Purdue Fort Wayne Mastodons vs. Milwaukee Panthers (W)
Volume:
$6,458
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
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Description
This event group covers a women's college basketball game between Purdue Fort Wayne Mastodons and Milwaukee Panthers scheduled for February 25, 2026 at 6:00 PM ET. Markets across Polymarket and Kalshi are tracking the binary outcome of this matchup, with resolution based on the final score including overtime.
Kalshi market has a logical contradiction where both possible game outcomes (Milwaukee win and Purdue Fort Wayne win) resolve to Yes, making the market fundamentally unresolvable and creating a data integrity failure.
Hero Tip:
Avoid trading the Kalshi version of this market until the resolution logic is corrected. The market cannot distinguish between the two outcomes. Polymarket's binary structure (Purdue Fort Wayne vs Milwaukee) is the only resolvable version.
Critical Divergence Points:
Polymarket: Binary winner-take-all structure. Resolves to Purdue Fort Wayne Mastodons if they win, Milwaukee Panthers if they win. Includes postponement (market stays open) and cancellation (50-50 split) contingencies. Key quote: If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Kalshi: Contradictory dual-YES mapping. Both Milwaukee win and Purdue Fort Wayne win are stated to resolve to Yes. This creates logical impossibility—the market cannot distinguish between outcomes. Key quote: If Milwaukee wins...resolves to Yes. If Purdue Fort Wayne wins...resolves to Yes.
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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