Fair value is the number the model publishes for every team, and it is built in three steps: turn power ratings into game-by-game win probabilities, simulate the season and playoff bracket to get a raw title probability, then reconcile that output with de-vigged market prices. For the 2026 title, the process puts Ohio State and Notre Dame on top at 11.3% each, with Oregon at 10.1% and Texas at 9.9% just behind.
How do power ratings become win probabilities?
A power rating is a single number for team strength. On its own it says little; the model's job is to translate the gap between two ratings, adjusted for site and rest, into a probability that one side wins a given game. A larger rating edge maps to a higher win probability, and the same rating feeds every matchup on a team's schedule.
That translation is where schedule strength enters. A strong rating behind a demanding slate produces lower cumulative win probabilities than the same rating against a soft one. This is why teams with similar reputations can separate on the board once the full schedule runs through the model.
How does simulation turn probabilities into a title number?
Individual game probabilities are only inputs. The model simulates the entire season thousands of times, then seeds and runs the playoff bracket in each pass. The share of simulations in which a team lifts the trophy is its raw title probability.
That method captures paths, not just talent. A team can carry a strong rating yet see its title share capped by a tough road to the bracket, while a slightly lower-rated team in a friendlier region banks more clean runs. The simulation is what separates Indiana at 8.7% and Miami at 6.7% from the cluster at the top.
Where the model's fair value sits for 2026
The published fair values compress the field tightly at the top. Ohio State and Notre Dame lead at 11.3%, Oregon holds 10.1%, Texas 9.9%, and Georgia 9%. The gap from first to fifth is under two and a half points, which signals a title race the model views as unusually open.
Below the leaders, the drop is steeper: Miami at 6.7%, LSU at 5%, then a long tail with Texas A&M at 2.6%, Texas Tech at 2.5%, Alabama at 2.3%, and Oklahoma at 2%.
How fair value flags a tradeable price
Fair value only matters next to the price on the screen. Because raw contract prices across a field sum past 100%, the model de-vigs them into clean probabilities, and that de-vigged consensus is the reference. A contract reads as value when its best available price sits below fair value, and as a premium when it sits above.
The current board shows both. Ohio State's best price is 13c on Kalshi against 11.3% fair value, a premium to the model. Texas is the tighter read: 12c on Polymarket versus 9.9%. Georgia at 10c on Kalshi and Indiana at 9c on Kalshi sit just over their fair values as well, which is typical for liquid favorites carrying a small margin.
Reading the board across venues
The model does not pick a venue; it reports the cheapest price wherever it lives. Right now Kalshi holds the best price on Ohio State, Notre Dame, Oregon, Georgia, and Indiana, while Polymarket leads on Texas, Miami, LSU, and most of the tail. Splitting the shortlist by venue is how the model keeps the fair-value comparison honest.
Prices and the model can both be wrong, and fair value is an estimate, not a guarantee. New traders comparing the two boards can weigh promos such as Kalshi FADE or Polymarket TGSWC when choosing where to price a contract, but the discipline is the same: trade only when price sits under fair value, and treat the model as a reference rather than advice.
