A college basketball game between North Alabama Lions and Lipscomb Bisons scheduled for February 21, 2026 at 5:00 PM ET. Markets cover moneyline (winner), multiple point spreads, and over/under totals across Polymarket and Kalshi platforms.
Kalshi moneyline market contains a logical contradiction: both possible game outcomes (North Alabama win and Lipscomb win) are mapped to the same resolution value (Yes), making the market unresolvable and creating a data integrity failure.
Hero Tip:
Do not trade the Kalshi moneyline market in its current form. The market logic is broken — it will resolve Yes regardless of which team actually wins, defeating the purpose of a moneyline bet. Contact Kalshi support for clarification. Polymarket's moneyline and all derivative markets (spreads, totals) are logically sound and can be traded with confidence.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name: 'If North Alabama Lions win, resolves to North Alabama Lions. If Lipscomb Bisons win, resolves to Lipscomb Bisons.' Spread markets (-12.5, -13.5) resolve based on margin thresholds. Over/Under markets (147.5, 148.5, 149.5) resolve on combined total. Postponement: markets remain open. Cancellation with no makeup: 50-50 resolution.
Kalshi: Moneyline states: 'If North Alabama wins...resolves to Yes. If Lipscomb wins...resolves to Yes.' Both outcomes map to Yes, creating a tautology. No specification of No resolution condition or tie-breaking logic. This contradicts standard moneyline semantics where each team outcome should map to distinct resolution values.
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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