This event group covers an NHL game between the Dallas Stars and Vancouver Canucks scheduled for March 2, 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 for Stars). Resolution depends on final score including overtime and shootouts.
Kalshi moneyline market lacks explicit edge case handling for postponement, cancellation, and shootout scoring rules that are clearly defined in Polymarket markets. Scope difference between binary winner resolution and Yes/No moneyline structure creates potential settlement ambiguity.
Hero Tip:
Before settlement, confirm with Kalshi whether postponed games remain open or auto-resolve, and whether shootout scoring follows the one-goal addition convention. Polymarket's detailed rules provide a reference standard; use them to validate Kalshi's actual settlement behavior.
Critical Divergence Points:
Polymarket: Moneyline resolves to Stars or Canucks based on final score. Postponement keeps market open until completion. Cancellation with no makeup resolves 50-50. Shootout adds one goal to winner's score. Applies consistently across all five market types (moneyline, four O/U thresholds, spread).
Kalshi: Moneyline resolves Yes if either VAN or DAL wins the game scheduled for Mar 2, 2026. No explicit rules for postponement, cancellation, or shootout scoring methodology. Statement 'If VAN Canucks wins... then resolves to Yes' and 'If DAL Stars wins... then resolves to Yes' creates logical redundancy (both outcomes = Yes).
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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