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Noelthemexican Ballot for 2026 Week 9

Ballot Type: Computer

Submitted: Oct. 25, 2026, 2:46 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(1) 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.

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.

(1) The rating used in this calculation is the rating with the previous weeks bias, but the current weeks results.

Note: I debated whether I should even bother writing a note after last week because so many people pick and chose phrases and completely misunderstood what I was saying. The model is working as described. The caveat is that the spreads are compared after including the current weeks results. As in, given we know Texas just beat Ole Miss, how much would we expect Texas to win by. There is an argument for and against this method. On one hand, you include more information. On the other hand, the results may not reflect the severity of an upset to the extent of which you would expect. It is not "doing the opposite". It is not "ignoring wins and losses". As I described last week (even though it appears nobody bothered to read that part), the biggest issue is certainly weighting. I considered breaking my personal rule this week and adjusting the weighting scheme to be closer to a normal distribution (something like mean = .5, SD = .2, so .7 would be a SD above .9 would be two SDs and so on), but after last week I'm just not going to do that, and instead leave it for next year.

Last week was actually the first time the rankings were not in the top 20 most unusual, though you would never think that. Oregon has the highest boost from E(MOV).

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