North Dakota Fighting Hawks vs. North Dakota State Bison (W)
Volume:
$420,183
Markets
Outcome
Chance %
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Liquidity
Volume
24h
7d
Open Interest
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Description
This event group covers a women's college basketball game between the University of North Dakota Fighting Hawks and North Dakota State University Bison, scheduled for February 28, 2026 at 3:00 PM ET. The markets resolve based on which team wins the game, with specific provisions for postponements and cancellations.
Kalshi's resolution criteria contains a logical contradiction: both possible outcomes (North Dakota St. wins OR North Dakota wins) resolve to Yes, leaving no valid No resolution state. This makes the market unresolvable as specified.
Hero Tip:
Avoid trading the Kalshi contract until the platform corrects the resolution logic. The market as written cannot function. Use Polymarket as your authoritative source, which has clear binary resolution: Hawks win or Bison win, with explicit edge-case handling for postponements (market stays open) and cancellations (50-50 split).
Critical Divergence Points:
Polymarket: Binary winner-take-all structure with explicit edge cases. Resolves to 'North Dakota Fighting Hawks' if Hawks win, 'North Dakota State Bison' if Bison win. Postponements keep market open until completion; cancellations with no makeup resolve 50-50. Resolution based on final score including overtime.
Kalshi: Logically defective. States market resolves Yes if North Dakota St. wins AND also resolves Yes if North Dakota wins. No valid No resolution path exists, making the market unresolvable as written.
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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