A college basketball game between the New Hampshire Wildcats and Binghamton Bearcats scheduled for February 26, 2026 at 6:00 PM ET. Markets cover moneyline (winner), multiple point spreads (-1.5, -2.5, -3.5), and total points over/under (141.5, 142.5).
Kalshi market contains a logical contradiction: both possible game outcomes (Binghamton win and New Hampshire win) are mapped to Yes resolution, making the market fundamentally unresolvable and creating a data integrity failure.
Hero Tip:
Avoid trading the Kalshi market until the contradiction is corrected. Use Polymarket moneyline and spread markets as the authoritative settlement source. Request clarification from Kalshi on whether one outcome should resolve to No, or if this is a platform error.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name (New Hampshire Wildcats or Binghamton Bearcats). Spreads resolve based on margin: -1.5 requires 2+ point win, -2.5 requires 3+ point win, -3.5 requires 4+ point win. Totals resolve Over if combined score meets or exceeds threshold (142 for 141.5 line, 143 for 142.5 line). All markets postpone if game delayed, resolve 50-50 if canceled. Key quote: 'Result will be determined based on the final score including any overtime periods.'
Kalshi: Market statement: 'If Binghamton wins...resolves to Yes. If New Hampshire wins...resolves to Yes.' Both mutually exclusive outcomes map to identical resolution value, creating logical impossibility. No resolution path for No outcome exists. Key quote: Both conditional branches resolve 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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