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mm2_old.py
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mm2_old.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 21 08:10:23 2019
@author: Jason
"""
import requests
import pandas as pd
import time
import bs4
import numpy as np
from tqdm import tqdm
def run(current_year, team_stats_2019, school_links):
seed_list = [1,16,8,9,5,12,4,13,6,11,3,14,7,10,2,15]
seasons_list = [current_year]
tourny_results_2019 = pd.DataFrame()
for season in seasons_list:
url = 'https://www.sports-reference.com/cbb/postseason/%s-ncaa.html' %(season)
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36'}
#time.sleep(5)
response = requests.get(url, headers=headers)
soup = bs4.BeautifulSoup(response.text, 'html.parser')
four_regions = soup.find('div', {'data-controls':'#brackets'}).find_all('a')
regions = [ a.text.replace(' ','').replace('.','').lower() for a in four_regions ]
#regions[-1] = 'national'
regions = regions[:4]
playInTeamList = []
for region in regions:
##################
#region = regions[0]
bracket = soup.find_all('div', {'id':region})[0]
rounds = bracket.find_all('div',{'class':'round'})
#for r in rounds:
#############
playIn = bracket.find('p')
playInSeed = playIn.find_all('strong')[-1].text
playInTeamNames_alpha = playIn.find_all('a', href=True)
playInTeamNames = []
for x in playInTeamNames_alpha:
if 'boxscores' not in x['href']:
playInTeamNames.append(school_links[x['href']])
playInTeamNames = '|'.join(playInTeamNames)
playInTeamList.append(playInTeamNames)
r = rounds[0]
games = r.find_all('div')
comments = r.find_all(string=lambda text:isinstance(text,bs4.Comment))
processPlayInTeams = False
idx = 0
for comment in comments:
if 'game' in comment:
game = comment.parent
teams = game.find_all('div')
try:
team_a = teams[0]
team_a_name = school_links[team_a.find('a')['href']]
team_a_seed = team_a.find('span').text
if team_a_seed == '':
team_a_seed = str(seed_list[idx*2])
except:
#team_a_name = 'N/A'
team_a_name = playInTeamNames
#team_a_seed = str(seed_list[idx*2])
team_a_seed = playInSeed
processPlayInTeams = True
try:
team_b = teams[1]
team_b_name = school_links[team_b.find('a')['href']]
team_b_seed = team_b.find('span').text
if team_b_seed == '':
team_b_seed = str(seed_list[(idx*2)+1])
except:
#team_b_name = 'N/A'
team_b_name = playInTeamNames
#team_b_seed = str(seed_list[(idx*2)+1])
team_b_seed = playInSeed
processPlayInTeams = True
temp_df = pd.DataFrame([[team_a_seed, team_a_name, team_b_seed, team_b_name, season]], columns=['School_Seed','School','School_Opp_Seed','School_Opp', 'Season'])
#tourny_results_2019 = tourny_results_2019.append(temp_df)
tourny_results_2019 = pd.concat([tourny_results_2019, temp_df])
print ('%s: %s: %-20s %s: %s' %(season, team_a_seed, team_a_name, team_b_seed, team_b_name))
idx+=1
tourny_results_2019 = tourny_results_2019.reset_index(drop=True)
# AVERAGE OUT PLAYIN TEAMS STATS
playIn_team_stats_2019 = pd.DataFrame()
if processPlayInTeams == True:
for playInTeams_temp in playInTeamList:
teamList = playInTeams_temp.split('|')
temp_df = team_stats_2019[team_stats_2019['School'].isin(teamList)]
for col in temp_df.columns:
try:
temp_df[col] = temp_df[col].astype(float)
except:
pass
averageDf = pd.DataFrame(temp_df.mean()).T
averageDf['School'] = playInTeams_temp
averageDf = averageDf[team_stats_2019.columns]
#playIn_team_stats_2019 = playIn_team_stats_2019.append(averageDf)
playIn_team_stats_2019 = pd.concat([playIn_team_stats_2019, averageDf])
#team_stats_2019 = team_stats_2019.append(playIn_team_stats_2019).reset_index(drop=True)
team_stats_2019 = pd.concat([team_stats_2019, playIn_team_stats_2019])
team_stats_2019 = team_stats_2019.reset_index(drop=True)
to_predict_df = tourny_results_2019.merge(team_stats_2019, how='left', left_on = ['School', 'Season'], right_on = ['School','Season'])
to_predict_df = to_predict_df.merge(team_stats_2019, how='left', left_on = ['School_Opp', 'Season'], right_on = ['School','Season'], suffixes = ("","_Opp"))
to_predict_df.rename(columns={'Points_Opp': 'Points_Allowed'}, inplace=True)
to_predict_df = to_predict_df.loc[:, ~to_predict_df.columns.duplicated()]
to_predict_df['Outcome'] = None
to_predict_df['School_Seed'] = to_predict_df['School_Seed'].astype(int)
to_predict_df['School_Opp_Seed'] = to_predict_df['School_Opp_Seed'].astype(int)
return to_predict_df, team_stats_2019