A men's college basketball game between St. Thomas (MN) Tommies and Kansas City Roos scheduled for February 14, 2026 at 8:00 PM ET. Markets cover the moneyline winner, point spread outcomes, and total points over/under at multiple thresholds.
Kalshi moneyline market contains a logical contradiction where both possible outcomes (St. Thomas win and Kansas City win) are mapped to the same resolution (Yes), making it impossible to distinguish a winner and rendering the market unresolvable.
Hero Tip:
Do not trade the Kalshi moneyline. Use Polymarket moneyline exclusively for winner determination. All spread and total markets are logically sound and consistent: resolve based on final score including overtime, remain open if postponed, and resolve 50-50 if canceled with no makeup game.
Critical Divergence Points:
Kalshi: Moneyline market has both outcomes resolving to Yes. Quote: 'If St. Thomas wins the St. Thomas at Kansas City men's college basketball game originally scheduled for Feb 14, 2026, then the market resolves to Yes. If Kansas City wins the St. Thomas at Kansas City men's college basketball game originally scheduled for Feb 14, 2026, then the market resolves to Yes.' This is a data integrity failure.
Polymarket: Moneyline resolves to winner name: 'If the St. Thomas (MN) Tommies win, the market will resolve to St. Thomas (MN) Tommies. If the Kansas City Roos win, the market will resolve to Kansas City Roos.' Spread and total markets use clear thresholds (152.5, 153.5, 154.5 points) with consistent postponement and cancellation rules.
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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