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Noelthemexican Ballot for 2026 Week 3

Ballot Type: Computer

Submitted: Sept. 13, 2026, 4:37 p.m.

Overall Rationale: Every year I've done some variation of the Colley Matrix (introducing bias to the famously "bias-free computer poll"), and this year is no different. Two years ago the teams were biased by FPI. This was probably good as far as accuracy goes, if you consider accuracy "closer to human polls." But in practice it was pretty much the Colley matrix with G5 teams down a lot of spots and P5 (especially SEC) teams up a lot of spots. Last year, I biased the teams by PFF rating. Specifically their "Overall" rating. This wasn't too bad, it boosted a few teams and dropped others. But the black box of PFF made it a little uninteresting to me.

This year the bias is going to come from excess score. I will run a regression where I use the rating of the teams facing each other to predict the scoreline, so:

Home Team Score - Away Team Score = a+b(Home Team Rating - Away Team Rating)

Then I will take the residuals from this regression, and average by team. The averages will be converted to numbers between 0-1. And this number will be the bias number.

At this point there's mostly 1 observation per team so the results will be very funky. But as the season progresses, teams will get punished for winning close games that had no business being close, and rewarded for big wins in games that should have been competitive. This also applies on the other side too, teams will get rewarded for making games close they should have been blown out in, and so on. I guess in a way, you can consider this a "beat the spread bias" but the spread comes from me, not Vegas.

A couple of issues and questions that have since been decided by me:

Since this was the first week, actually starting this was a little difficult, as the question of should the ratings used be the ratings of the current week or the prior week?
-I have decided that I will input the given weeks games, allow the ratings to calculate based on the bias numbers from the previous week. My logic is that this might actually correct the issues quicker since a team like Louisiana Tech would be expected to win big at LSU, receive a large negative residual when they don't achieve that, which might make the process quicker.

Which leads the the next question: Should the following weeks use the normal Colley matrix to predict scores, or my ratings?
-As I said above, I'm actually thinking that I'm going to use my own ratings to predict scores.

Is the min/max normalization of the residuals between 0-1 going to have too much of an effect on the ratings?
-I'm gonna roll with this for now. If we get to week 7-8 and it's still really funky, I think I'll force the ratings on a smaller distribution around .5

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