This event group covers a single professional AHL (American Hockey League) game between the Milwaukee Admirals and Manitoba Moose scheduled for March 18, 2026 at 11:30 AM EDT. The markets track the outcome of this game, with resolution based on the final score including overtime and shootouts.
Kalshi market contains a logical contradiction where both possible game outcomes (Manitoba Moose win and Milwaukee Admirals win) are stated to resolve to Yes, making the market fundamentally unresolvable. Polymarket uses a clear categorical resolution model.
Hero Tip:
Do not trade Kalshi until the market logic is corrected. The contradiction makes it impossible to determine a valid resolution path. Polymarket's market is tradeable and uses standard categorical resolution (winner name). Confirm Kalshi's actual terms directly with the platform.
Critical Divergence Points:
Kalshi: Contradictory binary logic: states both Manitoba Moose victory and Milwaukee Admirals victory resolve to Yes. This is logically impossible for a mutually exclusive event. Key Quote: If Manitoba Moose wins resolves to Yes; If Milwaukee Admirals wins resolves to Yes.
Polymarket: Clear categorical resolution: resolves to team name of winner (Milwaukee Admirals or Manitoba Moose). Includes explicit edge case handling: postponement keeps market open until completion; cancellation with no makeup resolves 50-50. Includes shootout scoring rule (one goal added to winner). Key Quote: If Milwaukee Admirals win resolves to Milwaukee Admirals; If Manitoba Moose win resolves to Manitoba Moose.
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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