KHL: Admiral Vladivostok vs. Neftekhimik Nizhnekamsk
Volume:
$811
Markets
Outcome
Chance %
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Liquidity
Volume
24h
7d
Open Interest
Ends in
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Description
This event group covers a professional KHL (Kontinental Hockey League) ice hockey match between Admiral Vladivostok and Neftekhimik Nizhnekamsk scheduled for March 7, 2026. The markets resolve based on which team wins the game, including overtime and shootout results.
Kalshi market contains a logical contradiction: both mutually exclusive outcomes (Admiral Vladivostok wins OR Neftekhimik Nizhnekamsk wins) are specified to resolve to Yes, making the market fundamentally unresolvable and creating a data integrity failure.
Hero Tip:
This is a critical market design flaw on Kalshi. The market cannot function as written because only one team can win, yet both winning scenarios are mapped to the same resolution (Yes). Polymarket's binary structure is sound and resolvable. Avoid Kalshi until corrected; trade only Polymarket.
Critical Divergence Points:
Polymarket: Binary categorical resolution: market resolves to the name of the winning team (Admiral Vladivostok or Neftekhimik Nizhnekamsk). Includes postponement hold and 50-50 cancellation protocol. Shootout adds one goal to winner's score for resolution purposes.
Kalshi: Logical contradiction: both Admiral Vladivostok winning and Neftekhimik Nizhnekamsk winning are specified to resolve to Yes. Since only one team can win, this creates an impossible resolution state where the market cannot distinguish between the two mutually exclusive outcomes.
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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