TOTAL VOLUME:
$134.1b
24H VOL:
$133,388,117
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,436,095,462
405,232
Markets across
30,526
events
MATCHED EVENTS:
2,693
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 26, 9:54 PM EST
Kalshi
This group covers various markets related to the college football game between Incarnate Word and Texas State, scheduled for September 26th. Markets include the winner of the game, point spreads, and over/under totals for points scored by both teams and in different game segments.
In the upcoming college football game between Incarnate Word and Texas State, scheduled for September 26 at 6:00PM ET: This market will resolve to "Incarnate Word" if Incarnate Word win the game. This market will resolve to "Texas State" if Texas State 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 the specified team wins the college football game originally scheduled for September 26, 2026. If the game is postponed but begins within 48 hours of the originally scheduled start time, the market stays open and resolves based on the official final result. If the game is cancelled or not started within 48 hours of the originally scheduled start, the market resolves to a fair price. Kalshi is not affiliated with the NCAA, and all trademarks, logos, and brand names are the property of their respective owners.
Differences in prices between Polymarket and Kalshi for the Incarnate Word vs. Texas State game can arise from several factors. 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. Each platform attracts a different user base with varying levels of expertise and access to information, which influences trading behavior. Furthermore, the specific market structures and fee schedules on each platform can impact pricing. Trading volume also plays a role; lower volume on one platform can lead to greater price volatility and divergence from the other. These factors contribute to the dynamic pricing observed across different prediction markets.