Quicksilver

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Honestly who are you gonna get that's better?

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Barring an upset, State is very likely headed to 6-6. That will be 3 SEC wins in 3 seasons. How much worse can it get?

Huge loss. Frazier looked good in relief but there is no replacing Kewan as a pass protector in the backfield.

My concern would be getting to 23 with a bummy Kamario and no Fluff. It's a havoc heavy defense that applies pressure and forces mistakes. If Kamario can't escape it with his legs, it could be a long day.

Good news is Oklahoma's offense is completely inept so you can score 16 and win.

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The top 2 teams in the SEC

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Well,

I think havoc rates may have an impact. Actually predicting turnovers, I'd agree would likely be like trying to measure injury impacts and penalties

Ole Miss is notorious for creating Heisman moments for struggling QBs.

It'll be interesting to see if Kalen Deboer can maintain his 20 point margin of victory average over LSU this year.

It'd be something like team x returns y OG this week. Compare team x's offensive EPA the first 3 weeks where y OG played vs. the two weeks he didn't and apply a weight.

The harder part is making that a reproducible prompt that, to your point, doesn't require weekly interjection but AI tools do make it a bit simpler. What makes injuries stupid to try and predict is the lack of official injury news. In the NFL, you know game participation lists are required an hour and a half before kickoff so you can schedule prompts for those slates to run. Half the time you're getting official word that college players are out literally minutes before kickoff.

Then you have situations where someone like Trammel Jones is starting over Aaron Philo. We should discount Florida some because they're missing a starting QB but how much? We have no data with Trammel at QB.

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Do such things exist?

Because that seems like a crazy thing to try and model.

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It is in CFB. I model it mostly retroactively to avoid giving a team an additional boost when they beat a team that had to suddenly play back-ups. The problem is that, with MSU @ LSU for example, it's just one game of data so you can't just punish LSU harshly for pummeling a disabled opponent. MSU's season EPA is what it is, you just have to hope you're applying the appropriate weight to recent game data.

And even then, you have games like Mizzou @ Ole Miss that statistically just make no sense. My models had Mizzou by 6-7 and Mizzou by 8-9 and they lose by 28. I'll nerd out for a sec if you'll humor me.

You have a team in Ole Miss that's bottom 10 in the country in Rush defense EPA and bottom half in rush offense EPA vs. a team that's top 10 in rush offense EPA and middle of the pack in rush defense EPA. All of these EPAs are opponent adjusted too. The result? Ole Miss outrushes Mizzou by 230 yards and wins by 28. We correctly predicted that a dominant rushing EPA advantage results in a big point swing. We could not have predicted it would be Ole Miss with the dominant advantage. You just gotta throw your hands up sometimes.

ETA: EPA is efficiency per attempt.

OP, I give you kudos for trying to model what has been an insanely difficult season to model. Modeling isn't easy and a lot of folks don't understand what all goes into it.

While I do not agree that LSU is #1 in the SEC, I can get there via the logic if we consider something like: How often does LSU lose to Kentucky if they play 1000 times, to Ole Miss 1000 times, etc. compared to how often does Ole Miss lose if they play Florida 1000 times. Ole Miss beats Florida significantly less than LSU beats Ole Miss or Kentucky (with all other elements the same).

I would say that you probably need some injury adjusted EPA. Assuming you are pulling parquets or JSONs from Github and running this through an AI model, I am curious as to which AI you're using.

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SEC Mike is an idiot. Circa sets the market and they release their lines at 1pm

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I mean most offshore books are already online. He's probably getting these from there. Mine has Tennessee -1.5 though.