This event group covers the outcome of an NCAA Women's Basketball game between SMU Mustangs and California Golden Bears scheduled for March 1, 2026 at 4:00 PM ET. Markets resolve based on the final score including overtime, with provisions for postponement or cancellation.
Kalshi's resolution statement contains a logical contradiction: both SMU win and California win are stated to resolve to Yes, which is impossible in a binary Yes/No market. This makes Kalshi's market fundamentally unresolvable as written.
Hero Tip:
Avoid trading on Kalshi until the platform clarifies whether the California resolution should read 'resolves to No' instead of 'resolves to Yes'. Polymarket offers clear, unambiguous resolution logic and should be preferred for this event.
Critical Divergence Points:
Polymarket: Categorical resolution: market resolves to either 'SMU Mustangs' or 'California Golden Bears' based on final score including overtime. Cancellation with no makeup resolves 50-50. Postponement keeps market open. Key quote: 'If the SMU Mustangs win, the market will resolve to SMU Mustangs. If the California Golden Bears win, the market will resolve to California Golden Bears.'
Kalshi: Binary Yes/No structure with contradictory logic: states both 'If SMU wins...resolves to Yes' and 'If California wins...resolves to Yes'. This creates logical impossibility. Key quote: 'If SMU wins the SMU at California women's college basketball game originally scheduled for Mar 1, 2026, then the market resolves to Yes. If California 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.
Follow the signals, not the noise
Get insights on market conviction, notable shifts, and what the data is quietly signaling.