This event group covers a women's college basketball game between the Charleston Cougars and Towson Tigers scheduled for February 20, 2026 at 7:00 PM ET. Markets on Polymarket and Kalshi are attempting to settle based on the final game outcome, but the resolution logic differs fundamentally between platforms.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Charleston win and Towson win) are mapped to the same resolution value (Yes), making the market fundamentally unresolvable and creating data integrity failure.
Hero Tip:
Avoid trading the Kalshi market entirely until the platform corrects the resolution language. The Polymarket version is the only logically sound market in this group. If you hold Kalshi positions, escalate to support immediately for clarification or cancellation.
Critical Divergence Points:
Polymarket: Binary winner-take-all structure. Resolves to 'Charleston Cougars' if Charleston wins; resolves to 'Towson Tigers' if Towson wins. Postponement keeps market open; cancellation with no makeup resolves 50-50. Final score including overtime determines outcome. Key quote: 'If the Charleston Cougars win, the market will resolve to Charleston Cougars. If the Towson Tigers win, the market will resolve to Towson Tigers.'
Kalshi: Defective Yes/No structure with logical contradiction. Both outcomes incorrectly map to Yes. Key quote: 'If Charleston wins... resolves to Yes. If Towson wins... resolves to Yes.' This is unresolvable because only one team can win, but both outcomes claim the same resolution value.
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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