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Noelthemexican Ballot for 2026 Week 8

Ballot Type: Computer

Submitted: Oct. 18, 2026, 2:18 a.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.

Permanent Note: I think I've figured out why my results are so unexpected. The way I have things set up above, it's doing something I'm not entirely happy with. Since I am doing the current weeks ratings WITH RESULTS, essentially it's adding a bias against surprise winners and a bias for surprise losers. E.G. Team A is rated .80 and team B is rated .75, so team A is expected to win by 6 points. Team B ends up winning by 4 points. So the ratings update with the results and suddenly team A is rated .70 and team B is rated .85 so the prediction in the model is actually that team B wins by 8 points. Ergo, Team A, who lost an unexpected game is actually rewarded for losing by only 4 points (less than expected) and Team B is discounted for only winning by 4 points (less than expected).

There may be some argument why this might be a valid way to assess teams, but it doesn't quite fit my vision for what I wanted to do. I will stick with it for the rest of the season for consistency sake, but I plan to use a lagged rating differential in the future.

Note: The results are not reducing in variance as much as I'd hoped at this point. Part of it is the new additional note above, but part of it just might be how LARGE the influence of the expected scores is. Next year I might not do a 0-1 min/max transformation and instead do a normal distribution around 0.5. To illustrate how much influence it has, Texas is #17 without bias in my model, and it has shot them up to #1 with bias. Combine the above note with this influence and you have teams ahead of teams that beat them, even when that losing team has a worse record and a lower raw rating.

The only positive I have at this point is that the top 25 itself isn't really that far off from the raw ratings, but the order absolutely is. The teams who got shafted because of their MOV are Boise, NDSU, and UTSA.

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