TOTAL VOLUME:
$54.3b
24H VOL:
$198,896,558
OPEN INTEREST:
$1,191,591,877
500,572
Markets across
13,691
events
MATCHED EVENTS:
1,797
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Dayton Flyers vs. Davidson Wildcats (W)
Volume:
$15,226
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 Dayton Flyers and Davidson Wildcats scheduled for February 21, 2026 at 1:00 PM ET. The markets resolve based on which team wins the game, with provisions for postponement, cancellation, and overtime.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution logic contains a logical contradiction: both Dayton win and Davidson win are stated to resolve to Yes, making the market unresolvable as a binary contract. Polymarket provides clear, mutually exclusive outcomes.Hero Tip:
Avoid trading Kalshi until the platform clarifies whether the second outcome should resolve to No or if this is a guaranteed-Yes market. Polymarket is the only actionable market in this group.Critical Divergence Points:
- Polymarket: Binary winner-take-all resolution. Dayton win resolves to Dayton Flyers; Davidson win resolves to Davidson Wildcats; cancellation with no makeup resolves 50-50; postponement keeps market open. Final score including overtime determines outcome.
- Kalshi: Both Dayton win and Davidson win are stated to resolve to Yes, creating a logical impossibility. The market cannot distinguish between the two 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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