This event group covers the women's college basketball game between Liberty Flames and Sam Houston Bearkats scheduled for March 12, 2026 at 12:30 PM ET. The markets resolve based on which team wins the game, with provisions for postponement or cancellation.
Kalshi's resolution logic is logically incoherent: both possible game outcomes (Liberty win or Sam Houston win) resolve to Yes, making the market unresolvable as a meaningful prediction instrument. Polymarket uses standard categorical resolution (team name). The two platforms are incompatible.
Hero Tip:
Do not trade the Kalshi market until its resolution criteria are clarified. The current logic suggests either a platform error or a misunderstanding of the market type. Polymarket's categorical structure is standard and resolvable. Treat Kalshi as unreliable pending correction.
Critical Divergence Points:
Kalshi:
Binary Yes resolution for all outcomes. Both Liberty win and Sam Houston win trigger Yes resolution. Quote: 'If Liberty wins...then the market resolves to Yes. If Sam Houston wins...then the market resolves to Yes.' This creates a logical contradiction—there is no No outcome.
Polymarket:
Categorical outcome resolution by team name. Liberty win resolves to 'Liberty Flames', Sam Houston win resolves to 'Sam Houston Bearkats'. Includes postponement (market stays open) and cancellation (50-50 split) provisions. Quote: 'If the Liberty Flames win, the market will resolve to Liberty Flames. If the Sam Houston Bearkats win, the market will resolve to Sam Houston Bearkats.'
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.