This event group covers the La Liga match between FC Barcelona and Levante UD scheduled for February 22, 2026. Markets across Polymarket and Kalshi assess the outcome of the match within 90 minutes plus stoppage time, including win/loss outcomes and draw scenarios.
Polymarket's draw market contains a cancellation clause that resolves to YES if the game is canceled with no makeup, creating asymmetric payoff profiles across the three outcome markets. Kalshi's omnibus market structure provides no explicit cancellation guidance.
Hero Tip:
Traders should recognize that on Polymarket, a draw position is hedged against cancellation risk, while win positions are not. On Kalshi, clarify the platform's cancellation protocol before Feb 22, 2026. If both platforms are used for arbitrage, the draw market on Polymarket may trade at a premium due to embedded cancellation optionality.
Critical Divergence Points:
Polymarket: Three separate binary markets with unified postponement logic but divergent cancellation logic. Barcelona Win and Levante Win resolve NO on cancellation. Draw market resolves YES on cancellation: 'If the game is canceled entirely, with no make-up game, this market will resolve YES.'
Kalshi: Single omnibus market covering all three outcomes (Tie, Levante, Barcelona) with no explicit cancellation or postponement clause. Implicitly assumes game completion: 'If [outcome] wins the Barcelona vs Levante professional La Liga soccer game originally scheduled for Feb 22, 2026 after 90 minutes plus stoppage time, 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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