San Diego State Aztecs vs. Boise State Broncos (W)
Volume:
$726
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 San Diego State Aztecs and Boise State Broncos scheduled for February 25, 2026 at 8:30 PM ET. Markets across Polymarket and Kalshi are betting on the winner of this matchup.
Kalshi's resolution logic contains a fundamental contradiction: both San Diego State win and Boise State win are stated to resolve to Yes, making the market logically unresolvable and unable to distinguish between outcomes.
Hero Tip:
This is a critical data integrity failure on Kalshi. The market cannot function as written because both possible game outcomes map to the same resolution value. Do not trade on Kalshi until the platform corrects the resolution terms. Polymarket's binary structure is correct and tradeable.
Critical Divergence Points:
Polymarket: Clean binary winner-take-all logic. San Diego State Aztecs win resolves to San Diego State Aztecs; Boise State Broncos win resolves to Boise State Broncos. Handles postponement (market stays open) and cancellation without makeup (50-50 split). Key Quote: The result will be determined based on the final score including any overtime periods.
Kalshi: Logically malformed. Both conditional statements resolve to Yes: If San Diego St. wins resolves to Yes AND If Boise St. wins resolves to Yes. This creates an impossible scenario where the market cannot differentiate between the two teams winning.
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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