Uncovering Value Drivers of High Performance Soccer Players

Abstract

This article tries to uncover the drivers of soccer players’ market value in the five major European soccer leagues taking into account model uncertainty (variable selection) in a framework with 35 billion potential models. For this purpose, we use a hedonic regression framework and implement Bayesian model averaging (BMA) through Markov chain Monte Carlo model composition (MC3). To deal with endogeneity issues, instrumental variable Bayesian model averaging (IVBMA) is implemented as well. We find very strong, and robust evidence, that the most important value drivers are player’s performance, participation in the national team (senior and under-21), age, and age squared.