This event group covers the women's college basketball game between Princeton Tigers and Harvard Crimson scheduled for February 28, 2026 at 6:00 PM ET. The markets resolve based on which team wins the game, with specific provisions for postponements and cancellations.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Harvard win or Princeton win) resolve to Yes, leaving no valid No resolution path and rendering the market unresolvable. Polymarket's logic is sound and unambiguous.
Hero Tip:
Kalshi's market is logically broken and should not be traded until the platform clarifies the resolution criteria. The statement that both teams winning resolves to Yes suggests either a drafting error or missing cancellation/postponement logic. Polymarket provides clear, resolvable criteria and should be treated as the reliable source for this event.
Critical Divergence Points:
Polymarket:
Binary outcome market: resolves to Princeton Tigers if Princeton wins, Harvard Crimson if Harvard wins. Postponements keep market open; cancellations with no make-up resolve 50-50. Resolution based on final score including overtime. Key Quote: If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Kalshi:
Logically contradictory structure: states If Harvard wins resolve to Yes AND If Princeton wins resolve to Yes, creating no valid No outcome. Missing explicit guidance on postponements and cancellations. Key Quote: If Harvard wins...resolves to Yes. If Princeton wins...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.