Fairleigh Dickinson Knights vs. Central Connecticut State Blue Devils (W)
Volume:
$69,456
Markets
Outcome
Chance %
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Liquidity
Volume
24h
7d
Open Interest
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Description
This event group covers the women's college basketball matchup between Fairleigh Dickinson Knights and Central Connecticut State Blue Devils scheduled for February 14, 2026 at 1:00 PM ET. The markets track which team wins the game, with resolution based on the final score including overtime.
Kalshi market logic is internally contradictory and unresolvable. Both possible game outcomes (FDU win and Central Connecticut win) are mapped to the same resolution (Yes), making the market outcome predetermined and meaningless. Polymarket provides coherent binary resolution logic.
Hero Tip:
This is a critical data integrity failure on Kalshi. The market cannot function as described. Recommend trading only on Polymarket until Kalshi corrects the resolution criteria. If forced to settle Kalshi, escalate to PredictionHero compliance as the market violates basic logical consistency.
Critical Divergence Points:
Kalshi: Tautological resolution: both FDU win and Central Connecticut win resolve to Yes. This makes the market outcome independent of actual game result. Key Quote: If FDU wins resolves Yes; If Central Connecticut wins resolves Yes.
Polymarket: Standard binary winner-take-all: FDU win resolves to Fairleigh Dickinson Knights, Central Connecticut win resolves to Central Connecticut State Blue Devils. Includes postponement (market stays open) and full cancellation (50-50 split) provisions.
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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