The wage of a football player is a function of numerous aspects such as the player’s skills, performance in the previous seasons, age, trajectory of improvement, personality, and more. Based on these aspects, salaries of football players are determined through negotiation between the team management and the agents. In this study we propose an objective quantitative method to determine football players’ wages based on their skills. The method is based on the application of pattern recognition algorithms to performance (e.g., scoring), behavior (e.g., aggression), and abilities (e.g., acceleration) data of football players. Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). The proposed method can be used as an assistive technology when negotiating players salaries, as well as for performing quantitative analysis of links between the salary and the performance of football players. The method is based on the performance and skills of the players, but does not take into account aspects that are not related directly to the game such as the popularity of the player among fans, predicted merchandise sales, etc, which are also factors of high impact on the salary, especially in the case of the team lead players and superstars. Analysis of player salaries in eight European football leagues show that the skills that mostly affect the salary are largely consistent across leagues, but some differences exist. Analysis of underpaid and overpaid players shows that overpaid players tend to be stronger, but are inferior in their reactions, vision, acceleration, agility, and balance compared to underpaid football players.
Based on previous research it can be stated that modelling sport economics related demand curves (e.g. demand for sport events and athletes) is different from other types of modelling. The difference lies in the fact that some parts of the demand curves are nearly horizontal in case of sport goods and nearly vertical in case of athletes, because the price of sport events is inflexible and at the same time, salaries of top athletes are extremely flexible. This study investigates parameter estimation methods appropriate for the relevant demand functions of sport economics. In this cases the generally used ordinary least squares estimator is less robust, so the weighted least squares estimators are able to handle heteroskedasticity. If the distribution of the variables is known, the Newey-West heteroscedasticity corrected estimates give even stronger results. The empirical study analyses footballer transfer fees in top European leagues and identifies a threshold at which the traditional supply-demand functions are not appropriate. According to the results, word class athletes, in a way, can be considered prestige goods for which demand may be irrational.
András, K. (2003). Üzleti elemek a sportban, a labdarúgás példáján /Business elements in sports through the example of football/. Ph.D. thesis, Budapest: Budapest University of Economics Sciences and Public Administration.
Andreff, W. (2008). Globalization of the SportsEconomy. Rivista di diritto ed economia dello sport, IV(3), 13-32.
Andreff, W. (2010). What future sustainable funding model(s) for grassroots sports in the internal market? Brussels Conference, February 16th. Retrieved June 27, 2013