TOTAL VOLUME:
$154.8b
24H VOL:
$277,138,640
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,905,404,943
412,077
Markets across
39,838
events
MATCHED EVENTS:
4,526
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 15, 7:31 PM EST
Polymarket
This event group concerns the halftime result of a soccer match between RC Celta de Vigo and Juventus FC. Markets exist to predict whether Celta Vigo, Juventus FC, or a draw will be the leading score at halftime.
In the upcoming UEFA Europa League game between RC Celta de Vigo and Juventus FC, scheduled for October 15, 2026 at 3:00 PM ET: This event contains halftime result markets for home, draw, and away outcomes within the first 45 minutes of regular play plus stoppage time.
In the upcoming UEFA Europa League game between RC Celta de Vigo and Juventus FC, scheduled for October 15, 2026 at 3:00 PM ET: This event contains halftime result markets for home, draw, and away outcomes within the first 45 minutes of regular play plus stoppage time.
Prediction market odds, like those found on Polymarket and Predict for this market, often reflect the collective wisdom of a diverse group of traders, potentially offering a different perspective than traditional sportsbooks. Sportsbooks set odds based on their own models and aim to profit from imbalances in betting, while prediction markets incentivize accurate predictions through financial rewards. This can lead to prediction market prices being more accurate, especially when there's significant information asymmetry or subjective interpretation involved. However, sportsbooks generally offer higher liquidity and wider market coverage than prediction markets currently do.
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 for the Celta vs Juventus halftime market can differ due to several factors. Each platform attracts a different user base with potentially varying levels of expertise and information. Trading volume also plays a role; lower volume on one platform can lead to greater price volatility and divergence. Furthermore, the specific mechanisms for trading and order execution on each platform can influence price discovery. Differences in user interface, fees, or platform-specific rules can also contribute to these discrepancies, even when both platforms are responding to the same underlying event.