This event group covers the women's college basketball matchup between Indiana State Sycamores and Drake Bulldogs scheduled for February 14, 2026 at 3:00 PM ET. The markets track which team wins the game, with resolution based on the final score including any overtime periods.
Kalshi market structure is logically contradictory: both possible outcomes (Drake win and Indiana State win) are stated to resolve to Yes, making the market unresolvable. Polymarket uses standard categorical resolution (team name outcomes), which is consistent and resolvable.
Hero Tip:
Do not trade Kalshi until the market logic is corrected. The statement that both Drake and Indiana State wins resolve to Yes is a data integrity failure. Polymarket's market is safe to trade. Confirm Kalshi's actual resolution mechanism with platform support before proceeding.
Critical Divergence Points:
Kalshi:
Both Drake win and Indiana State win stated to resolve to Yes. This is logically impossible for a binary market and suggests either a documentation error or a critical market design flaw. Quote: 'If Drake wins... then the market resolves to Yes' AND 'If Indiana St. wins... then the market resolves to Yes.'
Polymarket:
Categorical resolution: Indiana State win resolves to 'Indiana State Sycamores', Drake win resolves to 'Drake Bulldogs'. Postponement keeps market open; cancellation with no makeup resolves 50-50. Quote: 'If the Indiana State Sycamores win, the market will resolve to "Indiana State Sycamores". If the Drake Bulldogs win, the market will resolve to "Drake Bulldogs".'
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.