A men's college basketball game between Stanford Cardinal and Notre Dame Fighting Irish scheduled for March 4, 2026 at 9:00 PM ET at Notre Dame. Markets cover moneyline (winner), point spread (-1.5), and multiple over/under total points variations across Kalshi and Polymarket platforms.
Kalshi moneyline contains a logical contradiction (both outcomes resolve to Yes), rendering it unresolvable. Polymarket offers consistent moneyline and spread logic but fragments total points markets across four distinct thresholds, creating settlement value divergence for identical game outcomes.
Hero Tip:
Do not trade Kalshi moneyline. For Polymarket, treat each O/U threshold (145.5, 146.5, 147.5, 148.5) as independent markets with different settlement values for the same final score. Example: a 147-147 combined score resolves Over on 145.5 and 146.5, but Under on 147.5 and 148.5. Spreads and moneyline are consistent across platforms where available.
Critical Divergence Points:
Kalshi: Moneyline market states both Stanford win and Notre Dame win resolve to Yes, creating a logical impossibility. No clear resolution path for a binary outcome. Spreads and totals not specified on Kalshi source data.
Polymarket: Moneyline resolves to winner name (Stanford Cardinal or Notre Dame Fighting Irish). Spreads resolve based on margin (Notre Dame -1.5 or Stanford -1.5). Four separate O/U markets with thresholds at 145.5, 146.5, 147.5, and 148.5 points, each with independent settlement logic. Quote: 'This market will resolve to Over if...combine to score [threshold] or more points.'
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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