The model turns power ratings into fair value in four steps: rate every team on one scale, convert rating gaps into per-game win probabilities, simulate the season to count title outcomes, then compare that probability against the de-vigged market price. The output is a single number per team, a fair value, that can be read straight against a contract quote.
At the top of the current national-title board, Notre Dame's fair value is 11.2 percent, Ohio State's is 11.1 percent, and Oregon's is 10.3 percent. Those are model estimates of title probability, not prices, and they are what every quoted contract gets measured against.
What is a power rating and why start there?
A power rating expresses each team's strength as one number on a shared scale. The value of the number is not the number itself but the gap between two teams: that gap, adjusted for home site and neutral fields, maps to a win probability for a single game.
Starting from ratings keeps the process disciplined. Instead of pricing a title directly, the model prices the thing it can actually estimate, a game, and lets the season structure do the rest. A schedule of favorable matchups compounds into a higher title number; a gauntlet drags it down even for a strong team.
How do game probabilities become a title number?
Per-game probabilities are the inputs; the season simulation is the engine. The model plays the full schedule many times over, sampling each game by its win probability, then advancing simulated results through the conference races and the playoff bracket.
Counting how often each team finishes as champion across all simulations yields its title share. That share is the fair value. Indiana at 8.2 percent and Miami at 6.8 percent sit where they do because their simulated paths to a title clear less often than the teams above them, not because of any single result.
Why de-vig the market before comparing?
Raw contract prices are inflated by an overround: add up every team's price and the field sums to more than 100 percent, because the spread between buy and sell has to live somewhere. Comparing a model probability to a raw price without adjusting is an apples-to-oranges error.
De-vigging scales the field back toward 100 percent so each price reads as a clean implied probability. Only then does a comparison mean anything. The consensus fair value shown for each team is this de-vigged number, blended across venues such as Kalshi and Polymarket.
Where does the model see the tightest gaps?
The interesting cases are where best available price and fair value nearly touch. Ohio State's best price is 12c on Kalshi against a 11.1 percent fair value, one of the closer alignments near the top. Notre Dame's 13c best price on Polymarket sits just over its 11.2 percent fair value, and Texas prices at 11c on Polymarket against 9.6 percent.
Reading the board this way turns a list of numbers into a shortlist. The model does not chase the highest fair value; it flags contracts whose price sits at or below the model line. For traders comparing venues, promo access such as Kalshi FADE or Polymarket TGSWC changes net cost but not the underlying fair value, which is the point of measuring price against the model in the first place.
None of this is a forecast dressed as certainty. Both the simulation and the market price carry error, and both revise as depth charts, results and liquidity move. Fair value is the reference line, not the last word.
