TOTAL VOLUME:
$54.4b
24H VOL:
$224,446,576
24H TRANSACTIONS:
531,611,392
OPEN INTEREST:
$1,198,377,813
501,416
Markets across
13,642
events
MATCHED EVENTS:
1,816
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Penn Quakers vs. Brown Bears
Volume:
$748,650
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A men's college basketball game between the University of Pennsylvania Quakers and Brown University Bears scheduled for March 6, 2026 at 7:00 PM ET. Markets cover moneyline (winner), point spreads at multiple thresholds, and total points over/under at multiple lines.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's moneyline market contains a logical contradiction where both Penn and Brown outcomes are specified to resolve to Yes, making the market fundamentally unresolvable and creating data integrity failure.Hero Tip:
Do not trade the Kalshi moneyline. It is broken. Use Polymarket's moneyline instead, which has proper mutually exclusive outcomes. All spread and total markets across both platforms are logically sound and consistent.Critical Divergence Points:
- Kalshi: Moneyline market states: If Penn wins, resolve Yes. If Brown wins, resolve Yes. This creates a tautology where both outcomes map to the same resolution, making the market logically impossible to settle correctly.
- Polymarket: Moneyline market states: If Penn wins, resolve Penn Quakers. If Brown wins, resolve Brown Bears. Mutually exclusive outcomes with proper binary structure.
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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