Prediction-market prices move for three reasons: liquidity, news, and the model's view of fair value. Liquidity decides how far a single order pushes a quote, news changes the information set traders are pricing, and the model's de-vigged fair value is the probability anchor that prices drift toward over time. Everything visible on the national title board is some combination of those three forces.
What does liquidity do to prediction-market prices?
Liquidity is depth: how much size can trade before the price has to move. In a deep market, a large order is absorbed with little drift, and the quote stays close to where it started. In a thin market, the same order walks the book, and the price can jump several cents on flow alone, with no new information attached.
This matters most on longshots. A contract priced in the low single digits, like Texas A&M or Oklahoma further down the board, moves more on a single order than a heavily traded favorite does. Thin depth is why two teams the model separates can sit at an identical posted price: the quote is sticky between trades, and small flow has not yet pulled them apart.
The practical read is to separate price moves caused by information from price moves caused by liquidity. A jump on no news is a depth event, and it often reverts. A jump on news is a repricing, and it tends to hold.
How does news reprice a futures contract?
News is the cleanest driver. A roster change, a schedule development, or a result shifts the probability traders assign to a title, and the price responds before any model does. The market quote is the first mover; the model's fair value is the second mover that either confirms or fades it.
On the current board, the favorites cluster tightly. Texas leads at a 9.1% fair value, Notre Dame follows at 8.6%, and Oregon sits at 7.8%. Gaps this small mean a single piece of news can reorder the top of the board, because only a point or two of probability separates the contenders.
The key distinction is durable versus transient. Information that changes a team's path to the title is durable and should stick in the price. A reaction to noise is transient, and the de-vigged fair value is the tool for telling the two apart.
What is the model's fair value, and where is the vig?
Every posted price carries overround, the cushion that keeps the implied probabilities across all outcomes summing to more than 100%. The model strips that overround out and redistributes it, leaving a de-vigged fair value that reads as a clean probability.
That is why fair value sits below the posted price. Texas trades at 11c but models to 9.1%. Ohio State and Indiana both post 9c against a 7% fair value. The distance between the cents and the percentage is the cost embedded in the quote, and it is the first number to check before treating a price as cheap.
Two teams can share a posted price while the model separates them, or carry the same fair value at different posted prices. Reading both columns together, not the cents alone, is what surfaces where price and probability disagree.
Where do price and fair value sit on the board now?
The favorites compress into a narrow band. Texas (9.1%) and Notre Dame (8.6%) anchor the top, Oregon (7.8%) sits just behind, and Ohio State and Indiana share the 7% line. Miami (5.8%), Georgia (5.3%) and LSU (4.9%) round out the next tier.
The posted prices track that order but flatten it: 11c for both Texas and Notre Dame, 10c for Oregon, 9c each for Ohio State and Indiana, then 7c, 7c and 6c down the next group. The compression at the top is where liquidity and news do their work, nudging teams within a single cent of one another.
Which venue holds the best price?
Across the listed national title contracts, Kalshi posts the best available price for every team, from Texas and Notre Dame at 11c down to LSU at 6c. With a single venue leading the board, cross-exchange moves come down to which book updates first when news lands.
The posted-price ladder below mirrors the fair value order but sits a notch higher in each case, the overround made visible. Traders comparing venues can note the Kalshi FADE code, though the analytical point stands on its own: read the price against the model, not against the hype.
Prices and the model can both be wrong, and none of this is financial advice. The framework is simply this: when a quote moves, ask whether it was liquidity, news, or the model, and price the answer accordingly.
