This event group covers a men's college basketball game between Army Black Knights and Lafayette Leopards scheduled for February 28, 2026 at 1:00 PM ET. Markets include moneyline (winner), point spread (Lafayette -3.5), and two over/under totals (143.5 and 144.5 points).
Kalshi moneyline market contains a logical contradiction where both possible game outcomes (Lafayette win and Army win) are specified to resolve to the same outcome (Yes), making the market fundamentally unresolvable and creating data integrity failure.
Hero Tip:
Avoid trading the Kalshi market until the resolution logic is corrected. The Polymarket suite (moneyline, spread, over/unders) all contain coherent, internally consistent resolution logic and can be safely settled based on final game score including overtime. Request clarification from Kalshi on whether the market should resolve Yes only on game completion, or if one outcome should resolve No.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name (Army Black Knights or Lafayette Leopards); Spread resolves to Lafayette Leopards if they win by 4+ points, otherwise Army Black Knights; Over/Under markets resolve based on combined score thresholds (144+ for 143.5 line, 145+ for 144.5 line). All use final score including overtime; 50-50 split if game canceled with no makeup.
Kalshi: Market states both 'If Lafayette wins...resolves to Yes' AND 'If Army wins...resolves to Yes' - creating logical impossibility where both mutually exclusive outcomes map to identical resolution. No No outcome is defined.
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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