Campbell Fighting Camels vs. Stony Brook Seawolves
Volume:
$617,417
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A college basketball game between Campbell Fighting Camels and Stony Brook Seawolves scheduled for March 7, 2026 at 12:00 PM ET. Markets cover moneyline (winner), spread (-2.5 Campbell), and total points (O/U 146.5 and 148.5).
Kalshi moneyline market contains a logical contradiction: both Campbell win and Stony Brook win resolve to Yes, making the market unresolvable and creating arbitrage risk across platforms.
Hero Tip:
Do not trade Kalshi until the contradiction is resolved. Use Polymarket as your authoritative source. Request urgent clarification from Kalshi on whether the Yes outcome should be conditional on a specific winner or game completion.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name (Campbell Fighting Camels or Stony Brook Seawolves). Spread resolves Campbell if they win by 3+ points, otherwise Stony Brook. Totals (146.5 and 148.5) resolve Over/Under based on combined points. All markets resolve 50-50 if game canceled with no makeup. Source: NCAA.com. Final score includes overtime.
Kalshi: Moneyline states: If Stony Brook wins, resolves Yes. If Campbell wins, resolves Yes. LOGICAL CONTRADICTION: Both outcomes map to the same resolution (Yes), making it impossible to distinguish winner and rendering the market unresolvable as written.
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.
Follow the signals, not the noise
Get insights on market conviction, notable shifts, and what the data is quietly signaling.