This event group covers a single AHL (American Hockey League) regular season game between the Charlotte Checkers and Hartford Wolf Pack scheduled for March 29, 2026. Both Polymarket and Kalshi are offering prediction markets on the outcome of this game, with resolution based on the final score including overtime and shootout results.
Kalshi's resolution logic is internally contradictory: both Charlotte Checkers win and Hartford Wolf Pack win are stated to resolve to Yes, creating a market with no valid No resolution path. This is a data integrity failure that makes the Kalshi market fundamentally unresolvable.
Hero Tip:
Avoid trading on Kalshi until the platform corrects the resolution criteria. The market cannot logically settle because both possible outcomes map to the same resolution value. Polymarket offers a clear, resolvable binary structure and should be treated as the reliable reference for this event.
Critical Divergence Points:
Polymarket: Binary outcome market with mutually exclusive resolution paths. Charlotte Checkers win resolves to 'Charlotte Checkers', Hartford Wolf Pack win resolves to 'Hartford Wolf Pack'. Postponement keeps market open; cancellation without makeup resolves 50-50. Includes shootout handling (one goal added to winner's score).
Kalshi: Yes/No market with logical contradiction. States 'If Charlotte Checkers wins...resolves to Yes' and separately 'If Hartford Wolf Pack wins...resolves to Yes'. Both possible game outcomes map to Yes, leaving no valid condition for No resolution. This is a critical data error.
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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