TJC Rankings
Computer College Football Rankings

About the Model

Algorithmic Rankings

TJC Rankings uses a transparent, iterative algorithm to rank college football teams based on actual game data. No human bias, no conference favoritism — just math.

The model runs up to 10 iterations, recalculating team strengths based on opponent quality until rankings stabilize (typically 4-7 iterations).

On-Field Results Matter

Head-to-head results are used as a tiebreaker when two teams have played each other. If Team A is ranked above Team B despite having a lower composite score, the badge indicates that Team A won their head-to-head matchup.

This ensures that on-field results are respected — a team that won the game gets the benefit when resumes are otherwise close.

7 Metrics (Weighted)

Each metric is scored on a 0-100 scale and color-coded for quick reading:

Green = Strong (75+) • Yellow = Average (50–74) • Red = Below Average (<50)

Championship Behavior

A composite score measuring how a team performs in high-leverage situations:

Normalized to 0-100 (center at 50). Above 50 = good championship behavior. Below 50 = concerning losses.

Data Sources

All game data comes from CollegeFootballData.com, a free API for college football statistics.

Rankings are updated weekly during the season and finalized after bowl games.

Iterative Methodology

Because Strength of Schedule and Quality Wins depend on opponent rankings, the algorithm must iterate:

  1. Start with neutral rankings (all teams at rank 65)
  2. Calculate all 10 metrics for each team
  3. Generate composite scores using weights
  4. Re-rank teams by composite score
  5. Repeat until top 25 stabilizes (≤2 position changes)