This market resolves based on the outcome of the Golden State Warriors vs. Detroit Pistons NBA game scheduled for March 20, 2026 at 7:30 PM ET in Detroit. The winner is determined by the final score including any overtime periods, with specific provisions for postponement and cancellation scenarios.
Kalshi's market resolves YES for ANY outcome (either team winning), making it logically incoherent and fundamentally unresolvable. Polymarket correctly structures mutually exclusive outcomes (Warriors vs. Pistons moneyline, spreads, totals, and player props) with proper resolution criteria.
Hero Tip:
Avoid the Kalshi moneyline market entirely — it is broken and will resolve YES regardless of the game result. All player prop and spread markets on Polymarket are resolvable and use consistent NBA box score sources; use those for settlement confidence.
Critical Divergence Points:
Kalshi: Outlier: Kalshi's moneyline market (items 1-2) states 'If Detroit wins... then the market resolves to Yes' AND 'If Golden State wins... then the market resolves to Yes.' This creates a logical contradiction where every possible outcome resolves YES, violating basic market design. The market is unresolvable.
Polymarket: Aligned with standard market logic: Polymarket structures the moneyline as a mutually exclusive choice ('If the Warriors win, the market will resolve to Warriors. If the Pistons win, the market will resolve to Pistons.'), with clear tie-breaking and postponement rules. All 46 player prop markets use consistent NBA.com box score sources and include inactive-player clauses.
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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