A machine learning AI used to predict the winners and under/overs of NBA games. Takes all team data from the 2007-08 season to current season, matched with odds of those games, using a neural network to predict winning bets for today's games. Achieves ~69% accuracy on money lines and ~55% on under/overs. Outputs expected value for teams money lines to provide better insight. The fraction of your bankroll to bet based on the Kelly Criterion is also outputted. Note that a popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.
Use Python 3.11. In particular the packages/libraries used are...
- Tensorflow - Machine learning library
- XGBoost - Gradient boosting framework
- Numpy - Package for scientific computing in Python
- Pandas - Data manipulation and analysis
- Colorama - Color text output
- Tqdm - Progress bars
- Requests - Http library
- Scikit_learn - Machine learning library
Make sure all packages above are installed.
$ git clone https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting.git
$ cd NBA-Machine-Learning-Sports-Betting
$ pip3 install -r requirements.txt
$ python3 main.py -xgb -odds=fanduelOdds data will be automatically fetched from sbrodds if the -odds option is provided with a sportsbook. Options include: fanduel, draftkings, betmgm, pointsbet, caesars, wynn, bet_rivers_ny
If -odds is not given, enter the under/over and odds for today's games manually after starting the script.
Optionally, you can add '-kc' as a command line argument to see the recommended fraction of your bankroll to wager based on the model's edge
This repo also includes a small Flask application to help view the data from this tool in the browser. To run it:
cd Flask
flask --debug run
# Create dataset with the latest data for 2023-24 season
cd src/Process-Data
python -m Get_Data
python -m Get_Odds_Data
python -m Create_Games
# Train models
cd ../Train-Models
python -m XGBoost_Model_ML
python -m XGBoost_Model_UO
All contributions welcomed and encouraged.