NBA Machine Learning and Market Analysis Tools 🏀

Features

• 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

Game Analysis

• 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

Model Training

• 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

Performance Tracking

• Win/Loss percentage
• Spread accuracy
• Margin-based evaluations
• Profit/loss calculations

Data Features

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

Data Feature Correlation:

h : home, v : visitor, w : win, l : loss

Live Server:

FireBet.ai

Setup and Development

Step 1: Clone the Repository

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

Step 2: Set Up a Virtual Environment

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

Step 4: Install Dependencies

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

Step 5: Start the Development Server

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.

http://localhost:8000/login/

Contributing

Open issues and pull requests welcome at GitHub repository

Author/contact:

Neal Mick