A men's college basketball game between Oral Roberts Golden Eagles and Kansas City Roos scheduled for February 28, 2026 at 8:00 PM ET. Markets cover the moneyline winner, point spread outcomes at multiple thresholds, and total points over/under at three different levels.
Kalshi's moneyline market contains a logical contradiction where both possible outcomes (Oral Roberts win and Kansas City win) are stated to resolve to Yes, making the market fundamentally unresolvable. Polymarket's moneyline uses standard binary logic with mutually exclusive outcomes.
Hero Tip:
Do not trade the Kalshi moneyline market. Use Polymarket's moneyline market for winner determination. All spread and total markets across both platforms are logically consistent and use identical resolution criteria: final score including overtime, with 50-50 split if game is canceled entirely with no makeup date.
Critical Divergence Points:
Kalshi: Moneyline market states both Oral Roberts win and Kansas City win resolve to Yes. This is a logical impossibility and suggests a template error. Quote: 'If Oral Roberts wins...then the market resolves to Yes. If Kansas City wins...then the market resolves to Yes.'
Polymarket: Moneyline market uses standard binary logic: Oral Roberts win resolves to 'Oral Roberts Golden Eagles', Kansas City win resolves to 'Kansas City Roos'. Mutually exclusive outcomes. Quote: 'If the Oral Roberts Golden Eagles win, the market will resolve to "Oral Roberts Golden Eagles". If the Kansas City Roos win, the market will resolve to "Kansas City Roos".'
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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