TOTAL VOLUME:
$54.4b
24H VOL:
$234,875,025
OPEN INTEREST:
$1,205,330,693
501,301
Markets across
13,585
events
MATCHED EVENTS:
1,825
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
UCLA Bruins vs. USC Trojans (W)
Volume:
$527,901
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 UCLA Bruins and USC Trojans scheduled for March 1, 2026 at 5:00 PM ET. 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 states the market resolves Yes if either team wins, which is logically impossible for a binary outcome and makes the market unresolvable. Polymarket provides clear winner-based resolution.Hero Tip:
Avoid Kalshi until the platform clarifies whether the market should resolve Yes/No based on a specific team winning, or if this is a data entry error. Polymarket's market is tradeable and should be your primary reference.Critical Divergence Points:
- Polymarket: Resolves to winner name: UCLA Bruins if UCLA wins, USC Trojans if USC wins. Cancellation without makeup resolves 50-50. Key quote: 'The result will be determined based on the final score including any overtime periods.'
- Kalshi: Resolves to Yes if USC wins OR if UCLA wins. This is a logical contradiction for a binary market. Key quote: 'If USC wins...then the market resolves to Yes. If UCLA wins...then the market 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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