A men's college basketball game between Lindenwood Lions and Tennessee State Tigers scheduled for February 19, 2026 at 8:30 PM ET. Markets track the moneyline winner, point spread outcomes, and combined point totals across multiple thresholds.
Kalshi moneyline market contains a logical contradiction where both possible game outcomes (either team winning) are mapped to the same resolution (Yes), making the market fundamentally unresolvable and unable to differentiate winners from losers.
Hero Tip:
Do not trade the Kalshi moneyline market. The logic guarantees Yes regardless of final score, violating basic binary market design. Polymarket's moneyline, spreads, and totals are logically sound and should be treated as the authoritative markets for this event. Request Kalshi clarification or treat as void pending correction.
Critical Divergence Points:
Kalshi: Moneyline market resolves to Yes if Tennessee St. wins AND Yes if Lindenwood wins—a tautology that eliminates outcome differentiation. Quote: 'If Tennessee St. wins...then the market resolves to Yes. If Lindenwood wins...then the market resolves to Yes.'
Polymarket: Moneyline resolves to 'Lindenwood Lions' if Lindenwood wins, or 'Tennessee State Tigers' if Tennessee State wins. Spread and total markets follow standard binary logic with clear thresholds (4+ points, 5+ points, 163+, 162+, 160+ combined points). Quote: 'If the Lindenwood Lions win, the market will resolve to Lindenwood Lions. If the Tennessee State Tigers win, the market will resolve to Tennessee State Tigers.'
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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