A men's college basketball game between Merrimack College Warriors and Quinnipiac Bobcats scheduled for February 15, 2026 at 2:00 PM ET. Markets cover moneyline winner, point spread outcomes, and total points over/under thresholds.
Kalshi moneyline market contains a logical contradiction where both possible game outcomes (Quinnipiac win and Merrimack win) are specified to resolve to Yes, making the market fundamentally unresolvable. Polymarket moneyline and all spread/total markets across both platforms are logically sound.
Hero Tip:
Do not trade Kalshi moneyline until the platform corrects the specification. The contradiction makes it impossible to determine settlement. Focus trading activity on Polymarket moneyline and the spread/total markets, which have consistent, resolvable logic across both platforms.
Critical Divergence Points:
Kalshi:
Moneyline market states both Quinnipiac win and Merrimack win resolve to Yes. This is a logical impossibility. Key Quote: 'If Quinnipiac wins...then the market resolves to Yes' AND 'If Merrimack wins...then the market resolves to Yes'.
Polymarket:
Moneyline resolves to the winner's name (Merrimack College Warriors or Quinnipiac Bobcats), creating mutually exclusive outcomes. Spread and total markets use clear numeric thresholds (margin >= 2 or >= 3 points; combined score >= 142 or >= 143 points). Key Quote: 'If the Merrimack College Warriors win, the market will resolve to Merrimack College Warriors. If the Quinnipiac Bobcats win, the market will resolve to Quinnipiac Bobcats.'
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.