Data-driven exploratory approach on player valuation in football transfer market

Abstract

Transfer markets in football have attracted the interest of researchers in economy and management. In this paper, we propose a high level analysis approach for classifying player valuation based on their performance during recent seasons. In particular, several data analysis techniques such as regression analysis, feature selection, and cluster analysis are presented for classifying players in term of performances and transfer fee. Specifically, by collecting and analyzing data from Wholescored, the largest detailed football statistics website, we have defined players into four groups, which include (1) Low performance and low transfer fee (LPLF), (2) Low performance and high transfer fee (LPHF), (3) high performance and high transfer fee (HPHF), and (4) high performance and low transfer fee (HPLF). The results in the implementation section show that, with the differences positions, there are different required skills that affect to the performance of players. We expect that this study can contribute to the management of Football Teams in terms of integrating these analyses into their management strategy.