https://github.com/kyleskom/nba-machine-learning-sports-betting

NBA sports betting using machine learning
https://github.com/kyleskom/nba-machine-learning-sports-betting

ai data-science deep-learning gambling gpt keras llm machine-learning nba nba-analytics nba-prediction neural-network python sports sports-analytics sports-betting sports-data tensorflow

Last synced: 5 days ago
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NBA sports betting using machine learning

• Host: GitHub
• URL: https://github.com/kyleskom/nba-machine-learning-sports-betting
• Owner: kyleskom
• Created: 2019-11-29T18:52:26.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-12-21T22:22:00.000Z (about 1 month ago)
• Last Synced: 2025-01-09T21:58:26.347Z (12 days ago)
• Topics: ai, data-science, deep-learning, gambling, gpt, keras, llm, machine-learning, nba, nba-analytics, nba-prediction, neural-network, python, sports, sports-analytics, sports-betting, sports-data, tensorflow
• Language: Python
• Homepage:
• Size: 416 MB
• Stars: 1,292
• Watchers: 82
• Forks: 457
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# NBA Sports Betting Using Machine Learning 🏀



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



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



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.