Mount St. Mary's Mountaineers vs. Niagara Purple Eagles (W)
Volume:
$132,900
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a women's college basketball game between Mount St. Mary's Mountaineers and Niagara Purple Eagles scheduled for February 19, 2026 at 6:00 PM ET. Markets on Polymarket and Kalshi are pricing the outcome of this single game, with resolution based on the final score including overtime.
Kalshi market has a logical contradiction where both possible outcomes (Mount St. Mary's win and Niagara win) are specified to resolve to YES, making settlement impossible. Polymarket uses standard binary resolution.
Hero Tip:
This is a critical data integrity failure on Kalshi. The market cannot resolve correctly as written. Contact Kalshi support to confirm whether the second condition should resolve to NO. Avoid trading Kalshi until clarified. Polymarket appears structurally sound.
Critical Divergence Points:
Polymarket: Binary winner-take-all structure. Resolves to 'Mount St. Mary's Mountaineers' if Mount St. Mary's wins, or 'Niagara Purple Eagles' if Niagara wins. Includes standard edge cases: postponement keeps market open; cancellation with no makeup resolves 50-50. Resolution based on final score including overtime.
Kalshi: Logically contradictory YES/YES resolution. Both 'If Mount St. Mary's wins' and 'If Niagara wins' are specified to resolve to YES. This creates an unresolvable market—one team must lose, but the market structure assigns YES to both outcomes.
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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