Our Methodology

Our prediction system uses machine learning models trained on comprehensive college football data spanning multiple seasons. We employ a consensus approach that combines multiple models to produce more accurate and reliable predictions.

Team Performance Metrics

• Team Efficiency Ratings - Advanced offensive and defensive metrics
• Power Rankings - Statistical team strength indices
• Historical Performance - Season and multi-year trend analysis
• Talent Metrics - Roster composition and quality indicators

Game-Level Features

• Home field advantage adjustments
• Rest days and scheduling factors
• Historical matchup data
• Conference strength metrics

Consensus Approach

We use a consensus of multiple machine learning models, each trained on different feature sets:

• Current Model - Uses recent season data with emphasis on current year performance
• Historical Model - Trained on multi-season data for robust long-term patterns
• Ensemble Model - Combines offensive and defensive predictions separately
• Full Feature Model - 700+ features including advanced metrics and recruiting data

Training Process

• Ridge regression with optimal regularization (α = 0.1)
• Trained on 12,000+ historical games
• Cross-validated to prevent overfitting
• Calibrated to match actual scoring distributions

Score Calibration

We apply calibration factors to ensure our predictions match real-world scoring distributions:

• Training data average: 55.2 total points per game
• Predictions are scaled to match this distribution
• Prevents systematic OVER/UNDER bias

Team Name Mapping

Critical quality control step to ensure accurate data alignment:

• Canonical team names mapped across all data sources
• Verified mappings for all teams before generating predictions
• Prevents feature misalignment that could cause incorrect predictions

Model Accuracy

• R² Score: 0.607 (explains 60.7% of variance in game scores)
• Mean Absolute Error: ~10 points per score prediction
• ATS Target: 55%+ win rate (breakeven is 52.4%)

Edge Detection

We calculate the "edge" as the difference between our prediction and Vegas lines:

• Small Edge: < 3 points - Lower confidence
• Medium Edge: 3-7 points - Moderate disagreement with Vegas
• Large Edge: 7+ points - Significant value opportunity

What We Can't Predict

• Injuries to key players (unless reflected in pre-game lines)
• Weather conditions (extreme weather can significantly impact scoring)
• Motivational factors (rivalry games, playoff implications)
• Coaching changes or in-season staff turnover