Computational Estimation of Football Player Wages

L. Yaldo 1  and L. Shamir 1
  • 1 Lawrence Technological University,


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1):37–66.

  • Aldous, D. (1993). The continuum random tree III. The Annals of Probability, 248–289.

  • Arnedt, R. B. (1998). European union law and football nationality restrictions: the economics and politics of the bosman decision. Emory International Law Review, 12, 1091.

  • Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning for control. In Lazy learning (pp. 75-113). Springer Netherlands.

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Machine Learning, 128, 1–58.

  • Bryson, A., Rossi, G., & Simmons, R. (2014). The migrant wage premium in professional football: a superstar effect? Kyklos, 67(1), 12–28.

  • Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). Evaluation of research using computerised tracking systems (amisco r and prozone r) to analyse physical performance in elite soccer: A systematic review. Sports Medicine, 44(5), 701–712.

  • Cleary, J. G., Trigg, L. E., et al. (1995). K*: An instance-based learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine learning, 5, 108–114.

  • Dasarathy, B. V. (1994). Minimal consistent set (mcs) identification for optimal nearest neighbor decision systems design. IEEE Transactions on Systems, Man, and Cybernetics, 24(3), 511–517.

  • Dejonghe, T. & Van Opstal, W. (2010). Competitive balance between national leagues in european football after the bosman case. Rivista di Diritto ed Economia dello Sport, 6(2), 41–61.

  • Feess, E., Gerfin, M., & Muehlheusser, G. (2010). The incentive effects of long-term contracts on performance-evidence from a natural experiment in european soccer. Technical Report, Mimeo: Berlin.

  • Frank, E., Hall, M., & Pfahringer, B. (2002). Locally weighted naive bayes. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 249–256.

  • Frick, B. (2006). Salary determination and the pay-performance relationship in professional soccer: Evidence from germany. Sports Economics After Fifty Years: Essays in Honour of Simon Rottenberg. Oviedo: Ediciones de la Universidad de Oviedo, 125–146.

  • Frick, B. (2007). The football player’s labor market: Empirical evidence from the major european leagues. Scottish Journal of Political Economy, 54(3), 422–446.

  • Frick, B. (2011). Performance, salaries, and contract length: empirical evidence from german soccer. International Journal of Sport Finance, 6(2), 87.

  • Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.

  • Garcia-del Barrio, P., & Pujol, F. (2007). Pay and performance in the spanish soccer league: who gets the expected monopsony rents. Technical report, University of Navarra, Spain

  • Garcia-del Barrio, P., & Pujol, F. (2009). The rationality of under-employing the bestperforming soccer players. Labour, 23(3), 397–419.

  • Giulianotti, R. (2012). Football. Wiley Online Library.

  • Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.

  • Jensen, M. M., Grønbæk, K., Thomassen, N., Andersen, J., and Nielsen, J. (2014). Interactive football-training based on rebounders with hit position sensing and audio-visual feedback. Intentional Journal of Computer Science in Sport, 13(1), 57–68.

  • Kase, K., De Hoyos, I. U., Sanchis, C. M., & Breton, M. O. (2007). The proto-image of real madrid: implications for marketing and management. International Journal of Sports Marketing and Sponsorship, 8(3), 7–28.

  • Kohavi, R. (1995). The power of decision tables. In European Conference on Machine Learning, 174–189.

  • Lames, M., McGarry, T., Nebel, B., & Roemer, K. (2011). Computer science in sportspecial emphasis: Football (dagstuhl seminar 11271). Dagstuhl Reports, 1(7).

  • Markovits, A. S., & Green, A. I. (2017). FIFA, the video game: a major vehicle for soccer’s popularization in the United States. Sport in Society, 20(5-6), 716-734.

  • Muller, J. C., Lammert, J., & Hovemann, G. (2012). The financial fair play regulations of uefa: An adequate concept to ensure the long-term viability and sustainability of european club football? International Journal of Sport Finance, 7(2), 117.

  • O’Donoghue, P. & Robinson, G. (2009). Validity of the prozone3 r player tracking system: A preliminary report. International Journal of Computer Science in Sport, 8(1), 37–53.

  • Orejan, J. (2011). Football/Soccer: History and tactics. McFarland. Jefferson, NC, USA.

  • Prasetio, D. (2016). Predicting football match results with logistic regression. In International Conference on Advanced Informatics: Concepts, Theory And Application, 1–5.

  • Robnik-Sikonja, M. & Kononenko, I. (1997). An adaptation of relief for attribute estimation in regression. In Machine Learning: Proceedings of the Fourteenth International Conference, 296–304.

  • Rohde, M. & Breuer, C. (2016). Europes elite football: Financial growth, sporting success, transfer investment, and private majority investors. International Journal of Financial Studies, 4(2), 12.

  • Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 287–294.

  • Shin, J. & Gasparyan, R. (2014). A novel way to soccer match prediction. Technical Report, Stanford U., CA. USA.

  • Siegle, M., Stevens, T., & Lames, M. (2013). Design of an accuracy study for position detection in football. Journal of Sports Sciences, 31(2), 166–172.

  • Torgler, B. & Schmidt, S. L. (2007). What shapes player performance in soccer? empirical findings from a panel analysis. Applied Economics, 39(18), 2355–2369.

  • Torgler, B., Schmidt, S. L., & Frey, B. S. (2006). Relative income position and performance: an empirical panel analysis.

  • Wicker, P., Prinz, J., Weimar, D., Deutscher, C., & Upmann, T. (2013). No pain, no gain? effort and productivity in professional soccer. International Journal of Sport Finance, 8(2), 124.


Journal + Issues