TOTAL VOLUME:
$61.8b
24H VOL:
$236,615,189
24H TRANSACTIONS:
600,069,390
OPEN INTEREST:
$1,344,513,070
577,941
Markets across
14,530
events
MATCHED EVENTS:
4,090
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 a women's college basketball game between Cal State Bakersfield Roadrunners and Long Beach State Beach scheduled for February 28, 2026. The markets resolve based on which team wins the game, with provisions for postponements and cancellations.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution logic contains a logical contradiction: both possible game outcomes (Bakersfield win OR Long Beach win) are stated to resolve to YES in a binary YES/NO market, which is impossible. Polymarket provides coherent, mutually exclusive resolution paths.Hero Tip:
Avoid trading the Kalshi market until it is corrected or clarified by the platform. The Polymarket version is logically sound and should be your primary reference. A critical data integrity failure exists on Kalshi.Critical Divergence Points:
Polymarket:
Resolves to winning team name (Bakersfield Roadrunners or Long Beach State Beach). Postponements keep market open; full cancellations resolve 50-50. Mutually exclusive outcomes with clear edge case handling.Kalshi:
States both Bakersfield win and Long Beach win resolve to YES. This is logically contradictory in a binary market structure and makes the market unresolvable as written.
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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