<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 &lt; .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.</p></abstract>

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.

Computational Estimation of Football Player Wages

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Computational Estimation of Football Player Wages"}

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...

Computational Estimation of Football Player Wages

Online veröffentlicht: 22. Juli 2017

Seitenbereich: 18 - 38

DOI: https://doi.org/10.1515/ijcss-2017-0002

Schlüsselwörter
football, soccer, sports economy, machine learning

© 2017 Yaldo, L. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Computational Estimation of Football Player Wages

Online veröffentlicht: 22. Juli 2017

Seitenbereich: 18 - 38

DOI: https://doi.org/10.1515/ijcss-2017-0002

Schlüsselwörterfootball, soccer, sports economy, machine learning

© 2017 Yaldo, L. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
football, soccer, sports economy, machine learning