This event group covers a single AHL (American Hockey League) regular season game between the Springfield Thunderbirds and Iowa Wild scheduled for February 13, 2026 at 8:00 PM EST. The markets resolve based on the final outcome of the game, with provisions for postponements and cancellations.
Kalshi's resolution logic resolves both possible outcomes (Iowa Wild win and Springfield Thunderbirds win) to Yes, which is logically contradictory for a single game. Polymarket correctly resolves to the winning team's name. This is a data integrity failure on Kalshi's side.
Hero Tip:
Do not trade Kalshi's version of this market without clarification from the platform. The stated logic makes the market unresolvable as written—both outcomes cannot resolve to Yes. Polymarket's structure is standard and resolvable. Treat Kalshi as having a critical specification error.
Critical Divergence Points:
Kalshi: Binary Yes/No structure that resolves to Yes for both possible game outcomes (either team winning). This is logically impossible for a single game with two mutually exclusive outcomes. Quote: 'If Iowa Wild wins...resolves to Yes' AND 'If Springfield Thunderbirds wins...resolves to Yes.'
Polymarket: Categorical structure that resolves to the winning team's name (Springfield Thunderbirds or Iowa Wild). Includes explicit handling for postponement (market remains open) and full cancellation (50-50 split). Quote: 'If Springfield Thunderbirds win, the market will resolve to Springfield Thunderbirds. If Iowa Wild win, the market will resolve to Iowa Wild.'
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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