A college basketball game between the Vermont Catamounts and NJIT Highlanders scheduled for February 21, 2026 at 7:00 PM ET. Multiple prediction markets track the moneyline winner, point spread outcomes at different thresholds, and total points scored.
Kalshi moneyline market contains a logical contradiction where both Vermont win and NJIT win resolve to Yes, making the market fundamentally unresolvable. This is a data integrity failure that prevents proper settlement.
Hero Tip:
Avoid the Kalshi moneyline market entirely until the platform corrects the resolution logic. Use Polymarket moneyline, spread, and totals markets which have clear, mutually exclusive outcomes. Confirm with Kalshi support that NJIT outcome should resolve to No.
Critical Divergence Points:
Polymarket: Moneyline: Vermont win resolves to Vermont Catamounts, NJIT win resolves to NJIT Highlanders (mutually exclusive). Spread -4.5: Vermont resolves if they win by 5+, otherwise NJIT. Spread -6.5: Vermont resolves if they win by 7+, otherwise NJIT. O/U 142.5: Over if combined 143+, Under if less than 143. O/U 141.5: Over if combined 142+, Under if less than 142. All markets: postponement keeps market open, cancellation without makeup resolves 50-50. Key Quote: If Vermont wins, resolves to Vermont Catamounts. If NJIT wins, resolves to NJIT Highlanders.
Kalshi: Moneyline: If Vermont wins, resolves to Yes. If NJIT wins, resolves to Yes. Both outcomes map to identical resolution (Yes), creating logical impossibility. Key Quote: If Vermont wins...resolves to Yes. If NJIT wins...resolves to Yes.
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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