A college basketball game between the Wyoming Cowboys and UNLV Runnin' Rebels scheduled for March 11, 2026 at 3:00 PM ET. Markets cover the moneyline winner, point spread outcomes, and total points over/under thresholds.
Kalshi's moneyline market contains a logical contradiction: both possible game outcomes (UNLV win and Wyoming win) are mapped to the same resolution (Yes), making the market fundamentally unresolvable and unable to differentiate between outcomes.
Hero Tip:
Do not trade the Kalshi moneyline market. It is logically broken and will always resolve Yes regardless of which team wins. Use only Polymarket's moneyline, spread, and total markets for this event, which have clear, mutually exclusive resolution criteria.
Critical Divergence Points:
Kalshi: Moneyline market maps both UNLV win and Wyoming win to Yes resolution, creating a tautology. This is a data integrity failure that makes the market unresolvable. Quote: 'If UNLV wins... then the market resolves to Yes. If Wyoming wins... then the market resolves to Yes.'
Polymarket: Moneyline resolves to the winning team's name (Wyoming Cowboys or UNLV Runnin' Rebels), with clear postponement (market stays open) and cancellation (50-50 split) protocols. Spread markets use explicit point thresholds (UNLV -3.5 and -4.5). Over/Under markets use explicit combined-score thresholds (154.5 and 153.5). All edge cases are well-defined. Quote: 'If the Wyoming Cowboys win, the market will resolve to Wyoming Cowboys. If the UNLV Runnin' Rebels win, the market will resolve to UNLV Runnin' Rebels.'
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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