In the upcoming Euroleague basketball game, scheduled for April 10 at 2:30PM ET:
If the Partizan win, the market will resolve to "Partizan".
If the Zalgiris Kaunas win, the market will resolve to "Zalgiris Kaunas".
If the game is postponed, this market will remain open until the game has been completed.
If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Kalshi's resolution rules contain a logical contradiction: both outcomes (Partizan win and Zalgiris Kaunas win) resolve to YES, making the market fundamentally unresolvable. Polymarket uses standard binary logic where exactly one outcome resolves to a named winner.
Hero Tip:
Do not trade on Kalshi's version of this market — it is logically broken and cannot be settled. Polymarket's market is resolvable: bet on the team you expect to win, and the market will resolve to that team's name or the opposing team's name. Kalshi's contradiction means both outcomes trigger YES, which violates basic market mechanics.
Critical Divergence Points:
Kalshi: Outlier: Contains a logical contradiction. The market states 'If KK Partizan Belgrade wins...then the market resolves to Yes' AND 'If BC Zalgiris Kaunas wins...then the market resolves to Yes.' This means every possible outcome (either team winning) resolves to YES, making the market unresolvable and unhedgeable.
Polymarket: Standard binary logic: resolves to 'Partizan' if Partizan wins, 'Zalgiris Kaunas' if Zalgiris Kaunas wins, with explicit postponement and cancellation clauses. Exactly one outcome per scenario is defined.
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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