This event group covers a men's college basketball game between Davidson Wildcats and Fordham Rams scheduled for February 21, 2026 at 2:00 PM ET. Markets include moneyline (winner), point spreads, and over/under totals at multiple thresholds.
Kalshi moneyline market contains a logical contradiction: both possible outcomes (Fordham win and Davidson win) are mapped to the same resolution value (Yes), making the market unresolvable and creating data integrity failure.
Hero Tip:
Do not trade the Kalshi moneyline market in its current form. Contact Kalshi support to clarify whether the market should resolve Yes only for one team, or if there is a No outcome option. Polymarket markets (moneyline, spreads, totals) are logically sound and can be traded with confidence, provided the game is played as scheduled.
Critical Divergence Points:
Polymarket: Moneyline resolves to team name of winner (Davidson Wildcats or Fordham Rams). Spreads resolve based on margin: Fordham -1.5 wins if Fordham wins by 2+; Davidson -1.5 wins if Davidson wins by 2+. Totals (O/U 132.5 and O/U 131.5) resolve based on combined points. All markets remain open if postponed; resolve 50-50 if canceled. Key Quote: 'The result will be determined based on the final score including any overtime periods.'
Kalshi: Moneyline market states both outcomes resolve to Yes: 'If Fordham wins...resolves to Yes. If Davidson wins...resolves to Yes.' This is a logical contradiction that prevents proper market resolution. No clarification provided on what happens if game is postponed or canceled.
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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