College football Predictions

Your College Football Second Opinion.

Your weekly college football margin prediction model. Right beside the market. Compare projected margins, totals and win probabilities for every game. Right beside the market. Line movement tracker shows which way the market is shifting throughout the week. Built-in AI agent will do research to validate the models predictions and provide extra feedback on important updates like weather and injuries.

7 days free, no card required.

71.3%
Winners picked
238–96
2026 record
±13.7
Avg margin error
50.2%
Against the spread
165–164
Record vs. spread
751
Games rated

What this is for

Most public predictions are a number with no history attached. This site is the opposite: every prediction is posted before kickoff and graded after it.

The goal isn't to beat the bookmakers on every game — nothing does. It's to find the handful of games each week where the model and the market genuinely disagree, and to be honest about the rest.

Anatomy of a prediction

Every game on the predictions page is one card. This is a real published prediction — Vanderbilt at Florida — with each part labelled.

Live example
Sat Nov 21 · TBD Upcoming
Vanderbilt Pick –
@Florida –
Total 60 · implied 34–26
Model Vanderbilt −8.5 70% win prob
Enhanced Check Details →
1

Kickoff & status

Before kickoff: the local start time, or TBD when the TV window isn't set, plus an Upcoming chip. Afterwards: Final and how many points the model missed the margin by. A ◆ Toss-up chip appears when Vegas has the game inside 3.5 points — historically the model's best spot against the spread.

2

The matchup

Away team on top, home team below with an @. Before the game the side the model picks carries a gold bar and a Pick tag; once it's played, the bar and a Win tag move to whoever actually won, beside the final score.

5

Total — combined points

A separate model from the one that predicts the margin. It projects the two teams' combined score, and whether that lands Over or Under the posted total. The implied pair beside it is that total split by the predicted margin — arithmetic on the two numbers, not a forecast of either team's score.

6

Model — the prediction itself

Written in the same notation as the Vegas line above, so the two compare directly: Vanderbilt −8.5 means the model has Vanderbilt winning by 8.5. Beneath it, that side's calibrated win probability.

7

Going deeper

Enhanced Check sends an AI agent to verify the prediction against ESPN, DraftKings and FanDuel. Details opens the full matchup page with the team stats behind the number. Both work on the predictions page; this card is only an illustration.

How the model works

What it learns from

Team-strength features built from play-by-play going back a decade: opponent-adjusted EPA for passing and rushing on both sides of the ball, success rate, points per scoring opportunity, havoc rate, and an ELO rating that carries form across seasons.

How it predicts

A ridge regression predicts the final margin from the two teams' adjusted profiles — deliberately simple, because with this much noise per game a heavier model mostly learns the noise. A second model projects the combined total, and a calibrated win probability turns the margin into odds: when it says 70%, that side has historically won about 70% of the time.

When it's at its best

Early-season predictions lean on last season's numbers, regressed toward the league average, so treat September with some skepticism — by around week 5 the features are mostly this season's play, and the model is at its best from there on.

Measured against the market

Each prediction is published beside the sportsbook spread and total, along with how far the line has moved since we first recorded it. Games the market calls close are where the model predicts best and the against-the-spread record is tracked separately, so its edge is a number rather than a claim.

Checked by an AI agent

Enhanced Check sends a single game to an AI agent that pulls current injuries, weather, and live odds, then says whether it agrees with the model and why. It catches what a season-long statistical profile cannot see — a quarterback ruled out on Friday night, 30 mph wind, or a half the team is out with the mumps.

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