TOTAL VOLUME:
$154.3b
24H VOL:
$228,458,490
24H TRANSACTIONS:
2,984,028,936
OPEN INTEREST:
$1,879,276,037
410,833
Markets across
40,345
events
MATCHED EVENTS:
4,363
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 20, 5:58 PM EST
Polymarket
This prediction group covers various spread and over/under markets for the FC Porto vs. PSV Champions League match. Markets range from point spreads on either team winning by a certain margin to total goals scored, including options for both the full game and just the first or second half.
More markets for the UEFA Champions League game, scheduled for October 20 at 3:00 PM ET.
More markets for the UEFA Champions League game, scheduled for October 20 at 3:00 PM ET.
Prediction market odds, like those found on Polymarket and Predict for the FC Porto vs. PSV match, often reflect a different kind of information than traditional sportsbook odds. Sportsbooks set lines to balance action and incorporate a profit margin, while prediction markets are driven by traders who are incentivized to accurately forecast the outcome. This can lead to discrepancies, as prediction markets may incorporate unique insights or react more quickly to new information. However, both offer valuable perspectives on the probabilities of different results, and comparing them can provide a more well-rounded assessment of this market.
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. Price discrepancies between Polymarket and Predict on the FC Porto vs. PSV match can arise from several factors. Each platform attracts a different user base with varying levels of expertise and information, which influences trading behavior. Additionally, the specific market structures and available liquidity on each platform can impact pricing. For example, Polymarket currently favors Over 6.5 goals at 99 percent chance, while Predict leans toward Spread: PSV (-3.5). These differences highlight how distinct trading dynamics can shape expectations for the same event.