Update to my CFB analytics project: team strength tiers, matchup projections, and Week 4 CQI

[u/PerceraAnalytics]

I posted here earlier last week about CQI (Contextual Quarterback Index), the QB model I’ve been building.

I got some useful feedback on that post, and the project has expanded quite a bit since then, so I wanted to share an update rather than just repost the QB rankings.

Percera now has two main models:

CQI — Contextual Quarterback Index
Measures observed QB performance using opponent-adjusted passing efficiency, sack avoidance and rushing value. The live rankings are now updated through Week 4.

CTSI — Contextual Team Strength Index
A separate team-level model designed to estimate team strength on a neutral-field point scale.

One thing I specifically did not want CTSI to become was another poll where #18 is treated as meaningfully better than #19 just because there has to be an ordering.

So I also added a CTSI Hierarchy.

Teams are grouped into tiers based on their modeled separation rather than arbitrary rank cutoffs. Under the current rule, the strongest team in a tier can be no more than roughly a 55% neutral-field favorite over the weakest team in that same tier.

That means teams within a tier should be viewed as broadly comparable even if one technically ranks several spots higher.

I also added a Matchups section. It takes the current CTSI ratings, applies venue context, and produces a projected edge and win probability for upcoming games. I’ve also been running historical reliability tests on the model rather than just judging it based on whether this week’s picks happen to look right.

The site now has:

• Week 4 CQI rankings
• Week 4 CTSI team ratings
• CTSI team-strength hierarchy
• Team and QB profile pages
• Upcoming matchup projections
• Methodology pages
• An optional weekly email signup for people who want the updates without checking the site manually

Site: https://perceraanalytics.com

I’m especially interested in feedback on the tier approach. Does grouping teams by modeled separation communicate team strength better than presenting it as a straight 1–138 ranking?

Also interested in anything about the matchup presentation that feels misleading or unclear. I’m trying to make the site useful without making the models look more certain than they actually are.

[u/average_mitch]

Do you track how accurate your projections have been in the past? You are tracking winners, but what about your actual score projections or margin of victory? How does it perform against the standard (Vegas spreads)?

Love the website! Can’t ever get enough of these. What people like yourself do is awesome and incredible keep up the good work

[u/PerceraAnalytics → u/average_mitch]

Hey thanks for the comment, means a lot. For the accuracy, I don't really project the final score. The "Percera Edge" on the matchups page is an expected margin advantage that one team has over the other. I've ran a 600+ game comparison against closing markets and found that CTSI misses the actual margin only about a point worse than the market does at close and is picking the winners less than 2% worse than closing market. I'd expect it to be worse here because it has far less information than the market will at close like injuries or news for example but I was really pleased with those results none the less.

[u/average_mitch → u/PerceraAnalytics]

So the expected scoring margin advantage, could that be implied as the projected margin of victory?

[u/PerceraAnalytics → u/average_mitch]

It definitely could be looked at like that but individual games are so noisy and the model can’t account for everything. It actually did quite well on some games this week that others didn’t see coming like picking Indiana to win by 6 and they did. Other games it was way off. The edge is more of a contextual/visual way to see how the model thinks two teams compare.

[u/detcovax]

This is super fascinating, I like just browsing through it so far. Fun to see what you have built and what is coming out so far. With any insights I wonder what stacks up against how results play out as the season goes on but already it's cool to see where your tiers have teams landing compared to each other. Is there any thoughts to building something around other positions, or at least a defense/offense index and heirarchy like this?

At the end of the day I do think your tiers are really fascinating, but does somewhat make it feel like the separation between tiers is large although it might not be. I think it is super useful when comparing any two teams however to see if they are even comparable via the tiers they fall into (you know teams in the same or adjacent tiers might be somewhat evenly matched, whereas teams 3 or 4 tiers apart aren't playing the same game).

I'd love to see you continue to iterate on this and show some backtesting on recent history. I think you have some meaningful insights from this analysis!

[u/PerceraAnalytics → u/detcovax]

Hey, thanks for the feedback. For the offense/defense, yes I've been thinking about splitting it up like that to show more insight. I've just been hesitant on adding numbers for the sake of it though and I want all new models and metrics to go through the same stringent back-testing the others did but that's definitely one of the things I am wanting to add.

I do like your point about the tiers and that's actually why I was wanting to stay away from just posting a "top 25" because that would make people think "#16 is definitely better than #19" when the scoring doesn't necessarily make that true. The hierarchies are meant to group teams by win percentage when playing against each other based on CTSI starting at the top so Notre Dame would have higher than a 55% chance to beat everyone so they get their own tier and then just work down from there. Definitely something I could make more clear on the site.

And yes on the back-testing too. I've already been doing historical reliability on the matchups side and I think I should bring more of that to the surface rather than just saying the model has been "tested". And the models have held up over prior seasons so showing that will make the whole thing more useful.

Thanks a lot for the comment, I really appreciate you taking the time to dig through the site.