TOTAL VOLUME:
$134b
24H VOL:
$103,397,351
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,410,176,180
399,592
Markets across
30,097
events
MATCHED EVENTS:
2,622
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 13, 10:04 AM EST
Kalshi
This group forecasts the outcome of a La Liga 2 soccer match between Real Sporting de Gijón and CD Eldense, scheduled for September 13, 2026. Markets exist for Sporting Gijón winning, CD Eldense winning, and the game ending in a draw.
This event is for the upcoming La Liga 2 game, scheduled for Sunday, September 13, 2026 between Real Sporting de Gijón and CD Eldense.
The event resolves based on the result of the Gijon vs Eldense professional La Liga 2 soccer match originally scheduled for September 13, 2026, after 90 minutes plus stoppage time, excluding extra time or penalties. If Gijon wins, the 'Gijon' market resolves to Yes. If Eldense wins, the 'Eldense' market resolves to Yes. If the match ends in a tie, the 'Tie' market resolves to Yes. If the game is cancelled or rescheduled to more than 48 hours beyond the original date, all markets will resolve to a fair price according to the platform's guidelines. The platform explicitly states it is not affiliated with the governing league, and all trademarks remain the property of their respective owners.
Prediction market odds, as reflected in this market, often differ from traditional sportsbook odds due to the wisdom of the crowd and the incentive structure. Sportsbooks set lines to balance action and ensure profit, while prediction markets allow participants to directly express their beliefs. This can lead to more accurate probabilities, especially as the event approaches and more information becomes available. The aggregated volume of $75,831 demonstrates active participation, potentially offering a more refined forecast than a single sportsbook's assessment of the Sporting Gijón vs Eldense match.
Prices on Polymarket and Kalshi for this market can diverge due to several factors. Polymarket and Kalshi can show different implied probabilities for the same outcome because of liquidity, fee structure, participant mix, and how each venue defines the contract. Each platform has its own user base, trading dynamics, and risk assessments, leading to differing opinions on the likelihood of each outcome. Furthermore, trading fees and liquidity can vary between Polymarket and Kalshi, influencing price discovery. Market participants may also interpret available information differently, or react to news at different speeds, contributing to price discrepancies. These variations are a natural part of a decentralized prediction market ecosystem.