NFL Game Prediction using Hidden Markov Models

Overview

This project implements a Hidden Markov Model (HMM) to predict NFL game outcomes using historical game data. It provides a comprehensive machine learning approach to sports prediction, offering advanced analytics and predictive capabilities.

Features

• Advanced Prediction: Uses Hidden Markov Models for game outcome prediction
• Monte Carlo Simulation: Robust risk assessment and performance analysis
• Interactive Web Interface: Streamlit-powered dashboard for model exploration
• Comprehensive Validation: Detailed model performance metrics

Installation

1. Clone the repository:

git clone https://github.com/flancast90/nfl_markov_predictor.git
cd nfl_markov_predictor

2. Install dependencies:

pip install -r requirements.txt

Usage

Command Line Interface

python cli.py [options]

Options:

• --process: Process input data
• --train: Train HMM model
• --validate: Validate model performance
• --montecarlo N: Run Monte Carlo simulation

Web Application

Launch the Streamlit app:

streamlit run app.py

Project Structure

nfl_markov_predictor/
├── app.py # Streamlit web interface
├── cli.py # Command line interface
├── markov.py # HMM implementation
├── montecarlo.py # Simulation engine
├── requirements.txt
├── data/ # Dataset storage
├── model/ # Model processing modules
│ ├── process.py
│ ├── train.py
│ ├── validate.py
│ └── utils.py
└── results/ # Simulation results

Key Components

• Markov Model: Probabilistic state transition modeling
• Monte Carlo Simulation: Performance risk assessment
• Data Processing: Historical game data analysis
• Model Validation: Comprehensive performance metrics

Performance Metrics

• Accuracy
• Confusion Matrix
• Profit/Loss
• Return on Investment (ROI)
• Precision and Recall

Contributing

Contributions are welcome! Please submit pull requests or open issues.

License

MIT License

Contact

Project Maintainer: flancast90