Central Connecticut State Blue Devils vs. St. Francis (PA) Red Flash (W)
Volume:
$630
Markets
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 Central Connecticut State Blue Devils and St. Francis (PA) Red Flash scheduled for February 28, 2026 at 2:00 PM ET. Both prediction markets resolve based on the final outcome of this single game, with provisions for postponement, cancellation, and overtime.
Kalshi's market structure contains a logical contradiction: both possible game outcomes (Central Connecticut St. wins OR St. Francis (PA) wins) are mapped to the same resolution value (Yes), making the market unable to differentiate between outcomes and therefore unresolvable.
Hero Tip:
Trade only on Polymarket for this event. Kalshi's market is fundamentally broken and cannot serve as a valid prediction instrument. Request clarification from Kalshi before committing capital.
Critical Divergence Points:
Polymarket: Binary outcome structure with clear differentiation. Resolves to winner name (Central Connecticut State Blue Devils or St. Francis (PA) Red Flash). Handles edge cases: postponement keeps market open until completion; cancellation without makeup resolves 50-50. Includes overtime in final score determination.
Kalshi: Tautological Yes-resolution structure. Both Central Connecticut St. win AND St. Francis (PA) win scenarios resolve to Yes, creating logical impossibility. No documented handling of postponement or cancellation scenarios.
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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