A college basketball game between North Florida Ospreys and Stetson Hatters scheduled for February 21, 2026 at 3:30 PM ET. Markets cover moneyline (winner), multiple point spreads, and over/under total points wagered across Polymarket and Kalshi platforms.
Kalshi market contains a logical contradiction where both possible outcomes (North Florida win and Stetson win) resolve to the same result (Yes), making the market fundamentally unresolvable and unambiguous.
Hero Tip:
Do not trade the Kalshi market in its current form. The market cannot differentiate between the two teams' victories. Rely exclusively on Polymarket's moneyline and spread markets, which have clear, mutually exclusive resolution paths. Monitor for Kalshi market clarification or emergency resolution.
Critical Divergence Points:
Polymarket: Moneyline resolves to either North Florida Ospreys or Stetson Hatters based on final score. Spread markets (multiple thresholds: -3.5, -2.5, -1.5) resolve based on point differential. Over/Under markets resolve based on combined points vs. threshold (162.5 or 163.5). All include postponement continuation and 50-50 cancellation. Key Quote: The result will be determined based on the final score including any overtime periods.
Kalshi: Market states both outcomes resolve identically to Yes: If North Florida wins, resolves Yes. If Stetson wins, resolves Yes. This creates logical impossibility with no differentiation mechanism. Key Quote: If North Florida wins... then the market resolves to Yes. If Stetson wins... then the market resolves to Yes.
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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