Welcome to the world of NBA sports betting, where we apply cutting-edge machine learning techniques to make informed betting decisions. This powerful AI system predicts winners and under/over outcomes for NBA games with substantial accuracy, leveraging data from the 2007-08 season to the current dates. With an impressive ~69% accuracy on money lines and ~55% on under/overs, this tool can significantly boost your betting edge.

Understanding the Technology Behind the Predictions

This system functions like a seasoned coach analyzing the stats and habits of both teams. Just as a coach examines performance metrics, our AI utilizes a neural network trained on vast datasets to determine the most likely outcomes for today’s matches.

Consider the prediction model as a finely tuned sports car. It uses various components (data from past seasons, betting odds, and performance metrics) to reach its destination (the outcome of the game). Here’s a breakdown of how the various parts work together:

• Tensorflow: Like the engine of the car, it’s the core machine learning library that powers our neural networks.
• XGBoost: Think of this as a turbocharger that significantly boosts the performance of our model by refining predictions through gradient boosting.
• Numpy: The wheels of the vehicle, essential for scientific computations and data manipulations.
• Pandas: This is akin to the dashboard displaying critical data needed for analysis.
• Colorama: This adds flair to our output, similar to the car’s interior lights that brighten the experience.
• Tqdm: Acts like speedometers, providing progress bars to track the model’s training process.
• Requests: The communication systems allowing us to fetch real-time data.
• Scikit_learn: Offers a toolkit for additional machine learning techniques, complementing what Tensorflow provides.

Getting Started with NBA Sports Betting AI

To dive into this tool, follow these steps to set it up:

1. Ensure you have Python 3.11 installed along with the required libraries.
2. Clone the repository:

git clone https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting.git

3. Navigate to the directory:

cd NBA-Machine-Learning-Sports-Betting

4. Install the required packages:

pip3 install -r requirements.txt

5. Run the main script with selected betting odds:

python3 main.py -xgb -odds=fanduel

6. For manual entry of odds, omit the -odds option and enter them after the script starts.
7. For bankroll management, you can use the -kc command line argument to see how much of your bankroll to wager based on the Kelly Criterion.

Building a Flask Web Application

Our software also comes equipped with a small Flask web application to visualize the predictions neatly:

1. Change to the Flask directory:

cd Flask

2. Run the Flask application in debug mode:

flask --debug run

Updating Your Dataset and Training the Model

To keep your model sharp, ensure you have the latest data from the ongoing season:

1. Create datasets:

cd src

python -m Get_Data

python -m Get_Odds_Data

python -m Create_Games

2. Train your models:

cd ..

python -m XGBoost_Model_ML

python -m XGBoost_Model_UO

Troubleshooting

If you encounter issues while setting up or running the model, consider the following:

• Ensure all required Python packages are installed. You can do this using the pip installer with the specified requirements.txt file.
• Check that your Python version is compatible (3.11 recommended).
• If data fetch fails, verify your internet connection or try a different sportsbook option.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By leveraging this machine learning model, you can significantly increase your chances of making informed sports bets in the NBA. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.