TOTAL VOLUME:
$154.6b
24H VOL:
$333,432,169
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,971,286,090
419,492
Markets across
40,634
events
MATCHED EVENTS:
4,654
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 24, 11:58 AM EST
Kalshi
This group forecasts the outcome of a League One soccer match between AFC Wimbledon and Wycombe Wanderers on October 24, 2026. Markets exist for each team winning, or the game ending in a draw. Resolution relies on official match statistics, with fallback sources specified.
This event is for the upcoming League One game, scheduled for Saturday, October 24, 2026 between AFC Wimbledon and Wycombe Wanderers FC.
The event resolves based on the result of the Wimbledon vs Wycombe professional EFL League One soccer match scheduled for October 24, 2026, after 90 minutes plus stoppage time. A 'Yes' outcome occurs if Wimbledon wins (WIM market), Wycombe wins (WYW market), or the match ends in a tie (Tie market). All markets share identical secondary rules: if the game ends in a tie, the Tie market resolves to Yes; if the match is cancelled or rescheduled to more than 48 hours later, the markets resolve to a fair price. The event exclusively considers regulation time results, with no effect from extra time or penalty shootouts. Kalshi disclaims any official affiliation with the governing league, and all trademarks remain 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. Prices on Polymarket and Kalshi for the AFC Wimbledon vs. Wycombe match can differ due to several factors. Each platform attracts a different user base with varying levels of expertise and access to information. Trading volume also plays a role; higher volume on one platform can lead to more efficient price discovery. Furthermore, the specific market design and fee structures on Polymarket and Kalshi can influence trading behavior and, consequently, the resulting odds. These differences highlight the value of aggregating data across multiple venues to gain a more robust understanding of market sentiment.