Polymarket uses three separate binary markets with asymmetric cancellation rules, while Kalshi uses a single three-outcome market. The draw/tie market on Polymarket resolves Yes on cancellation, but win markets resolve No, creating logical inconsistency across the platform pair.
Hero Tip:
Understand that Polymarket's draw market has a unique cancellation rule (Yes) that differs from win markets (No). Kalshi's three-outcome structure avoids this asymmetry. If you hold positions across both platforms, a cancellation scenario will resolve differently: Polymarket draw bets pay out, but Polymarket win bets do not. Verify the match status through official CONMEBOL Libertadores channels before settlement.
Critical Divergence Points:
Polymarket: Three independent binary markets: (1) Club Always Ready win resolves Yes if Always Ready wins, No otherwise; (2) Draw resolves Yes if match ends in draw OR is canceled with no makeup; (3) Mirassol win resolves Yes if Mirassol wins, No otherwise. Key quote: 'If the game is canceled entirely, with no make-up game, this market will resolve to Yes' (draw market) vs 'this market will resolve to No' (win markets).
Kalshi: Single market with three mutually exclusive outcomes, each resolving to Yes if met: Mirassol wins, Always Ready wins, or Tie. All outcomes evaluated at 90 minutes plus stoppage time. No explicit cancellation clause provided. Key quote: 'If Mirassol wins...then the market resolves to Yes. If Always Ready wins...then the market resolves to Yes. If Tie wins...then the market 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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