TOTAL VOLUME:
$139.4b
24H VOL:
$149,134,522
24H TRANSACTIONS:
2,540,891,627
OPEN INTEREST:
$1,613,330,048
474,211
Markets across
36,680
events
MATCHED EVENTS:
2,917
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 4, 10:14 PM EST
Kalshi
This market tracks the outcome of the Indiana State versus Purdue college football game, specifically whether Purdue will cover the -4.5 point spread. Currently, the consensus probability of Purdue covering is 100.0%. This prediction is an aggregation of markets from Kalshi and Polymarket, and will resolve based on the result of the Indiana St. vs Purdue college football game originally scheduled for Sep 4, 2026. Traders should watch the game itself on September 4, 2026, to see if Purdue achieves a victory by more than 4.5 points.
In the upcoming college football game, scheduled for September 4 at 7:00PM ET: If Indiana State wins, the market will resolve to "Indiana State". If Purdue wins, the market will resolve to "Purdue". If the game is postponed, this market will remain open until the game has been completed. If the game is canceled entirely or ends in a tie, with no make-up game, this market will resolve 50-50.
The market resolves based on the winner of the Indiana St. vs Purdue college football game scheduled for September 4, 2026. If either team secures a victory, the corresponding market will resolve to Yes. Should the game be postponed but commence within 48 hours of its original scheduled start time, the market remains open and resolves according to the final official result. If the game is cancelled or fails to start within the 48-hour window, the market resolves to a fair price, ensuring equitable treatment for all participants under these conditions.
Prediction market odds often reflect sharper, more granular insights than traditional sportsbook lines, especially for niche matchups like this one. While sportsbooks may post a standard spread or moneyline, prediction markets such as this one can incorporate real-time news, injury updates, and betting patterns, potentially offering tighter or more volatile pricing.
On Polymarket and Kalshi, pricing mechanisms, user bases, and liquidity conditions vary, leading to differences. Polymarket and Kalshi can show different implied probabilities for the same outcome because of liquidity, fee structure, participant mix, and how each venue defines the contract. For instance, one platform might have deeper liquidity around a specific outcome, or its user demographic could react more strongly to recent team performance news, pushing its odds away from the other venue.