Delaware Fightin' Blue Hens vs. Missouri State Bears
Volume:
$998,619
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A men's college basketball game between the University of Delaware Fightin' Blue Hens and Missouri State University Bears scheduled for February 14, 2026 at 3:00 PM ET. Multiple prediction markets track the moneyline winner, point spread outcomes, and total points scored in the game.
Kalshi's moneyline market contains a logical contradiction where both possible outcomes (Missouri State wins and Delaware wins) are specified to resolve to Yes, making the market fundamentally unresolvable. Polymarket's markets are logically coherent and resolvable.
Hero Tip:
Do not trade the Kalshi moneyline market due to the logical impossibility in its resolution rules. All Polymarket markets (moneyline, spreads, totals) have clear, mutually exclusive resolution criteria and should be used for trading this event.
Critical Divergence Points:
Kalshi: Moneyline market claims both Missouri State victory AND Delaware victory each resolve to Yes. This is logically impossible and makes the market unresolvable. The platform has failed to provide mutually exclusive outcomes.
Polymarket: Moneyline resolves to the winning team's name (Delaware Fightin' Blue Hens or Missouri State Bears). Spread markets (-7.5 and -8.5) resolve based on Missouri State margin of victory thresholds. Total markets (O/U 133.5 and 134.5) resolve based on combined points. All outcomes are mutually exclusive and resolvable.
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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