Central Michigan Chippewas vs. Miami (OH) RedHawks (W)
Volume:
$14,924
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 Central Michigan Chippewas and Miami (OH) RedHawks scheduled for February 18, 2026 at 7:00 PM ET. Markets on Polymarket and Kalshi are tracking the outcome of this matchup, with resolution based on the final score including overtime.
Kalshi's resolution logic contains a fatal logical contradiction: both possible outcomes (Central Michigan win and Miami win) are mapped to the same resolution state (Yes), making the market unresolvable and unable to differentiate between the two teams.
Hero Tip:
Do not trade on Kalshi's version of this market. The resolution criteria is internally incoherent and will fail to settle correctly regardless of the game outcome. Polymarket's binary logic is sound and should be the exclusive trading venue for this event.
Critical Divergence Points:
Polymarket: Clear binary winner-take-all structure with mutually exclusive outcomes. Central Michigan win resolves to 'Central Michigan Chippewas', Miami win resolves to 'Miami (OH) RedHawks'. Handles edge cases (postponement keeps market open, cancellation resolves 50-50). Resolution based on final score including overtime.
Kalshi: Defective resolution logic: both Central Michigan win and Miami win are stated to resolve to Yes, creating a logical impossibility. The market cannot distinguish between the two teams and will be unresolvable.
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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