TOTAL VOLUME:
$154.6b
24H VOL:
$323,326,361
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,969,435,260
419,363
Markets across
40,411
events
MATCHED EVENTS:
4,612
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 31, 11:32 AM EST
Kalshi
This group forecasts the outcome of a Serie C soccer match between Giugliano Calcio 1928 and Casertana FC on October 31, 2026. Markets exist for Giugliano winning, Casertana winning, and the game ending in a draw. Resolution depends on the official match result, with fallback sources specified.
This event is for the upcoming Serie C game, scheduled for Saturday, October 31, 2026 between Giugliano Calcio 1928 and Casertana FC.
The event covers three possible outcomes of the Giugliano vs Casertana professional Serie C soccer match scheduled for October 31, 2026: a win for Giugliano, a win for Casertana, or a tie. Each market resolves based on the result after the full 90 minutes of regulation time plus any stoppage time, excluding extra time or penalty kicks. If the match ends in a tie after regulation, the 'Tie' market resolves to Yes. If the game is cancelled or rescheduled to more than 48 hours beyond the original date, all markets will resolve to a fair price following the platform's guidelines. The platform explicitly states it is not affiliated with the governing league, and all trademarks remain the property of their respective owners.
Prediction market odds, like those found for this market, often reflect the wisdom of the crowd, potentially offering a different perspective than traditional sportsbooks. Sportsbooks set lines based on their own models and aim to balance action on both sides, incorporating a profit margin. Prediction markets, however, allow anyone to trade, and prices are driven purely by supply and demand. This can lead to discrepancies, with prediction markets sometimes being more accurate, especially when there's significant public information available. Comparing the odds can reveal where the crowd disagrees with experts and identify potential value.
Polymarket and Kalshi can show different implied probabilities for the same outcome because of liquidity, fee structure, participant mix, and how each venue defines the contract. Prices on Polymarket and Kalshi can diverge due to several factors. These platforms have different user bases, trading volumes, and fee structures, all of which influence price discovery. Furthermore, each platform may attract different types of traders – some more sophisticated, others more casual – leading to varying interpretations of available information. The volume of trading on this market is currently $12,033, and differing volume between Polymarket and Kalshi can amplify price discrepancies. Finally, differing liquidity can also contribute to price separation.