Most studies exploring the global market of association football (soccer) transfers have relied on the hedonic pricing approach, where the characteristics of the player are the key features. While this has largely been useful, there are some potential challenges since it does not represent the global market and is susceptible to selection bias. In this exploratory study, we aimed to address these two key issues by proposing a different modelling approach that relies on an original solution (i.e., video gaming data). First, we built a random global sample by starting from an existing player universe, and appended video gaming data, transfer and salary data. Second, we developed new measures of transfer prices (without selectivity). This design is superior as it addresses the challenges of non-representativeness and selection bias. Indeed, the results are homoscedastic across different segments (regions and player positions) and provide results that are robust and representative of the global market than previous studies.
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