This event group covers a women's college basketball game between Southern Utah Thunderbirds and Utah Tech Trailblazers scheduled for February 28, 2026 at 4:00 PM ET. Markets across Polymarket and Kalshi are designed to resolve based on the final game outcome, with provisions for postponement and cancellation scenarios.
Kalshi market contains a logical contradiction where both mutually exclusive outcomes (Southern Utah win and Utah Tech win) are mapped to the same resolution (Yes), making the market unresolvable. Polymarket uses standard binary win-loss logic.
Hero Tip:
This is a critical data integrity failure on Kalshi. The market cannot resolve correctly because both teams cannot win the same game. Do not trade on Kalshi until the platform corrects the resolution logic. Polymarket is the only reliable source for this event.
Critical Divergence Points:
Polymarket:
Standard binary resolution: Southern Utah win resolves to 'Southern Utah Thunderbirds', Utah Tech win resolves to 'Utah Tech Trailblazers'. Cancellation without makeup resolves 50-50. Postponement keeps market open. Key Quote: 'If the Southern Utah Thunderbirds win, the market will resolve to Southern Utah Thunderbirds. If the Utah Tech Trailblazers win, the market will resolve to Utah Tech Trailblazers.'
Kalshi:
Logically contradictory: both Southern Utah win and Utah Tech win are mapped to Yes resolution. This violates basic game logic where only one team can win. Key Quote: 'If Southern Utah wins...then the market resolves to Yes. If Utah Tech 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.