TOTAL VOLUME:
$154.6b
24H VOL:
$333,432,169
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,971,286,090
419,492
Markets across
40,634
events
MATCHED EVENTS:
4,654
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 6, 6:50 PM EST
Polymarket
This market tracks the likelihood of both Albania and San Marino scoring in their upcoming UEFA Nations League match. Currently, the aggregated probability of “Albania vs. San Marino: Both Teams to Score” is 100.0%. This consensus view combines data from Predict and Polymarket, and will be resolved using official match statistics from UEFA.com. This group contains a comprehensive set of markets related to this match on October 6th, so keep an eye on team lineups announced closer to that date for potential insights.
More markets for the UEFA Nations League game, scheduled for October 6 at 2:45 PM ET.
More markets for the UEFA Nations League game, scheduled for October 6 at 2:45 PM ET.
Prediction market odds, like those found on Polymarket and Predict, often reflect the wisdom of the crowd, potentially offering a different perspective than traditional sportsbooks. Sportsbooks set odds based on their internal models and aim to profit by balancing risk. This market, however, allows anyone to participate and express their opinion, which can lead to odds that more accurately forecast the true probability of an event. While sportsbooks may incorporate public sentiment, prediction markets *are* public sentiment. Discrepancies can arise due to differing information access, biases, or simply variations in modeling approaches.
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 this market can diverge due to several factors. Each platform attracts a different user base with varying levels of expertise and access to information regarding the Albania vs. San Marino match. Trading volume and liquidity also play a role; a larger volume on one platform can lead to more efficient price discovery. Furthermore, differing platform fees and user interface designs can subtly influence trading behavior, creating discrepancies in the assessed probabilities. These factors contribute to the dynamic pricing observed across prediction markets.