This event group covers a single AHL (American Hockey League) matchup between the Texas Stars and Henderson Silver Knights scheduled for March 22, 2026 at 8:00 PM EDT. Both Polymarket and Kalshi have created markets to predict the winner of this professional hockey game.
Kalshi market contains a logical contradiction where both possible game outcomes (Texas Stars win OR Henderson Silver Knights win) are mapped to the same resolution state (Yes), making the market mathematically unresolvable. Polymarket uses standard binary winner-take-all logic with explicit edge-case handling.
Hero Tip:
This is a critical market definition error on Kalshi. Both outcomes cannot resolve to Yes in a binary market. Before trading, request clarification from Kalshi: is this meant to be a "Will the game be completed?" market, or is the Texas Stars condition a drafting error that should resolve to No? Polymarket's market is well-defined and tradeable as written.
Critical Divergence Points:
Polymarket: Standard binary winner-take-all structure. Texas Stars victory resolves to Texas Stars, Henderson victory resolves to Henderson. Postponement keeps market open; cancellation without makeup resolves 50-50. Includes shootout scoring rule (one goal added to winner). Source: theahl.com official schedule.
Kalshi: Logically contradictory definition. Both Henderson Silver Knights win AND Texas Stars win are stated to resolve to Yes, creating an impossible resolution state. No explicit handling of postponement or cancellation. Market cannot settle under current terms.
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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