How We Calculate Football Elo Ratings
Our specific methodology and customizations for high school football
While our system is based on the classic Elo rating formula developed in the 1960s, we've made specific adjustments to account for the unique characteristics of high school football. This page explains our exact calculation methodology, the factors we consider, and how to interpret the ratings.
Our Data Source
All game results come from JoeEitel.com, which aggregates comprehensive football scores from Ohio and bordering states. Results are updated weekly throughout the season, ensuring our ratings reflect the most current team performance.
Joe Eitel has been tracking high school football scores for decades and is recognized as one of the most reliable sources for Ohio high school football data.
Key Calculation Factors
K K-Factor: Maximum Point Change
We use a K-factor of 100, which represents the maximum number of points that can be gained or lost in a single game. This provides a good balance between responsiveness to new results and stability over time.
What this means:
- • A massive upset could result in up to 100 points changing hands
- • Most games result in smaller changes (20-40 points typically)
- • Expected results produce minimal rating changes (5-15 points)
E Expected Outcome Calculation
The scaling factor converts rating differences into win probabilities. We calculate each team's expected score (between 0 and 1) based on their rating difference. A team with a higher rating has a higher expected score.
Win Probability Examples:
M Margin of Victory Multiplier
Unlike basic Elo, we apply a margin of victory adjustment to reward dominant performances. This prevents teams from "running up the score" unfairly, while still recognizing truly dominant wins.
How it works:
- → Blowout wins (50+ points): Full point value awarded
- → Comfortable wins (20-49 points): Scaled multiplier based on margin
- → Close wins (1-19 points): Reduced point value
- → 1-point wins: Minimal point value (essentially a coin flip)
Note: This prevents teams from being over-rewarded for scoring excessive points against clearly inferior opponents, while still recognizing consistent dominance.
Understanding Rating Benchmarks
Here's how to interpret different rating levels in our system:
Average Team
Starting point for all teams. Represents an average, mid-level competitive team.
Above Average
Solid teams with winning records and competitive play.
Regional Powers
Strong regional teams, often top 25 in their division.
Elite Teams
Top 5-10 teams in their division, serious playoff contenders.
Championship Caliber
State championship contenders. The absolute best teams in the state.
Historically Dominant
Rare. Teams having truly exceptional, historic seasons.
Example Calculation
Let's walk through a specific example to see how ratings change after a game:
Before the Game:
Team A (Favorite): 1650 rating
Team B (Underdog): 1550 rating
Expected win probability for Team A: ~64%
Scenario 1: Expected Result
Team A wins by 21 points
Team A: 1650 → 1662 (+12)
Team B: 1550 → 1538 (-12)
Small change because the favorite won as expected
Scenario 2: Upset!
Team B wins by 14 points
Team A: 1650 → 1618 (-32)
Team B: 1550 → 1582 (+32)
Large change because the underdog won unexpectedly
Why This Methodology Works
Accounts for opponent strength: Beating a 1700-rated team is much more impressive than beating a 1400-rated team, and the ratings reflect this.
Rewards dominance appropriately: The margin of victory multiplier ensures that consistently dominant teams rise faster without over-rewarding blowouts.
Self-correcting over time: Teams that are initially over- or under-rated will quickly adjust as they play more games.
Predictive accuracy: Our system has demonstrated over 80% accuracy in predicting game outcomes, validating the methodology.
