This event group covers the women's college basketball game between Seton Hall Pirates and Missouri Tigers scheduled for March 19, 2026. The market resolves based on which team wins the game, with provisions for postponement or cancellation.
Kalshi resolves to YES for both possible outcomes (either team winning), making the market logically contradictory and unresolvable. Polymarket correctly resolves to either 'Seton Hall Pirates' or 'Missouri Tigers' based on the actual game winner, with proper handling of postponement and cancellation scenarios.
Hero Tip:
Avoid Kalshi entirely — its market structure is fundamentally broken and will resolve YES regardless of outcome. Trade only on Polymarket, which has coherent binary resolution logic (Seton Hall Pirates vs. Missouri Tigers) and explicit rules for postponement and cancellation.
Critical Divergence Points:
Kalshi: Outlier: Resolves to YES if Seton Hall wins AND YES if Missouri wins, creating a logical impossibility where both mutually exclusive outcomes trigger the same resolution. Key quote: 'If Seton Hall wins...then the market resolves to Yes. If Missouri wins...then the market resolves to Yes.'
Polymarket: Aligned with sound market design: Resolves to 'Seton Hall Pirates' if Seton Hall wins or 'Missouri Tigers' if Missouri wins, with explicit contingency rules for postponement (market remains open) and cancellation without makeup (50-50 split). Key quote: 'If the Seton Hall Pirates win, the market will resolve to Seton Hall Pirates. If the Missouri Tigers win, the market will resolve to Missouri Tigers.'
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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