Southeast Missouri State Redhawks vs. Little Rock Trojans (W)
Volume:
$2,562
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a women's college basketball game between Southeast Missouri State Redhawks and Little Rock Trojans scheduled for February 21, 2026 at 2:00 PM ET. The markets resolve based on which team wins the game, with provisions for postponements and cancellations.
Kalshi's market contains a logical contradiction where both possible game outcomes (Little Rock win and Southeast Missouri St. win) resolve to the same value (Yes), making the market unresolvable and unhedgeable. Polymarket's binary structure correctly distinguishes outcomes.
Hero Tip:
Avoid Kalshi entirely until the market is corrected. The Kalshi market cannot function as a prediction instrument. Trade only on Polymarket, which has a coherent binary resolution structure.
Critical Divergence Points:
Polymarket: Binary winner-take-all market with clear outcome mapping. Southeast Missouri State Redhawks win resolves to that team name; Little Rock Trojans win resolves to that team name. Postponements keep market open; full cancellations resolve 50-50. This structure is logically sound and resolvable.
Kalshi: Market contains a critical logical error. Both possible outcomes (Little Rock wins and Southeast Missouri St. wins) are mapped to Yes resolution. This makes it impossible to distinguish between the two teams and renders the market unresolvable. The market structure violates basic binary logic.
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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