Association football is a popular sport, but it is also a big business. From a managerial perspective, the
most important decisions that team managers make concern player transfers, so issues related to player
valuation, especially the determination of transfer fees and market values, are of major concern. Market
values can be understood as estimates of transfer fees—that is, prices that could be paid for a player
on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular
approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent,
crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach
to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market
values using multilevel regression analysis. The regression results suggest that data-driven estimates of
market value can overcome several of the crowd’s practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics
facilitates precise, objective, and reliable estimates of market value that can be updated at any time