TOTAL VOLUME:
$154.4b
24H VOL:
$235,918,089
24H TRANSACTIONS:
2,984,028,936
OPEN INTEREST:
$1,884,820,873
413,109
Markets across
40,428
events
MATCHED EVENTS:
4,364
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 28, 3:21 PM EST
Kalshi
This market tracks the Over/Under 8.5 goals for the Serie A soccer match between Parma Calcio 1913 and Udinese Calcio, aggregating data from Predict, Kalshi, and Polymarket. Currently, the consensus probability of Parma Calcio 1913 vs. Udinese Calcio: O/U 8.5 is 100.0%. Resolution will be based on data from https://www.legaseriea.it/. Keep an eye on the match itself, scheduled for resolution on October 28, 2026, as the final score will determine market settlement.
More markets for the Serie A game, scheduled for October 28 at 1:30 PM ET.
The event evaluates multiple thresholds for total goals scored in the Parma Calcio vs Udinese Serie A match scheduled for October 28, 2026. Each market resolves to 'Yes' if the combined goals exceed a specified half-goal increment (0.5, 1.5, 2.5, 3.5, 4.5, or 5.5). All markets consider only goals scored within the 90 minutes plus stoppage time, excluding extra time or penalty shootouts. The governing rules consistently apply across all thresholds, with Kalshi explicitly stating no official affiliation with the league or ownership of related trademarks and branding.
More markets for the Serie A game, scheduled for October 28 at 1:30 PM ET.
Polymarket and Predict 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 Predict can diverge due to several factors. Each platform has its own user base, trading dynamics, and fee structures, all of which influence price discovery. Furthermore, the specific markets offered on each platform may not be identical, leading to different levels of focus and trading activity. For example, Polymarket might have a more liquid market on a specific outcome within the Parma vs Udinese match, while Predict might prioritize a different aspect. These differences in participation and market design contribute to the observed price discrepancies.