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 Wisconsin Badgers and UCLA Bruins scheduled for February 22, 2026 at 5:30 PM ET. Markets across platforms are betting on the binary outcome: whether Wisconsin wins or UCLA wins the game.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution logic contains a logical contradiction: both Wisconsin win and UCLA win are stated to resolve to Yes, which is impossible in a binary sports outcome market. This makes the Kalshi market fundamentally unresolvable as written.Hero Tip:
Treat Kalshi as unresolvable until the platform clarifies the actual resolution rule. The contradiction suggests a data integrity failure. Trade only on Polymarket, which has clear, mutually exclusive resolution paths.Critical Divergence Points:
Polymarket:
Clean binary logic: Wisconsin win resolves to Wisconsin Badgers, UCLA win resolves to UCLA Bruins. Postponement keeps market open; total cancellation resolves 50-50. Resolution based on final score including overtime.Kalshi:
Contradictory rule: states both Wisconsin win and UCLA win resolve to Yes. This is logically impossible for a binary event and indicates either a platform error or incomplete rule specification.
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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