TOTAL VOLUME:
$154.4b
24H VOL:
$290,393,159
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,944,100,805
420,493
Markets across
40,511
events
MATCHED EVENTS:
4,612
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 17, 3:25 PM EST
Kalshi
This group covers various markets related to the college football game between Brown and Princeton scheduled for October 17, 2026. Markets include the winner of the game, point spreads, and the over/under on the total points scored.
In the upcoming college football game between Brown and Princeton, scheduled for October 17 at 12:00PM ET: This market will resolve to "Brown" if Brown win the game. This market will resolve to "Princeton" if Princeton win the game. Overtime is included if played. If the game ends in a tie, this market will resolve 50-50. If the game is postponed, this market will remain open until the game has been completed. If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
The market resolves according to the winner of the Brown vs Princeton college football game scheduled for October 17, 2026. Should the game be postponed but start within 48 hours of its original scheduled time, the market remains active and resolves based on the official result. If the game is cancelled or does not begin within the 48-hour period, the market resolves to a fair price. Kalshi clarifies that it holds no official connection with the NCAA, and all trademarks are property of their respective owners.
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. Differences in prices between Polymarket and Kalshi for the Brown-Princeton game can arise from several factors. Each platform has its own user base, with varying risk appetites and information sets, which can influence trading behavior. Furthermore, the liquidity and trading volume on each platform can differ, impacting price discovery. Different fee structures and market-making activity on Polymarket versus Kalshi also contribute to potential price discrepancies. These variations are normal in decentralized prediction markets and reflect the dynamic nature of crowd-sourced forecasting.