Influence of popularity on the transfer fees of football players

Abstract

Search popularity, as reported by Google Trends, has previously been demonstrated to be useful when studying many time series. However, its use in cross-section studies is not straightforward because search popularity is not provided in absolute terms but as a normalized index that impedes comparisons. This paper proposes a novel methodology for calculating popularity indicators obtained from Google Trends to improve the prediction of football players' transfer fees. The database is formed by 1428 players who competed in LaLiga, Premier League, Bundesliga, Serie A, and Ligue 1 on the 2018-2019 season. Random forest algorithm and multiple linear regression are used to study the popularity indicators' importance and significativity, respectively. Results showed that the proposed popularity indicators provide significant information to predict players’ transfer fees, as models including such popularity indicators had lower prediction error than those without them. This study's developed method could be used not only for analysts specialized in sports data analysis but for researchers of other fields.