This event group covers a single professional AHL (American Hockey League) game between the Tucson Roadrunners and Colorado Eagles scheduled for April 11, 2026 at 9:05 PM EDT. Markets on both Kalshi and Polymarket are betting on the outcome of this game, with resolution based on the final score including overtime and shootouts.
Kalshi's binary Yes/Yes structure is logically contradictory and unresolvable, while Polymarket uses a valid categorical winner-selection model. Additionally, the two platforms employ fundamentally different market architectures that cannot be reconciled.
Hero Tip:
Kalshi's market contains a critical logical flaw: both possible outcomes cannot resolve to Yes simultaneously. This makes the market unresolvable as currently written. Polymarket's market is properly structured with clear categorical outcomes and explicit edge-case handling. Avoid Kalshi until corrected; trade only Polymarket.
Critical Divergence Points:
Kalshi: Binary Yes/Yes structure where both winning conditions resolve to Yes, creating a logical impossibility. No edge-case handling provided. Key Quote: If Tucson Roadrunners wins...resolves to Yes. If Colorado Eagles wins...resolves to Yes.
Polymarket: Categorical winner-selection structure resolving to either Tucson Roadrunners or Colorado Eagles based on final score including overtime and shootouts. Includes postponement (market stays open) and cancellation (50-50) handling. Key Quote: If Tucson Roadrunners win, resolves to Tucson Roadrunners. If Colorado Eagles win, resolves to Colorado Eagles.
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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