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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
  1. 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