This event group covers a La Liga 2 soccer match between FC Andorra and Real Zaragoza scheduled for February 22, 2026. Markets across Polymarket and Kalshi predict the outcome (win, loss, or draw) within 90 minutes of regular play plus stoppage time. The core resolution hinges on the final match result and consistent handling of postponements and cancellations.
Cancellation handling diverges between platforms. Polymarket explicitly resolves the draw market YES and win markets NO if the game is canceled with no make-up. Kalshi provides no cancellation clause, leaving resolution ambiguous.
Hero Tip:
Monitor official La Liga 2 announcements for any postponement or cancellation. If canceled with no reschedule, Polymarket's draw market has an automatic YES resolution while win markets resolve NO—a one-sided payout structure. Kalshi's lack of cancellation language means you should seek clarification from the platform or assume standard sports betting convention (all markets void or cancel). Hedge accordingly.
Critical Divergence Points:
Polymarket:
Explicit three-way cancellation logic: draw market resolves YES if game is canceled with no make-up; both win markets (Zaragoza and Andorra) resolve NO. Key Quote: 'If the game is canceled entirely, with no make-up game, this market will resolve to Yes' (draw) and 'this market will resolve No' (win markets).
Kalshi:
No cancellation or postponement clause provided. Markets cover only the three outcomes (Zaragoza win, Tie, Andorra win) after 90 minutes plus stoppage time. Silence on edge cases creates settlement ambiguity.
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.