Title: Model comparison of regression, neural networks, and XGBoost as applied to the English Premier League transfer market

Authors: Yuchen Wang; Hakan Tarakci; Victor Prybutok

Addresses: School of Marketing and Management, College of Business and Public Management, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA ' Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, 1155 Union Circle #311160, Denton, Texas 76203, USA ' Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, 1155 Union Circle #311160, Denton, Texas 76203, USA

Abstract: The English Premier League (EPL) is the highest level in the UK professional soccer system and one of the largest and most competitive professional soccer leagues in the world. This research examines factors influencing transfer fees in the most popular transfer market for EPL using data spanning ten years. By building a Nash equilibrium model, a dynamic modelling system is developed to measure the transfer fee with the ordinal least square regression, eXtreme Gradient Boosting (XGBoost), and neural network (NN) models. The study recognises the effect of bargaining power and provides optimised strategies for clubs and players. Moreover, clubs will be able to utilise NN as the most advantageous method to determine the transfer fees. The research can serve as a comprehensive decision support system for assessing the expenditure needs corresponding to the players scouted in the transfer market.

Keywords: English Premier League; EPL; transfer market; game theory; Nash equilibrium; empirical model; neural network; XGBoost.

DOI: 10.1504/IJSMM.2023.133786

International Journal of Sport Management and Marketing, 2023 Vol.23 No.6, pp.543 - 559

Received: 23 Apr 2022
Received in revised form: 18 Oct 2022
Accepted: 17 Nov 2022
Published online: 03 Oct 2023 *

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