South Dakota State Jackrabbits vs. Kansas City Roos
Volume:
$242,238
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A men's college basketball game between South Dakota State Jackrabbits and Kansas City Roos scheduled for February 26, 2026 at 8:00 PM ET. Markets cover moneyline (winner), point spread (-11.5 and -12.5), and over/under totals (148.5, 149.5, and 151.5 points).
Kalshi's moneyline market contains a logical contradiction: both South Dakota St. winning and Kansas City winning are stated to resolve to Yes, making the market fundamentally unresolvable. Polymarket's moneyline, spread, and total markets use standard, consistent logic.
Hero Tip:
Do not trade Kalshi's moneyline—it is logically broken. Use Polymarket as the authoritative source for moneyline winner determination. Spread and total markets on both platforms are consistent and tradeable, with unified cancellation rules (50-50 split if game is canceled with no makeup).
Critical Divergence Points:
Kalshi: Moneyline market states: 'If South Dakota St. wins...resolves to Yes' AND 'If Kansas City wins...resolves to Yes.' This is a logical contradiction—both outcomes cannot both resolve to Yes. No resolution path exists for No outcome.
Polymarket: Moneyline resolves to the name of the winning team: 'South Dakota State Jackrabbits' or 'Kansas City Roos'. Spread markets (-11.5, -12.5) resolve based on margin thresholds. Total markets (148.5, 149.5, 151.5) resolve based on combined score. All include 50-50 cancellation clause.
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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