NBA Game Prediction Model

This project implements a machine learning model to predict NBA game outcomes using historical game data, team statistics, and betting odds. The model utilizes ELO ratings and various features to make predictions.

Project Structure

NBA-Prediction-Model/
├── data/                    # Data files
│   ├── 538_probs.csv
│   ├── all_moneylines_sbr.csv
│   ├── games.csv
│   ├── games_details.csv
│   ├── players.csv
│   ├── ranking.csv
│   └── teams.csv
├── notebooks/               # Jupyter notebooks
│   ├── BettingWithClassifiers.ipynb
│   └── eloUtils.py
└── README.md                # This file

Data Sources

• NBA Game Data: Historical game data including scores, teams, and player statistics.
• Betting Odds: Moneyline data from various sportsbooks.
• 538 Predictions: Game predictions from FiveThirtyEight's NBA model.

Key Features

• ELO rating system implementation for team strength estimation
• Feature engineering for game predictions
• Machine learning models for outcome prediction
• Performance evaluation metrics

Requirements

• Python 3.7+
• pandas
• numpy
• scikit-learn
• matplotlib
• jupyter

Usage

1. Install the required packages:

   pip install -r requirements.txt

2. Run the Jupyter notebook:

   jupyter notebook notebooks/BettingWithClassifiers.ipynb

Model Performance

The model's performance is evaluated using various metrics including accuracy, precision, recall, and ROC-AUC. Detailed performance analysis can be found in the notebook.

License

This project is licensed under the MIT License - see the LICENSE file for details.