This event group covers a Czech Extraliga ice hockey match between HC Verva Litvinov and HC Dynamo Pardubice scheduled for February 27, 2026. Markets on Polymarket and Kalshi both aim to resolve based on the final outcome of this single game, with Litvinov or Pardubice as the two possible winners.
Kalshi's resolution logic contains a logical contradiction: both Litvinov victory and Pardubice victory are stated to resolve to Yes, making the market fundamentally unresolvable. Polymarket uses a standard binary outcome structure.
Hero Tip:
This is a critical data integrity failure on Kalshi. The market cannot function as written because only one team can win, but both outcomes map to the same resolution. Contact Kalshi support immediately to confirm whether the second condition should resolve to No, or if this is a platform error requiring market cancellation.
Critical Divergence Points:
Polymarket:
Binary outcome structure: Litvinov win resolves to Litvinov, Pardubice win resolves to Pardubice. Handles postponements by keeping market open and cancellations by 50-50 split. Key Quote: If Litvinov win, the market will resolve to Litvinov. If Dynamo Pardubice win, the market will resolve to Dynamo Pardubice.
Kalshi:
Contradictory logic: states both If HC Verva Litvinov wins then resolves to Yes AND If HC Pardubice wins then resolves to Yes. This creates an impossible condition where both mutually exclusive outcomes cannot both be true. Key Quote: If HC Verva Litvinov wins... then the market resolves to Yes. If HC Pardubice wins... then the market resolves to Yes.
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.