base on NBA sports betting using machine learning # NBA Sports Betting Using Machine Learning 🏀 <img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/output.png" width="1010" height="292" /> 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. ## Packages Used 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 ## Usage <img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/Expected_value.png" width="1010" height="424" /> Make sure all packages above are installed. ```bash $ 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=fanduel ``` Odds 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 ## Flask Web App <img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/Flask-App.png" width="922" height="580" /> 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 ``` ## Getting new data and training models ``` # 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 ``` ## Contributing All contributions welcomed and encouraged. ", Assign "at most 3 tags" to the expected json: {"id":"4145","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"