A college basketball game between Eastern Kentucky Colonels and Bellarmine Knights scheduled for February 21, 2026 at 7:00 PM ET. Multiple prediction markets track the moneyline winner, point spread outcomes, and total points scored across Polymarket and Kalshi platforms.
Kalshi moneyline market contains a logical contradiction: both Eastern Kentucky win and Bellarmine win are mapped to Yes resolution, making the market unresolvable. Polymarket markets are logically sound and mutually consistent.
Hero Tip:
Avoid trading Kalshi moneyline until the platform corrects the resolution logic. Polymarket offers clear, resolvable markets: use those for exposure. If forced to hold Kalshi, escalate to support immediately for clarification on whether Bellarmine win should resolve to No.
Critical Divergence Points:
Polymarket: Moneyline: resolves to winner name (Eastern Kentucky Colonels or Bellarmine Knights). Spread (Bellarmine -1.5): Bellarmine wins if margin >= 2 points, otherwise Eastern Kentucky. Spread (Eastern Kentucky -1.5): Eastern Kentucky wins if margin >= 2 points, otherwise Bellarmine. Totals (154.5, 155.5, 156.5, 157.5): resolve Over if combined score meets or exceeds threshold+1, Under otherwise. All markets: postponed games remain open; canceled games with no makeup resolve 50-50; overtime included in final score.
Kalshi: Moneyline: 'If Eastern Kentucky wins...resolves to Yes. If Bellarmine wins...resolves to Yes.' Both outcomes map to identical resolution (Yes), creating a logical impossibility where no outcome can resolve to No.
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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