TOTAL VOLUME:
$54.3b
24H VOL:
$158,613,015
OPEN INTEREST:
$1,206,641,323
499,459
Markets across
13,676
events
MATCHED EVENTS:
1,789
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
CEHL: Liberec vs. Mountfield HK
Volume:
$4,621
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a Czech Extraliga professional ice hockey match between Liberec (Bili Tygri Liberec) and Mountfield HK scheduled for March 4, 2026. Markets on Polymarket and Kalshi both predict the winner of this single game, with identical game parameters and timing.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution criteria logically contradicts binary market structure by assigning both Liberec win and Mountfield HK win to the same resolution outcome (Yes), making the market unresolvable.Hero Tip:
This is a data integrity failure on Kalshi. The second resolution condition must resolve to No, not Yes. Request immediate clarification from Kalshi before trading. Polymarket's logic is sound and unambiguous.Critical Divergence Points:
- Polymarket: Clear binary outcome: Liberec win resolves to Liberec, Mountfield HK win resolves to Mountfield HK. Overtime and shootouts included in final score. Postponement keeps market open; cancellation without makeup resolves 50-50.
- Kalshi: Logical contradiction: States If Bili Tygri Liberec wins resolves to Yes AND If Mountfield HK wins resolves to Yes. Both mutually exclusive outcomes map to identical resolution, violating binary market structure.
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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