This event group covers prediction markets on the Liga MX professional soccer match between CF Cruz Azul and Tigres de la UANL scheduled for February 15, 2026. Markets track three mutually exclusive outcomes: Cruz Azul win, Tigres win, or draw, all evaluated at the conclusion of 90 minutes plus stoppage time (regular play only).
Polymarket's draw market contains a unique cancellation clause that resolves YES if the game is canceled with no make-up, while Kalshi and Polymarket's win markets do not address this scenario, creating potential payout inconsistency.
Hero Tip:
Monitor Liga MX official sources for any postponement or cancellation announcements. If cancellation occurs, Polymarket's draw market may resolve YES while win markets resolve NO, creating an arbitrage opportunity or hedge scenario. Clarify with Polymarket whether the cancellation clause will be enforced or waived.
Critical Divergence Points:
Kalshi:
Three mutually exclusive binary markets (Tigres win, Cruz Azul win, tie) each resolve YES if their outcome occurs after 90 minutes plus stoppage time. No explicit cancellation clause provided. Key Quote: 'If Tigres wins...then the market resolves to Yes' (and similarly for other outcomes).
Polymarket:
Three separate markets with explicit postponement and cancellation handling. Win markets (Cruz Azul, Tigres) resolve NO on cancellation; draw market uniquely resolves YES on cancellation with no make-up game. Key Quote: 'If the game is canceled entirely, with no make-up game, this market will resolve to Yes' (draw market only).
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.