TOTAL VOLUME:
$54.4b
24H VOL:
$218,201,443
24H TRANSACTIONS:
527,797,601
OPEN INTEREST:
$1,191,873,651
500,842
Markets across
13,663
events
MATCHED EVENTS:
1,809
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Loyola Maryland Greyhounds vs. Holy Cross Crusaders (W)
Volume:
$13,776
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the women's college basketball matchup between Loyola Maryland Greyhounds and Holy Cross Crusaders scheduled for February 14, 2026 at 2:00 PM ET at Holy Cross. The market resolves based on which team wins the game, with provisions for postponement or cancellation.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution logic is internally contradictory, resolving both possible outcomes (Holy Cross win and Loyola Maryland win) to Yes, making the market logically unresolvable. Polymarket uses standard winner-take-all resolution to team names.Hero Tip:
Do not trade Kalshi until the resolution logic is corrected. The market cannot function as written. Polymarket is the only resolvable venue for this event.Critical Divergence Points:
- Kalshi: Both outcomes resolve to Yes. If Holy Cross wins: Yes. If Loyola Maryland wins: Yes. This is a data integrity failure that makes the market unresolvable.
- Polymarket: Standard winner-take-all format. Loyola Maryland win resolves to 'Loyola Maryland Greyhounds', Holy Cross win resolves to 'Holy Cross Crusaders'. Includes postponement (market stays open) and cancellation (50-50 split) provisions.
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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