Rutgers Scarlet Knights vs. Illinois Fighting Illini (W)
Volume:
$230,871
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 Rutgers Scarlet Knights and Illinois Fighting Illini scheduled for February 17, 2026 at 7:00 PM ET. Markets across Polymarket and Kalshi are tracking the binary outcome of which team wins the matchup.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Rutgers win or Illinois win) are mapped to the same resolution state (Yes), making the market fundamentally unresolvable and creating a data integrity failure.
Hero Tip:
Avoid trading Kalshi until corrected. The market as written cannot distinguish between outcomes. Polymarket's binary structure is the only resolvable version of this event. Wait for Kalshi to clarify whether Illinois win should resolve to No, or treat this as a platform error.
Critical Divergence Points:
Polymarket: Clean binary winner-take-all structure with explicit handling of edge cases. Rutgers win resolves to Rutgers Scarlet Knights, Illinois win resolves to Illinois Fighting Illini. Postponement keeps market open; cancellation without makeup resolves 50-50. Resolution based on final score including overtime.
Kalshi: Defective Yes/Yes tautology. Both Rutgers win and Illinois win are mapped to Yes resolution, creating logical impossibility. No explicit handling of postponement or cancellation. Market cannot distinguish between outcomes.
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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