>\n",
       "      <td>0.916141</td>\n",
       "      <td>0.423440</td>\n",
       "      <td>0.314377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Four</th>\n",
       "      <td>0.247848</td>\n",
       "      <td>0.258795</td>\n",
       "      <td>0.872121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901814</td>\n",
       "      <td>0.202608</td>\n",
       "      <td>0.172805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>0.377414</td>\n",
       "      <td>0.463352</td>\n",
       "      <td>0.916141</td>\n",
       "      <td>0.901814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579498</td>\n",
       "      <td>0.421043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Six</th>\n",
       "      <td>0.289301</td>\n",
       "      <td>0.693932</td>\n",
       "      <td>0.423440</td>\n",
       "      <td>0.202608</td>\n",
       "      <td>0.579498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strike Rate</th>\n",
       "      <td>0.315933</td>\n",
       "      <td>0.452694</td>\n",
       "      <td>0.314377</td>\n",
       "      <td>0.172805</td>\n",
       "      <td>0.421043</td>\n",
       "      <td>0.639518</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Average   Century     Fifty      Four      Runs       Six  \\\n",
       "Average      1.000000  0.407470  0.546154  0.247848  0.377414  0.289301   \n",
       "Century      0.407470  1.000000  0.341871  0.258795  0.463352  0.693932   \n",
       "Fifty        0.546154  0.341871  1.000000  0.872121  0.916141  0.423440   \n",
       "Four         0.247848  0.258795  0.872121  1.000000  0.901814  0.202608   \n",
       "Runs         0.377414  0.463352  0.916141  0.901814  1.000000  0.579498   \n",
       "Six          0.289301  0.693932  0.423440  0.202608  0.579498  1.000000   \n",
       "Strike Rate  0.315933  0.452694  0.314377  0.172805  0.421043  0.639518   \n",
       "\n",
       "             Strike Rate  \n",
       "Average         0.315933  \n",
       "Century         0.452694  \n",
       "Fifty           0.314377  \n",
       "Four            0.172805  \n",
       "Runs            0.421043  \n",
       "Six             0.639518  \n",
       "Strike Rate     1.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dataframe_B.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Data Dictionary for Class C player (Less Runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Name</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.709677</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>121</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>1107</td>\n",
       "      <td>44</td>\n",
       "      <td>137.515528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AS Rajpoot</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>46.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>122.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>CJ Ferguson</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>83.760684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>MJ Clarke</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>104.255319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>VY Mahesh</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>55.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BA Bhatt</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DP Nannes</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.074074</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>74</td>\n",
       "      <td>A Symonds</td>\n",
       "      <td>974</td>\n",
       "      <td>41</td>\n",
       "      <td>129.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>MN van Wyk</td>\n",
       "      <td>167</td>\n",
       "      <td>1</td>\n",
       "      <td>126.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>53.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>H Das</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>88</td>\n",
       "      <td>5</td>\n",
       "      <td>88.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>VRV Singh</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>L Ronchi</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.116279</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>1295</td>\n",
       "      <td>38</td>\n",
       "      <td>127.964427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>S Rana</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>112.345679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>233.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>606</td>\n",
       "      <td>28</td>\n",
       "      <td>128.118393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AR Bawne</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>JM Kemp</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>108.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.166667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>MJ Lumb</td>\n",
       "      <td>278</td>\n",
       "      <td>6</td>\n",
       "      <td>143.298969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington Sundar</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>SM Katich</td>\n",
       "      <td>241</td>\n",
       "      <td>8</td>\n",
       "      <td>129.569892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>45.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>AC Voges</td>\n",
       "      <td>181</td>\n",
       "      <td>3</td>\n",
       "      <td>126.573427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>WA Mota</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>74.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>122.580645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>T Thushara</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>18.125000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>S Vidyut</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>133.027523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>RV Gomez</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>98.039216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>6.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NJ Maddinson</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>95.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Swapnil Singh</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>10.125000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>110.958904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A Mukund</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>86.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>SK Trivedi</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>71.186441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>57.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>20.421053</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>Azhar Mahmood</td>\n",
       "      <td>388</td>\n",
       "      <td>13</td>\n",
       "      <td>128.052805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joginder Sharma</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>F Behardien</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>107.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>29.040000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>726</td>\n",
       "      <td>21</td>\n",
       "      <td>132.240437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>34.285714</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>480</td>\n",
       "      <td>20</td>\n",
       "      <td>158.415842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>10.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>KB Arun Karthik</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>8.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>S Chanderpaul</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>80.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>MM Patel</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>95.121951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>P Awana</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>DS Lehmann</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>132.167832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>7.875000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>MJ McClenaghan</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>143.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>11.454545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>M Morkel</td>\n",
       "      <td>126</td>\n",
       "      <td>5</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TM Srivastava</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>24.538462</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>95</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>957</td>\n",
       "      <td>35</td>\n",
       "      <td>132.732316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BAW Mendis</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>T Kohli</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>57.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>13.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>RE Levi</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>113.698630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>854</td>\n",
       "      <td>34</td>\n",
       "      <td>121.134752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mohammad Ashraful</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>18.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>K Rabada</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>85.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>MA Khote</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>109.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Average  Century  Fifty  Four               Name  Runs  Six  \\\n",
       "0    35.709677        0      8   121          ML Hayden  1107   44   \n",
       "1     6.000000        0      0     1         AS Rajpoot     6    0   \n",
       "2     5.500000        0      0     0        Rashid Khan    11    1   \n",
       "3    16.333333        0      0     9        CJ Ferguson    98    0   \n",
       "4    16.333333        0      0    12          MJ Clarke    98    0   \n",
       "5     7.500000        0      0     0          VY Mahesh    15    0   \n",
       "6     6.000000        0      0     0           BA Bhatt     6    1   \n",
       "7     4.000000        0      0     0          DP Nannes     4    0   \n",
       "8    36.074074        1      5    74          A Symonds   974   41   \n",
       "9    55.666667        0      1    19         MN van Wyk   167    1   \n",
       "10    5.000000        0      0     0          YS Chahal    15    0   \n",
       "11    2.000000        0      0     0              H Das     2    0   \n",
       "12    5.500000        0      0     6         SL Malinga    88    5   \n",
       "13    2.000000        0      0     1          VRV Singh     4    0   \n",
       "14    6.800000        0      0     6           L Ronchi    34    1   \n",
       "15   30.116279        0      8   110       F du Plessis  1295   38   \n",
       "16   22.750000        0      0     9             S Rana    91    1   \n",
       "17   14.000000        0      0     0          DL Chahar    14    2   \n",
       "18   20.200000        0      0    34           AR Patel   606   28   \n",
       "19    0.000000        0      0     1           AR Bawne    12    0   \n",
       "20   13.000000        0      0     1            JM Kemp    26    1   \n",
       "21   23.166667        0      1    45            MJ Lumb   278    6   \n",
       "22    9.000000        0      0     0  Washington Sundar     9    0   \n",
       "23   24.100000        0      2    26          SM Katich   241    8   \n",
       "24   45.250000        0      0    15           AC Voges   181    3   \n",
       "25    9.333333        0      0     2            WA Mota    56    0   \n",
       "26    2.000000        0      0     0      Mohammad Nabi     2    0   \n",
       "27    7.600000        0      0     4          JO Holder    38    2   \n",
       "28    6.000000        0      0     0         T Thushara    12    0   \n",
       "29   18.125000        0      1    21           S Vidyut   145    3   \n",
       "..         ...      ...    ...   ...                ...   ...  ...   \n",
       "392  10.000000        0      0     5           RV Gomez    50    1   \n",
       "393   6.666667        0      0     4       NJ Maddinson    20    0   \n",
       "394   4.000000        0      0     0      Swapnil Singh    12    0   \n",
       "395  10.125000        0      0     2         WPUJC Vaas    81    3   \n",
       "396   9.500000        0      0     1           A Mukund    19    0   \n",
       "397   6.000000        0      0     3         SK Trivedi    42    1   \n",
       "398   0.000000        0      0     0         A Chandila     4    0   \n",
       "399  20.421053        0      2    39      Azhar Mahmood   388   13   \n",
       "400   9.000000        0      0     1    Joginder Sharma    36    2   \n",
       "401   7.000000        0      0     2        F Behardien    14    0   \n",
       "402  29.040000        1      5    90          Q de Kock   726   21   \n",
       "403  34.285714        0      1    46          KH Pandya   480   20   \n",
       "404  10.200000        0      0     4    KB Arun Karthik    51    1   \n",
       "405   8.333333        0      0     4      S Chanderpaul    25    0   \n",
       "406   6.500000        0      0     5           MM Patel    39    0   \n",
       "407   2.500000        0      0     0            P Awana     5    0   \n",
       "408   9.000000        0      0     3         DS Lehmann    18    0   \n",
       "409  21.000000        0      1    20         MJ Guptill   189    8   \n",
       "410   7.875000        0      0     2     MJ McClenaghan    63    6   \n",
       "411  11.454545        0      0    11           M Morkel   126    5   \n",
       "412   2.333333        0      0     1          SN Thakur     7    0   \n",
       "413   8.000000        0      0     0      TM Srivastava     8    0   \n",
       "414  24.538462        0      3    95            M Vohra   957   35   \n",
       "415   1.500000        0      0     0         BAW Mendis     3    0   \n",
       "416   3.666667        0      0     0            T Kohli    11    1   \n",
       "417  13.833333        0      1    10            RE Levi    83    4   \n",
       "418  21.350000        0      4    72         EJG Morgan   854   34   \n",
       "419   2.000000        0      0     0  Mohammad Ashraful     2    0   \n",
       "420  18.333333        0      0     5           K Rabada    55    1   \n",
       "421  12.000000        0      0     2           MA Khote    24    1   \n",
       "\n",
       "     Strike Rate  \n",
       "0     137.515528  \n",
       "1      46.153846  \n",
       "2     122.222222  \n",
       "3      83.760684  \n",
       "4     104.255319  \n",
       "5      55.555556  \n",
       "6      85.714286  \n",
       "7      30.769231  \n",
       "8     129.866667  \n",
       "9     126.515152  \n",
       "10     53.571429  \n",
       "11     40.000000  \n",
       "12     88.888889  \n",
       "13    133.333333  \n",
       "14    100.000000  \n",
       "15    127.964427  \n",
       "16    112.345679  \n",
       "17    233.333333  \n",
       "18    128.118393  \n",
       "19    100.000000  \n",
       "20    108.333333  \n",
       "21    143.298969  \n",
       "22     75.000000  \n",
       "23    129.569892  \n",
       "24    126.573427  \n",
       "25     74.666667  \n",
       "26     66.666667  \n",
       "27    122.580645  \n",
       "28     66.666667  \n",
       "29    133.027523  \n",
       "..           ...  \n",
       "392    98.039216  \n",
       "393    95.238095  \n",
       "394    60.000000  \n",
       "395   110.958904  \n",
       "396    86.363636  \n",
       "397    71.186441  \n",
       "398    57.142857  \n",
       "399   128.052805  \n",
       "400   120.000000  \n",
       "401   107.692308  \n",
       "402   132.240437  \n",
       "403   158.415842  \n",
       "404   100.000000  \n",
       "405    80.645161  \n",
       "406    95.121951  \n",
       "407    35.714286  \n",
       "408   100.000000  \n",
       "409   132.167832  \n",
       "410   143.181818  \n",
       "411   140.000000  \n",
       "412    77.777778  \n",
       "413    72.727273  \n",
       "414   132.732316  \n",
       "415    50.000000  \n",
       "416    57.894737  \n",
       "417   113.698630  \n",
       "418   121.134752  \n",
       "419    20.000000  \n",
       "420    85.937500  \n",
       "421   109.090909  \n",
       "\n",
       "[422 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dict_C = {\"Name\":ClassC_name,\"Runs\":ClassC_Runs, \"Strike Rate\":ClassC_SR ,\"Average\":ClassC_avg,\"Century\": ClassC_cent,\"Fifty\": ClassC_fifty,\"Six\": ClassC_six,\"Four\": ClassC_four}\n",
    "batsman_dataframe_C = pd.DataFrame(batsman_dict_C)\n",
    "batsman_dataframe_C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix for Class C Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Century</th>\n",
       "      <th>Fifty</th>\n",
       "      <th>Four</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Six</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274762</td>\n",
       "      <td>0.534521</td>\n",
       "      <td>0.578062</td>\n",
       "      <td>0.583429</td>\n",
       "      <td>0.544312</td>\n",
       "      <td>0.577449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century</th>\n",
       "      <td>0.274762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326655</td>\n",
       "      <td>0.304050</td>\n",
       "      <td>0.286484</td>\n",
       "      <td>0.304109</td>\n",
       "      <td>0.118608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fifty</th>\n",
       "      <td>0.534521</td>\n",
       "      <td>0.326655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895402</td>\n",
       "      <td>0.865931</td>\n",
       "      <td>0.776538</td>\n",
       "      <td>0.270036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Four</th>\n",
       "      <td>0.578062</td>\n",
       "      <td>0.304050</td>\n",
       "      <td>0.895402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971351</td>\n",
       "      <td>0.854222</td>\n",
       "      <td>0.353785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>0.583429</td>\n",
       "      <td>0.286484</td>\n",
       "      <td>0.865931</td>\n",
       "      <td>0.971351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931574</td>\n",
       "      <td>0.364954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Six</th>\n",
       "      <td>0.544312</td>\n",
       "      <td>0.304109</td>\n",
       "      <td>0.776538</td>\n",
       "      <td>0.854222</td>\n",
       "      <td>0.931574</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strike Rate</th>\n",
       "      <td>0.577449</td>\n",
       "      <td>0.118608</td>\n",
       "      <td>0.270036</td>\n",
       "      <td>0.353785</td>\n",
       "      <td>0.364954</td>\n",
       "      <td>0.401181</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Average   Century     Fifty      Four      Runs       Six  \\\n",
       "Average      1.000000  0.274762  0.534521  0.578062  0.583429  0.544312   \n",
       "Century      0.274762  1.000000  0.326655  0.304050  0.286484  0.304109   \n",
       "Fifty        0.534521  0.326655  1.000000  0.895402  0.865931  0.776538   \n",
       "Four         0.578062  0.304050  0.895402  1.000000  0.971351  0.854222   \n",
       "Runs         0.583429  0.286484  0.865931  0.971351  1.000000  0.931574   \n",
       "Six          0.544312  0.304109  0.776538  0.854222  0.931574  1.000000   \n",
       "Strike Rate  0.577449  0.118608  0.270036  0.353785  0.364954  0.401181   \n",
       "\n",
       "             Strike Rate  \n",
       "Average         0.577449  \n",
       "Century         0.118608  \n",
       "Fifty           0.270036  \n",
       "Four            0.353785  \n",
       "Runs            0.364954  \n",
       "Six             0.401181  \n",
       "Strike Rate     1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_dataframe_C.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Runs Scored by the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1177aa358>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.arange(461)\n",
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index,  Players_Runs)\n",
    "plt.suptitle(\"Runs Scored\", fontsize=20)\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Players_Runs))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Average of the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117937668>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index, P_avg)\n",
    "plt.suptitle(\"Average\", fontsize=20)\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(P_avg))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Strike Rate of the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11ee4c860>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index,  Players_SR)\n",
    "plt.suptitle(\"Strike Rate\", fontsize=20)\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Players_SR))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Century Scored by the Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f6f1780>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index,  Player_century)\n",
    "plt.suptitle(\"Century Scored\", fontsize=20)\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Player_century))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Half Century Scored by the Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11fc10a90>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "ax = plt.bar(index,  Player_fifty)\n",
    "\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Player_fifty))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Six Hitted by the Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1204a8668>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index,  Player_six)\n",
    "\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Player_six))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph for Four Hitted by the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1209390b8>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "ax = plt.bar(index,  Player_four)\n",
    "\n",
    "mean = np.empty(len(player_list))\n",
    "mean.fill(np.mean(Player_four))\n",
    "plt.plot(index, mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Runs Scored vs Strike Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1218919b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs Scored vs Strike Rate\", fontsize=20)\n",
    "plt.scatter(Players_Runs, Players_SR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Runs Scored vs Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x121d8fa58>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs Scored vs Average\", fontsize=20)\n",
    "plt.scatter(Players_Runs, P_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to generate eqautaion of Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "def y(x, m, b):\n",
    "    return m * x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Runs Scored vs Six Hitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation of Line : 0.036264969491593635  * X +  -0.332469379565149\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(y, Players_Runs, Player_six)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs Scored vs Six Hitted\", fontsize=20)\n",
    "plt.xlabel('Runs', fontsize=18)\n",
    "plt.ylabel('Sixes', fontsize=16)\n",
    "plt.scatter(Players_Runs, Player_six)\n",
    "lines = np.arange(max(Players_Runs))\n",
    "abline_values = [popt[0] * i + popt[1] for i in lines]\n",
    "plt.plot(lines,abline_values,'g')\n",
    "print(\"Equation of Line :\",popt[0],\" * X + \",popt[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Scatter Plot of Runs Scored vs Four Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation of Line : 0.0949201461247442  * X +  -0.9177778158875125\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(y, Players_Runs, Player_four)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs Scored vs Four Hitted\", fontsize=20)\n",
    "plt.xlabel('Runs', fontsize=18)\n",
    "plt.ylabel('Fours', fontsize=16)\n",
    "plt.scatter(Players_Runs,Player_four)\n",
    "lines = np.arange(max(Players_Runs))\n",
    "abline_values = [popt[0] * i + popt[1] for i in lines]\n",
    "plt.plot(lines,abline_values,'g')\n",
    "print(\"Equation of Line :\",popt[0],\" * X + \",popt[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Runs Scored vs Total Boundry Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation of Line : 0.1311851156185173  * X +  -1.2502471954504788\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(y, Players_Runs, np.array(Player_four)+np.array(Player_six))\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Runs Scored vs Total Boundry Scored\", fontsize=20)\n",
    "plt.xlabel('Runs', fontsize=18)\n",
    "plt.ylabel('Boundries', fontsize=16)\n",
    "plt.scatter(Players_Runs,np.array(Player_four)+np.array(Player_six))\n",
    "lines = np.arange(max(Players_Runs))\n",
    "abline_values = [popt[0] * i + popt[1] for i in lines]\n",
    "plt.plot(lines,abline_values,'g')\n",
    "print(\"Equation of Line :\",popt[0],\" * X + \",popt[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Six Hitted vs Four Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation of Line : 0.3482921610104425  * X +  1.2647882985444319\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(y,Player_four, Player_six)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\" Six Hitted vs Four Scored\", fontsize=20)\n",
    "plt.xlabel('Four', fontsize=18)\n",
    "plt.ylabel('Sixes', fontsize=16)\n",
    "plt.scatter(Player_four, Player_six)\n",
    "lines = np.arange(max(Player_four))\n",
    "abline_values = [popt[0] * i  for i in lines]\n",
    "plt.plot(lines,abline_values,'g')\n",
    "print(\"Equation of Line :\",popt[0],\" * X + \",popt[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the TOP-10 Batsman"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for printing in markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printmd(string):\n",
    "    display(Markdown(string))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Runs Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**SK Raina** : 4540"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Kohli** : 4418"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RG Sharma** : 4207"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**G Gambhir** : 4132"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 4014"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RV Uthappa** : 3778"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**CH Gayle** : 3626"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**S Dhawan** : 3561"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**MS Dhoni** : 3560"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AB de Villiers** : 3473"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_runs = sorted(range(len(Players_Runs)), key=lambda i: Players_Runs[i])[-10:]\n",
    "\n",
    "for value in top_runs[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str(Players_Runs[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Strike Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**DL Chahar** : 233.33"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Umar Gul** : 205.26"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RS Sodhi** : 200.00"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**BCJ Cutting** : 187.88"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**CR Brathwaite** : 179.25"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AJ Tye** : 176.67"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**LJ Wright** : 176.67"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shahid Afridi** : 176.09"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**I Malhotra** : 175.00"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**SN Khan** : 173.53"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_sr = sorted(range(len(Players_SR)), key=lambda i: Players_SR[i])[-10:]\n",
    "\n",
    "for value in top_sr[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str('{0:.2f}'.format(Players_SR[value])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**MN van Wyk** : 55.67"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Shankar** : 50.50"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**J Suchith** : 48.00"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AC Voges** : 45.25"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**HM Amla** : 44.38"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Iqbal Abdulla** : 44.00"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**CH Gayle** : 40.74"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**PD Collingwood** : 40.60"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 40.14"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**LMP Simmons** : 39.96"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_avg = sorted(range(len(P_avg)), key=lambda i: P_avg[i])[-10:]\n",
    "\n",
    "for value in top_avg[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str('{0:.2f}'.format(P_avg[value])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Century Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**CH Gayle** : 5"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Kohli** : 4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AB de Villiers** : 3"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 3"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**BB McCullum** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AC Gilchrist** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**M Vijay** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Sehwag** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**SR Watson** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**HM Amla** : 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_century = sorted(range(len(Player_century)), key=lambda i: Player_century[i])[-10:]\n",
    "\n",
    "for value in top_century[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str(Player_century[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Half-Century Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 36"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**G Gambhir** : 35"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RG Sharma** : 32"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**SK Raina** : 31"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Kohli** : 30"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**S Dhawan** : 28"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AM Rahane** : 25"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RV Uthappa** : 22"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AB de Villiers** : 22"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**CH Gayle** : 21"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_fifty = sorted(range(len(Player_fifty)), key=lambda i: Player_fifty[i])[-10:]\n",
    "\n",
    "for value in top_fifty[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str(Player_fifty[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Six Hitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**CH Gayle** : 265"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**SK Raina** : 173"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RG Sharma** : 173"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Kohli** : 160"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 160"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**MS Dhoni** : 156"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AB de Villiers** : 156"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**KA Pollard** : 147"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**YK Pathan** : 147"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Yuvraj Singh** : 141"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_six = sorted(range(len(Player_six)), key=lambda i: Player_six[i])[-10:]\n",
    "\n",
    "for value in top_six[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str(Player_six[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOP-10 Batsman based on Four Scored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**G Gambhir** : 484"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**SK Raina** : 402"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**S Dhawan** : 401"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**DA Warner** : 401"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Kohli** : 383"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RV Uthappa** : 377"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**RG Sharma** : 354"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**V Sehwag** : 334"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AM Rahane** : 320"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**PA Patel** : 297"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_four = sorted(range(len(Player_four)), key=lambda i: Player_four[i])[-10:]\n",
    "\n",
    "for value in top_four[::-1]:\n",
    "    printmd('**' + player_list[value] + '** : ' + str(Player_four[value]))"
   ]
  }
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