This event group covers three linked prediction markets on the Argentina Primera Division soccer match between CA Talleres (Córdoba) and CA San Lorenzo de Almagro scheduled for February 28, 2026. The markets assess whether Talleres wins, San Lorenzo wins, or the match ends in a draw, all measured at the conclusion of 90 minutes plus stoppage time (regular play only).
Cancellation handling diverges between platforms. Polymarket's draw market resolves YES on cancellation, while its win markets resolve NO. Kalshi provides no explicit cancellation clause, creating settlement ambiguity.
Hero Tip:
Verify game status with AFA before settlement. If canceled with no makeup: Polymarket draw resolves YES, Polymarket win markets resolve NO. Kalshi's behavior on cancellation is undefined—request clarification from the platform or assume void. This mismatch does not affect normal play resolution (win/loss/draw outcomes are aligned).
Critical Divergence Points:
Polymarket: Three separate markets with conflicting cancellation logic. Draw market: 'If the game is canceled entirely, with no make-up game, this market will resolve Yes.' Win markets (Talleres and San Lorenzo): 'If the game is canceled entirely, with no make-up game, this market will resolve No.' All three measure 90 minutes plus stoppage time only.
Kalshi: Three markets (Tie wins, Talleres wins, San Lorenzo wins) all resolve YES if their respective outcome occurs after 90 minutes plus stoppage time. No explicit cancellation clause provided; standard sports betting void convention likely applies but is not stated.
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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