This event group covers the outcome of the Pittsburgh Panthers vs. Miami Hurricanes women's college basketball game scheduled for February 26, 2026 at 7:00 PM ET. Both platforms are betting on which team will win the matchup, with consistent resolution tied to the final score including overtime.
Kalshi uses binary Yes/No settlement structure while Polymarket uses categorical team-name settlement, despite both markets resolving on the identical underlying event (final game outcome). This is a representation divergence, not a logical one.
Hero Tip:
Both markets will resolve based on the same game result and both handle postponements and cancellations consistently. The divergence is purely in how the settlement value is expressed (Yes vs. team name). Confirm your platform's payout mechanics before entering positions, but arbitrage risk is minimal since the underlying resolution source is unified.
Critical Divergence Points:
Kalshi: Binary Yes resolution for either winner. Resolves to Yes if Pittsburgh wins OR if Miami wins. Key Quote: 'If Pittsburgh wins the Pittsburgh at Miami (FL) women's college basketball game originally scheduled for Feb 26, 2026, then the market resolves to Yes. If Miami (FL) wins... then the market resolves to Yes.'
Polymarket: Categorical team-name resolution. Resolves to Pittsburgh Panthers if Pittsburgh wins, or Miami Hurricanes if Miami wins. Key Quote: 'If the Pittsburgh Panthers win, the market will resolve to Pittsburgh Panthers. If the Miami Hurricanes win, the market will resolve to Miami Hurricanes.'
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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