TOTAL VOLUME:
$134b
24H VOL:
$87,997,338
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,395,155,069
395,664
Markets across
30,076
events
MATCHED EVENTS:
2,626
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 7, 10:14 AM EST
Kalshi
This group resolves based on the outcome of a single FIBA Women's World Cup basketball game between Nigeria and France, scheduled for September 7, 2026. The market determines which country wins the game, with a 50-50 split if the game is canceled without a reschedule.
In the upcoming FIBA Women's World Cup game, scheduled for September 7 at 8:30AM ET: If the Nigeria win, the market will resolve to "Nigeria". If the France win, the market will resolve to "France". 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 result will be determined based on the final score including any overtime periods.
Markets resolve based on the official outcome of the France vs Nigeria women's professional FIBA World Cup basketball game scheduled for September 7, 2026, at 8:30 AM EDT. If either team wins, the corresponding market resolves to Yes. Should the game be postponed but commence within 48 hours of the original start time, markets remain open and resolve according to the final result. If the game is cancelled or fails to start within 48 hours, all markets resolve to a fair price. Kalshi clarifies it is not officially associated with the governing league, and all trademarks belong to their respective owners.
Prices on Polymarket and Kalshi for the Nigeria vs. France match can diverge due to 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, leading to differing opinions. Trading volume also plays a role; lower volume on one platform can result in greater price volatility. Furthermore, the specific rules and fee structures of each platform can influence trading behavior and, consequently, the displayed prices. These factors contribute to the dynamic pricing observed across prediction markets.