Southern Jaguars vs. Prairie View A&M Panthers (W)
Volume:
$17,339
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the women's college basketball game between Southern Jaguars and Prairie View A&M Panthers scheduled for February 14, 2026 at 3:00 PM ET. The markets track which team wins the game, with resolution based on the final score including overtime.
Kalshi market contains a logical contradiction: both possible outcomes (either team winning) are stated to resolve to Yes, making the market fundamentally unresolvable as written. Polymarket uses standard categorical resolution (team name outcome).
Hero Tip:
Do not trade on Kalshi until the market logic is corrected. The statement that both outcomes resolve to Yes indicates a critical data integrity failure. Polymarket's market is resolvable and should be treated as the authoritative source for this event.
Critical Divergence Points:
Kalshi: Declares both Prairie View A&M win and Southern win both resolve to Yes. This is logically impossible for a binary outcome. Quote: 'If Prairie View A&M wins...resolves to Yes. If Southern University wins...resolves to Yes.'
Polymarket: Resolves to specific team name: Southern Jaguars or Prairie View A&M Panthers. Includes postponement (market stays open) and cancellation (50-50 split) rules. Quote: 'If the Southern Jaguars win, the market will resolve to Southern Jaguars. If the Prairie View A&M Panthers win, the market will resolve to Prairie View A&M Panthers.'
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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