TOTAL VOLUME:
$134.2b
24H VOL:
$126,590,312
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,439,516,703
404,175
Markets across
30,277
events
MATCHED EVENTS:
2,685
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 11, 5:11 PM EST
Polymarket
This group aggregates prediction markets on the total number of corners taken in a Ligue 1 match between Stade Rennais FC 1901 and Olympique de Marseille. Markets range from over/under bets on the total corners to specific team corner counts and first-half/second-half corner totals.
Total corners markets for the Ligue 1 game between Stade Rennais FC 1901 and Olympique de Marseille, scheduled for September 11, 2026 at 2:45 PM ET.
Total corners markets for the Ligue 1 game between Stade Rennais FC 1901 and Olympique de Marseille, scheduled for September 11, 2026 at 2:45 PM ET.
Prediction market odds, such as those found on Polymarket and Predict, often reflect the collective wisdom of a diverse group of traders, potentially offering a different perspective than traditional sportsbooks. Sportsbooks typically set odds based on statistical models and expert analysis, while this market incorporates a broader range of information and opinions. This can lead to discrepancies, as prediction markets may be quicker to adjust to new information or account for factors not fully captured by sportsbook algorithms. However, both sources aim to accurately predict the probability of an event, and comparing them can provide a more comprehensive understanding of the potential outcomes for 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. Prices for the Rennes vs. Marseille corners market may vary between Polymarket and Predict due to several factors. Each platform has its own user base, trading dynamics, and fee structures, which can influence price discovery. Differences in liquidity, with one platform potentially having more active traders, can also lead to price discrepancies. Furthermore, the specific types of markets offered and the risk preferences of traders on each platform can contribute to divergent pricing. It’s common to see these variations even when tracking the same underlying event, reflecting the independent nature of each prediction market.