
Sports-Betting-ML-Tools-NBA
NBA Machine Learning and Market Analysis Tools
Stars: 62

Sports-Betting-ML-Tools-NBA is a repository containing machine learning and market analysis tools for NBA games. It features a game prediction model trained on 20,000+ games with 500+ data points per game, pre-game analysis with player stats, injuries, and Vegas odds, custom model training with configurable parameters, real-time score updates, and performance tracking. Users can analyze player stats, remove injured players, check Vegas odds and injury reports, review last game performance, and generate game score predictions. The repository also allows users to configure model training parameters, monitor training via Tensorboard, track performance metrics like win/loss percentage, spread accuracy, and profit/loss calculations, and access core statistics per player and team metrics.
README:
- Game prediction model trained on 20,000+ games with 500+ data points per game
- Pre-game analysis with player stats, injuries, and Vegas odds
- Custom model training with configurable parameters
- Real-time score updates and performance tracking
- Profile statistics for prediction accuracy and ROI
- View and edit player stats
- Remove injured players
- Check Vegas odds and injury reports
- Review last game performance
- Generate game score predictions
https://github.com/user-attachments/assets/a481faa3-9859-4a18-bbce-7d8ddfcbd7dd
- Configure layers, neurons, batch size
- Set activation functions and optimizers
- Enable early stopping and regularization
- Monitor training via Tensorboard
https://github.com/user-attachments/assets/dfbc7233-5fd7-4198-98d6-8e3f18d51347
- Win/Loss percentage
- Spread accuracy
- Margin-based evaluations
- Profit/loss calculations
Core statistics tracked per player:
- Shooting: FG%, 3P%, FT%
- Scoring: Points, assists
- Defense: Blocks, steals, rebounds
- Other: Minutes, fouls, turnovers
Team metrics:
- Win/loss records
- Recent performance
- Point spreads
- Historical matchups
h : home, v : visitor, w : win, l : loss
To begin, you need to clone the repository to your local machine. Open your terminal and run the following command:
git clone https://github.com/nealmick/Sports-Betting-ML-Tools-NBA
Next, navigate to the project directory and create a virtual environment. This will isolate the project's dependencies from your system-wide Python installation. Run the following command:
python3 -m venv env
source env/bin/activate
With the virtual environment activated, you can now install the project dependencies. The required packages are listed in the requirements.txt file. Run the following command to install them:
pip3 install -r requirements.txt
Now that you have completed all the setup steps, you can start the development server. Run the following command:
python3 manage.py runserver
Allow the server to start, 1-3 minutes, then navigate to the login url and use demo account.
Open issues and pull requests welcome at GitHub repository
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