TOTAL VOLUME:
$54.6b
24H VOL:
$283,259,638
OPEN INTEREST:
$1,235,549,422
505,521
Markets across
13,661
events
MATCHED EVENTS:
1,892
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
SNHL: Zug vs. Fribourg-Gotteron
Volume:
$7,264
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a Swiss National League (SNHL) ice hockey match between EV Zug and Fribourg-Gottéron scheduled for February 24, 2026. Both prediction markets resolve based on the final winner of the game, including overtime and shootout outcomes.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi market contains a logical contradiction: both possible game outcomes (Zug win and Fribourg-Gottéron win) are stated to resolve to Yes, making the market fundamentally unresolvable.Hero Tip:
Avoid trading the Kalshi market until the platform clarifies the resolution logic. The market as written cannot be settled. Polymarket is the reliable source for this event.Critical Divergence Points:
- Polymarket: Binary outcome market with clear categorical resolution: Zug win resolves to Zug, Fribourg-Gotteron win resolves to Fribourg-Gotteron. Postponement keeps market open; cancellation with no makeup resolves 50-50. Shootout adds one goal to winner's score for resolution purposes.
- Kalshi: Stated as Yes/No binary on Zug victory, but resolution text contradicts itself: both Zug win AND Fribourg-Gottéron win are said to resolve to Yes, creating logical impossibility. Market cannot be settled under current wording.
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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