This event group covers a college basketball game between Boston University (Terriers) and Lehigh University (Mountain Hawks) scheduled for February 22, 2026 at 12:00 PM ET. Markets include moneyline (winner), point spread, and over/under totals at multiple thresholds.
Kalshi moneyline market contains a logical contradiction: both possible game outcomes (Boston University win and Lehigh win) are stated to resolve to Yes, making the market logically unresolvable and creating data integrity failure.
Hero Tip:
This is a critical platform error on Kalshi. The market cannot function as written because there is no outcome that resolves to No. Request immediate clarification from Kalshi support before trading. Polymarket markets are internally consistent and resolvable.
Critical Divergence Points:
Polymarket:
Five distinct markets with clear, mutually exclusive resolution paths. Moneyline resolves to winning team name. Three over/under markets at different thresholds (142.5, 144.5, 145.5) resolve Over if combined score meets or exceeds threshold plus one. Spread resolves to Boston Terriers if they win by 2+ points, otherwise Lehigh. All markets: postponement keeps open; cancellation without makeup = 50-50 split. Final score includes overtime.
Kalshi:
Single market states: 'If Boston University wins the game... resolves to Yes. If Lehigh wins the game... resolves to Yes.' Both possible outcomes map to the same resolution (Yes), leaving no outcome for No resolution. This is a logical contradiction that makes the market unresolvable as drafted.
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.