This event group covers a professional KHL (Kontinental Hockey League) ice hockey match between Torpedo Nizhny Novgorod and Traktor Chelyabinsk scheduled for March 4, 2026. Markets across Polymarket and Kalshi are betting on the winner of this single game, with resolution based on final score including overtime and shootout outcomes.
Kalshi market contains a logical contradiction: both possible outcomes (Torpedo win and Traktor win) are stated to resolve to Yes, making the market fundamentally unresolvable. This is a data integrity failure, not a mere threshold or timing difference.
Hero Tip:
Treat Polymarket as the authoritative reference for this event. Kalshi's market structure appears to be a documentation error (likely copy-paste of resolution conditions). Contact Kalshi support immediately for clarification before placing trades on their platform. The underlying event itself is clear and resolvable; the divergence is purely in Kalshi's market mechanics.
Critical Divergence Points:
Polymarket: Clean binary outcome: market resolves to either 'Torpedo' or 'Traktor' based on match winner. Postponement keeps market open; cancellation with no makeup resolves 50-50. Final score including overtime and shootouts determines winner (shootout adds one goal to winner's score for resolution purposes).
Kalshi: Contradictory dual-Yes structure: states 'If Torpedo wins, resolves to Yes' AND 'If Traktor wins, resolves to Yes' - both outcomes cannot resolve to Yes in a binary market. This is logically impossible and suggests a template error in market creation.
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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