This event group covers a men's college basketball game between Valparaiso Beacons and UIC Flames scheduled for February 21, 2026 at 3:00 PM ET. Markets include moneyline (winner), two spread variations (-6.5 and -7.5), and a total points over/under (138.5). The group tests consistency of resolution logic across Polymarket and Kalshi platforms.
Kalshi market contains a logical contradiction where both possible game outcomes (UIC win and Valparaiso win) are stated to resolve to the same result (Yes), making the market fundamentally unresolvable and unable to differentiate between outcomes.
Hero Tip:
Avoid trading on Kalshi until the market description is corrected. The stated logic creates a scenario where no outcome can be distinguished from another. Polymarket markets are internally consistent and follow standard sports betting resolution conventions; prioritize those for trading.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name; spreads resolve based on margin (UIC -6.5 requires 7+ point win; UIC -7.5 requires 8+ point win); totals resolve based on combined score threshold (139+). All markets include postponement (remains open) and cancellation (50-50 split) provisions. Key Quote: 'The result will be determined based on the final score including any overtime periods.'
Kalshi: Market states both UIC win and Valparaiso win resolve to Yes, creating logical contradiction. No differentiation mechanism exists between outcomes. Key Quote: 'If UIC wins...then resolves to Yes. If Valparaiso wins...then 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.
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