TOTAL VOLUME:
$154.4b
24H VOL:
$245,801,863
24H TRANSACTIONS:
2,984,028,936
OPEN INTEREST:
$1,902,097,102
417,059
Markets across
40,178
events
MATCHED EVENTS:
4,415
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 31, 11:10 PM EST
Kalshi
This group covers markets related to the college football game between Lamar University and UT Rio Grande Valley, scheduled for October 31st. Markets include both the winner of the game and the point spread, with various options available for each. Over/Under total points markets are also included.
In the upcoming college football game between Lamar and UT Rio Grande Valley, scheduled for October 31 at 8:00PM ET: This market will resolve to "Lamar" if Lamar win the game. This market will resolve to "UT Rio Grande Valley" if UT Rio Grande Valley 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 to Yes if either Lamar or UT Rio Grande Valley wins the game as originally scheduled for October 31, 2026. If the game is postponed but begins within 48 hours of the original start time, the market remains open and resolves based on the official final result. If the game is cancelled or not started within 48 hours, the market resolves to a fair price. Kalshi is not affiliated with the NCAA, and all trademarks belong to 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. The differing prices on Polymarket and Kalshi for the Lamar vs. UT Rio Grande Valley game stem from several factors. Each platform attracts a different user base with varying levels of expertise and information. Trading mechanisms also differ; Polymarket may utilize a different order book structure or fee schedule than Kalshi, influencing price discovery. Furthermore, the volume traded on each platform can impact price volatility and create temporary discrepancies. These variations highlight the dynamic nature of prediction markets and the influence of platform-specific characteristics.