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nba-predictions

A project to predict the outcome of NBA matches using Machine Learning.

Data Description

Note that the columns with column names as Team1COLNAME and Team2COLNAME correspond to the same COLNAME description, and the two columns refer to the two teams playing that game. Also, note that the statistics were taken on a month-wise basis from Basketball Reference and the official NBA Website.

Column Name Description
AWAY The Away team, this team is not playing on their home court
Pts The number of points scored by a team
HOME The Home team, this team is playing on their home court
RANK The month-wise ranking of the team
GP Number of Games Played
W Number of Wins
L Number of Losses
MIN Number of Minutes Played
OFFRTG Offensive Rating
DEFRTG Defensive Rating
NETRTG Net Rating
AST% Assist Percentage
AST/TO Assist to Turnover Ratio
AST_RATIO Assist Ratio
OREB% Offensive Rebound Percentage
DREB% Defensive Rebound Percentage
REB% Rebound Percentage
TOV% Turnover Percentage
EFG% Effective Field Goal Percentage
TS% True Shooting Percentage
PACE Pace
PIE Player Impact Estimate

Directory Description

Contains all the raw and preprocessed datasets.

Contains the Chrome webdriver used for data scraping.

Python File to scrape the relevant data from the internet.

Jupyter Notebook used for data preprocessing.

Jupyter Notebook used to fit simple Machine Learning models to the data.

Resources

nbastats - https://www.nba.com/stats/teams/traditional/?sort=W_PCT&dir=-1&Season=2019-20&SeasonType=Regular%20Season&Month=1

bbref - https://www.basketball-reference.com/leagues/NBA_2020_games.html

pilot - https://towardsdatascience.com/building-my-first-machine-learning-model-nba-prediction-algorithm-dee5c5bc4cc1

scraping data - https://www.youtube.com/playlist?list=PLzMcBGfZo4-n40rB1XaJ0ak1bemvlqumQ

all nba team prediction model - https://www.kaggle.com/felixdonovan/predicting-the-all-nba-teams

nba all star league prediction model - https://towardsdatascience.com/using-machine-learning-to-predict-nba-all-stars-part-2-modelling-a66e6b534998

evaluating player similarity - https://towardsdatascience.com/which-nba-players-are-most-similar-machine-learning-provides-the-answers-r-project-b903f9b2fe1f

player ranking in soccer - https://arxiv.org/pdf/1802.04987.pdf

predict strategy of a team - https://www.mdpi.com/2076-3417/10/1/24/htm

selecting a team to beat a given opponent - https://thesai.org/Downloads/Volume8No8/Paper_59-Automated_Player_Selection_for_a_Sports_Team.pdf

use individual player stats to come up with the team stats and use the pilot model to predict the winner?

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