🏀 March Madness Prediction System

A sophisticated quantitative analysis and prediction system for NCAA March Madness tournaments, applying factor investing principles and machine learning techniques to college basketball data.

📊 Quantitative Signals

Core Predictive Factors

1. Conference Strength 📈

   • Win percentage aggregation
   • Simple Rating System (SRS)
   • Strength of Schedule (SOS)
   • Scoring metrics
   • Point differential analysis

2. Team Experience 👥

   • Player class distribution (FR/SO/JR/SR)
   • Games played metrics
   • Minutes played analysis
   • Team cohesion indicators

3. Momentum Factors 🔥

   • Recent win percentage trends
   • Point differential momentum
   • Performance trajectory analysis
   • Pre-tournament form

Coming Soon

• Coach experience metrics
• Historical tournament performance
• Player efficiency indicators
• Non-conference schedule strength

🔬 Backtesting Framework

Signal Evaluation

• Individual signal performance tracking
• Year-by-year analysis (2010-2024)
• Tournament game prediction accuracy
• Signal combination optimization

Performance Metrics

• Prediction accuracy by round
• Signal reliability scores
• Historical effectiveness trends
• Cross-validation results

🛠 Tech Stack

Data Pipeline

• Python: Data analysis, machine learning, and backtesting
  • pandas: Data processing
  • scikit-learn: Statistical analysis
  • BeautifulSoup4: Data collection

API Layer

• Go: High-performance web server
  • Fast data processing
  • RESTful API endpoints
  • Real-time predictions

Storage & Frontend

• Database: PostgreSQL
• Frontend: React with TypeScript

📁 Project Structure

├── data/               # Data storage
│   ├── raw/           # Raw scraped data
│   └── processed/     # Processed datasets
├── python/
│   ├── analysis/      # Factor models
│   ├── backtesting/   # Testing framework
│   └── data/          # Data processing
├── go/
│   ├── api/          # REST endpoints
│   ├── models/       # Data structures
│   └── services/     # Business logic
└── web/              # Frontend app

🚀 Getting Started

1. Environment Setup

   python -m venv venv
   source venv/bin/activate  # Unix
   pip install -r requirements.txt

2. Data Collection

   cd python/data
   python scraper.py  # Collect historical data

3. Signal Processing

   python processor.py  # Generate signals

4. View Results

   • Check data/processed/backtest_results.csv for signal performance
   • Analyze prediction accuracy by year and signal

📈 Performance Tracking

Track the performance of our predictive signals in the backtest_results.csv file, which includes:

• Signal accuracy by tournament year
• Comparative signal effectiveness
• Combined signal performance
• Historical trend analysis

🤝 Contributing

Interested in contributing? We welcome:

• Additional predictive signals
• Backtesting improvements
• Data source integrations
• UI/UX enhancements

📝 License

MIT License - Feel free to use and modify for your own March Madness analysis!