Northern Illinois Huskies vs. Western Michigan Broncos (W)
Volume:
$9,423
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 Northern Illinois Huskies and Western Michigan Broncos scheduled for February 21, 2026 at 1:00 PM ET. The markets resolve based on which team wins the game, with provisions for postponement or cancellation.
Kalshi's resolution statement contains a logical contradiction where both mutually exclusive outcomes are stated to resolve to Yes, making the market fundamentally unresolvable as written. Polymarket provides clear binary logic.
Hero Tip:
Do not trade Kalshi until clarification is obtained. The market cannot resolve as currently described. Polymarket's binary structure is sound and should be treated as the reference logic. Contact Kalshi support to confirm whether Western Michigan Yes means Northern Illinois No, or if this is a data entry error.
Critical Divergence Points:
Polymarket: Clear binary winner-take-all structure. Northern Illinois victory resolves to Northern Illinois Huskies, Western Michigan victory resolves to Western Michigan Broncos. Cancellation without makeup game resolves 50-50. Postponement keeps market open until completion.
Kalshi: Stated resolution: both Western Michigan win AND Northern Illinois win resolve to Yes. This is logically impossible for a mutually exclusive event. Likely intended as binary (one outcome Yes, other No) but as written creates unresolvable contradiction.
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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