I graded Percera’s full frozen Week 6 board. It went 40–16, and the confidence splits were pretty interesting

[u/PerceraAnalytics]

I’ve been tracking Percera’s matchup results throughout the season, and I went back and graded the full Week 6 board exactly as it was frozen before the games were played.

Across 56 FBS vs. FBS games it finished 40–16 picking winners (71.4%).

The overall record was obviously nice to see, but what interested me more was how the results changed as the model got more confident.

• 60%+ favorites: 36–7
• 65%+: 32–4
• 70%+: 25–2
• 80%+: 13–1

The projected edge showed a similar pattern:

• 7–13.9 point edges: 19–2
• 14–20.9 point edges: 8–0

The weak spot was pretty obvious too.

Games where Percera saw less than a 3-point edge went just 4–8.

So at least for this slate, the model did a much better job when it had a strong opinion than it did trying to separate teams it viewed as basically even (which is somewhat expected).

I also thought the probability calibration was interesting. The average favorite probability across the board was 70.4%. If you add all of those probabilities together, the model would have expected about 39.42 wins and it got 40.

The margin projections were quite a bit noisier. Mean absolute margin error was 13.1 points and the median miss was 8.9, so that’s something I’m going to keep an eye on separately from simply picking winners.

The full week 6 rankings, CQI/CTSI methodology, week 7 matchup board, and everything else are live:

Site: perceraanalytics.com

For anyone else building models, I’d be interested in what you’d track as this sample grows, especially for calibration beyond just confidence buckets and margin error.

[u/dharkmeat]

Are you saying that straight up W/L at 71.4% is good? Come join moderator Bill’s prediction site! https://predictions.collegefootballdata.com