The math behind Stat-urday: how the ELO formula, home field advantage, and margin of victory shape rankings.
Our college football ranking system uses a modified ELO algorithm with enhancements for:
Each team starts with a calculated preseason rating based on recruiting, transfers, and returning production.
Before each game, we calculate the expected win probability for each team using the logistic curve.
With Home Field Advantage: Add 65 points to the home team's rating before calculating win %.
Wins by larger margins earn more rating points, but with diminishing returns to prevent blowout inflation.
When quarter-by-quarter scores are available, we process each quarter separately with garbage time detection.
Rating changes are adjusted based on the strength of competition (conference matchups).
| Matchup | Winner Mult | Loser Mult | Rationale |
|---|---|---|---|
| P5 vs P5 / G5 vs G5 | 1.0x | 1.0x | Equal competition |
| P5 beats G5 | 0.8x | 1.2x | Expected result |
| G5 beats P5 | 1.5x | 0.8x | Upset! Reward G5 |
| FBS vs FCS | 0.0x | 0.0x | Excluded from rankings |
Combining all factors, here's the complete formula for rating changes after a game:
Progressive K-Factor: Our system uses different K-factors based on the week:
• Weeks 1-4: K = 64 (aggressive adjustment from preseason)
• Weeks 5-8: K = 48 (continued adjustment)
• Weeks 9+: K = 32 (stable ratings)
This allows early-season games to have more impact when correcting preseason predictions, then stabilizes for more predictable late-season rankings.
Teams start with ratings based on recruiting, transfers, and returning production rather than arbitrary values.
Logarithmic scaling rewards wins but prevents blowout inflation. Garbage time detection reduces impact of meaningless scoring.
P5 vs G5 matchups are weighted appropriately, rewarding upsets and adjusting for expected outcomes.