TOTAL VOLUME:
$61.7b
24H VOL:
$229,178,007
24H TRANSACTIONS:
600,069,390
OPEN INTEREST:
$1,336,753,738
577,196
Markets across
14,485
events
MATCHED EVENTS:
4,077
PLATFORM COVERAGE:
4
Polymarket:
50%
VS.
Kalshi:
50%
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the women's college basketball game between Kansas City Roos and South Dakota State Jackrabbits scheduled for February 14, 2026. The markets resolve based on which team wins the game, with specific handling for postponements and cancellations.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi uses a binary Yes/No structure that resolves Yes for any game outcome, while Polymarket uses categorical winner selection. Cancellation handling is explicit on Polymarket (50-50 split) but absent from Kalshi's stated rules.Hero Tip:
Kalshi's market is fundamentally a game-occurrence bet (does the game produce a winner?), not a winner-selection bet. Polymarket directly picks the winner. Verify Kalshi's cancellation protocol with support before trading, as it is not specified in the provided rules.Critical Divergence Points:
Kalshi:
Binary Yes/No structure. Resolves to Yes if either Kansas City or South Dakota State wins. No explicit handling for cancellation or postponement stated.Polymarket:
Categorical winner selection. Resolves to 'Kansas City Roos' or 'South Dakota State Jackrabbits' based on final score. Postponed games remain open until completion; canceled games with no make-up resolve 50-50.
Our PredictionHero Resolution Divergence Alerts (RDA) are there to help users identify potential differences across platforms. They do not replace or supersede the official rules and description of any prediction market. Users are solely responsible for reviewing and understanding the applicable rules and resolution criteria before placing any trade or bet. If you notice a potential inconsistency, discrepancy, or error in an alert, please report it to our team so we can review and improve the accuracy of our data.
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