This event group covers a professional German DEL (Deutsche Eishockey Liga) ice hockey match between Nurnberg Ice Tigers and Iserlohn Roosters scheduled for February 25, 2026. Both prediction markets are wagering on the outcome of this single game, with resolution based on the final score including overtime and shootout results.
Kalshi market contains a logical contradiction where both possible game outcomes (Tigers win and Roosters win) are mapped to the same resolution state (Yes), making the market fundamentally unresolvable and creating data integrity failure.
Hero Tip:
Avoid Kalshi until the market is corrected. The Yes/No structure appears to have a copy-paste error in the resolution conditions. Polymarket's binary outcome design is logically sound and should be the trusted reference for this event.
Critical Divergence Points:
Polymarket: Binary outcome market with clear mutually exclusive resolution paths. Resolves to either team name based on final score. Handles edge cases (postponement keeps market open, cancellation resolves 50-50). Includes shootout scoring rule. Key Quote: 'If Nurnberg Ice Tigers win, the market will resolve to Nurnberg Ice Tigers. If Iserlohn Roosters win, the market will resolve to Iserlohn Roosters.'
Kalshi: Yes/No market with unresolvable logic. Both Tigers win and Roosters win conditions are stated to resolve to Yes, creating logical impossibility. No edge case handling specified. Key Quote: 'If Nuremberg Ice Tigers wins...then the market resolves to Yes. If Iserlohn Roosters 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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