A men's college basketball game between Penn State Nittany Lions and Nebraska Cornhuskers scheduled for February 21, 2026 at 2:00 PM ET. Markets cover the moneyline winner, multiple point spread variations, and over/under total points.
Kalshi moneyline market contains contradictory resolution logic where both Penn State and Nebraska victories resolve to Yes, making the market logically unresolvable. Polymarket and derivative markets (spreads, totals) maintain consistent winner-based or threshold-based resolution.
Hero Tip:
Avoid trading the Kalshi moneyline until the platform clarifies whether this is a game-occurrence market or a drafting error. All Polymarket markets (moneyline, spreads, totals) and the spread/total markets across both platforms are internally consistent and resolvable based on final game score including overtime.
Critical Divergence Points:
Kalshi: Moneyline market resolves to Yes for both outcomes - creates logical contradiction. Quote: 'If Penn St. wins... then the market resolves to Yes. If Nebraska wins... then the market resolves to Yes.' This violates binary market structure.
Polymarket: Moneyline resolves to winner name with binary outcomes. Spreads resolve based on margin thresholds (17.5, 18.5, 19.5 point Nebraska wins). Totals resolve Over/Under at 147.5 and 148.5 combined points. All follow standard resolution logic. Quote: 'If the Penn State Nittany Lions win, the market will resolve to Penn State Nittany Lions. If the Nebraska Cornhuskers win, the market will resolve to Nebraska Cornhuskers.'
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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