This event group covers a men's college basketball game between Alabama State Hornets and Alabama A&M Bulldogs scheduled for February 28, 2026 at 5:00 PM ET. Markets include moneyline (winner), multiple point spreads, and over/under total points across Polymarket and Kalshi platforms.
Kalshi moneyline market contains a logical contradiction where both possible game outcomes (Alabama St. win and Alabama A&M win) are mapped to the same resolution value (Yes), making the market unresolvable. This is a data integrity failure on the Kalshi platform.
Hero Tip:
Avoid trading the Kalshi moneyline until Kalshi corrects the market description. The contradiction makes it impossible to determine which outcome should resolve to Yes. Polymarket markets are internally consistent and safe to trade. Request clarification from Kalshi support on whether this should be a binary Yes/No or if the description contains a copy-paste error.
Critical Divergence Points:
Polymarket: Moneyline resolves to Alabama State Hornets if they win, or Alabama A&M Bulldogs if they win, based on final score including overtime. Spreads resolve based on margin of victory. Over/unders resolve based on combined team points. Cancellation without makeup resolves 50-50. All markets logically consistent and resolvable.
Kalshi: Moneyline market states: If Alabama St. wins, resolve to Yes. If Alabama A&M wins, resolve to Yes. Both outcomes map to identical resolution, creating logical impossibility. Key Quote: Both conditional branches lead to same outcome, making market unresolvable.
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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