A college basketball game between Radford Highlanders and UNC Asheville Bulldogs scheduled for February 21, 2026 at 4:30 PM ET. Markets cover moneyline (winner), multiple spread variations, and total points over/under thresholds.
Kalshi moneyline contains a logical contradiction where both possible game outcomes (Radford win and UNC Asheville win) are mapped to the same resolution value (Yes), making the market unresolvable. Polymarket markets are logically sound and consistent.
Hero Tip:
Do not trade the Kalshi moneyline in its current form. The market structure is broken and cannot differentiate between the two teams. Trade Polymarket moneyline, spreads, and totals instead, which have clear, mutually exclusive resolution paths.
Critical Divergence Points:
Polymarket: Moneyline resolves to team name (Radford Highlanders or UNC Asheville Bulldogs) based on final score. Spreads resolve based on margin thresholds: -1.5 requires UNC Asheville win by 2+, -2.5 requires UNC Asheville win by 3+. Totals resolve based on combined points: 148.5 threshold at 149+, 149.5 threshold at 150+. All markets postpone if game delayed; resolve 50-50 if canceled with no makeup. Key quote: 'The result will be determined based on the final score including any overtime periods.'
Kalshi: Moneyline states both outcomes resolve to Yes: 'If Radford wins...resolves to Yes' and 'If UNC Asheville wins...resolves to Yes'. This creates a logical impossibility where the market cannot differentiate between the two teams. Key quote: Both conditional statements map to identical resolution value.
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.
Follow the signals, not the noise
Get insights on market conviction, notable shifts, and what the data is quietly signaling.