Southern Indiana Screaming Eagles vs. Little Rock Trojans (W)
Volume:
$1,289
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 Southern Indiana Screaming Eagles and Little Rock Trojans scheduled for February 28, 2026 at 2:00 PM ET. The markets resolve based on which team wins the game, with provisions for postponement, cancellation, and overtime.
Kalshi's resolution criteria contains a logical contradiction where both mutually exclusive outcomes (Southern Indiana win and Little Rock win) resolve to the same state (Yes), rendering the market fundamentally unresolvable and unable to differentiate between winning and losing positions.
Hero Tip:
Do not trade Kalshi's version. The market is logically broken—both teams winning resolves to Yes, eliminating the ability to have a losing position. Trade only on Polymarket, which uses proper binary logic (Southern Indiana vs. Little Rock). Report Kalshi's market to compliance immediately.
Critical Divergence Points:
Polymarket: Binary winner-take-all structure with clear mutually exclusive outcomes. Resolves to team name of winner. Handles postponement by keeping market open; handles cancellation with 50-50 split. Includes overtime in final score determination.
Kalshi: Defective Yes/No structure where both possible outcomes (Southern Indiana wins OR Little Rock wins) resolve to Yes, creating logical impossibility. No mechanism to distinguish between the two teams or establish a meaningful No resolution.
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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