This event group covers an NHL game between the Montreal Canadiens and San Jose Sharks scheduled for March 3, 2026 at 10:00 PM ET. Markets span moneyline (winner), multiple over/under total goals thresholds (4.5, 5.5, 6.5, 7.5), and a spread bet (-1.5 Canadiens). Resolution depends on final regulation and overtime scoring, with shootout goals counted as one additional goal for the winning team.
Kalshi moneyline market contains a logical contradiction: both teams cannot win simultaneously, yet both outcomes are coded to resolve 'Yes'. This makes the market fundamentally unresolvable and indicates a critical data integrity failure.
Hero Tip:
Do not trade the Kalshi moneyline. Polymarket's markets are well-structured with clear mutually exclusive outcomes (Canadiens vs. Sharks for moneyline; Over vs. Under for totals; Canadiens vs. Sharks for spread). Use Polymarket as the authoritative source for this event group.
Critical Divergence Points:
Polymarket: Five distinct markets: moneyline (Canadiens or Sharks), spread (-1.5 Canadiens), and four over/under totals (4.5, 5.5, 6.5, 7.5). Each market has mutually exclusive outcomes. Postponement keeps market open; cancellation without makeup resolves 50-50. Shootout adds one goal to winning team's score for resolution purposes.
Kalshi: Single moneyline market with contradictory logic: 'If SJ Sharks wins... resolves to Yes' AND 'If MTL Canadiens wins... resolves to Yes'. Both conditions cannot occur in a single game, making the market logically impossible to resolve. No edge-case handling specified.
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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