Beyond the Pitch: Determinants of Female Footballers' Market Values on Crowdsourced Platforms

ABSTRACT Women's football has undergone remarkable growth in recent years. However, it continues to lag behind the men's game in terms of economic development and media visibility. Academic research has largely overlooked the women's market, in part due to historical data limitations. This study addresses a key gap by proposing a multilevel regression model to explain the market value of female footballers, using data directly obtained from the crowd‐based platform Soccerdonna . The dataset comprises 2153 players across the top five leagues (Germany, France, Spain, England, and the United States) during the 2021/22 and 2022/23 seasons and includes objective variables such as physical attributes, on‐field performance, and social media popularity. The model reveals systematic differences in valuation dynamics by player position and competitive segment, reflecting structural disparities within the women's game. Moreover, crowd‐estimated values from Soccerdonna are found to be reasonably aligned with actual transfer fees at the aggregate level. These findings provide a robust, data‐driven framework to assess player value in women's football, contributing to the empirical validation of signaling theory, superstar economics and efficient markets, with practical implications for clubs, agents, federations, and other market stakeholders.