Assessment of the market value of Liga MX footballers based on their sporting performance and qualitative variables in the 2021-2022 season
DOI:
https://doi.org/10.46661/rev.metodoscuant.econ.empresa.11486Keywords:
Machine learning, market value of football playersAbstract
This work identifies the key variables influencing the market value of Liga MX soccer players, using performance information from the 2021-2022 season and other qualitative data. We employed three algorithms to predict the market value assigned by Transfermarkt: multiple linear regression, decision trees, and random forests. According to the model with the lower mean squared error (random forests), the performance variables with the greatest impact on a player’s market value are total actions, dribbles, and duels in which the player participate. In addition to performance in the previous season, highly relevant qualitative variables include the player’s team, contract year, and participation in the national team. We recommend that Liga MX clubs use similar analytical methods to allocate their financial resources more efficiently.
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Al-Asadi, M. A., & Tasdemir, S. (2022). Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques. IEEE Access, 10, 22631-22645. https://doi.org/10.1109/ACCESS.2022.3154767
Awasthi, P., Beutel, A., Kleindessner, M., Morgenstern, J., & Wang, X. (2021). Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 206-214.https://doi.org/10.1145/3442188.3445884
Behravan, I., & Razavi, S. M. (2021). A Novel Machine Learning Method for Estimating Football Players' Value in the Transfer Market. Soft Computing, 25(3), 2499-2511. https://doi.org/10.1007/s00500-020-05319-3
Deutscher, C., & Büschemann, A. (2016). Does Performance Consistency Pay Off Financially for Players? Evidence From the Bundesliga. Journal of Sports Economics, 17(1), 27-43. https://doi.org/10.1177/1527002514521428
Franceschi, M., Brocard, J., Follert, F., & Gouguet, J. (2023). Determinants of Football Players' Valuation: A Systematic Review. Journal of Economic Surveys. 38(3), 577-600.https://doi.org/10.1111/joes.12552
Frick, B. (2011). Performance, Salaries and Contract Length: Empirical Evidence from German Soccer. International Journal of Sport Finance, 6(2), 87-118.https://doi.org/10.1177/155862351100600201
Herm, S., Callsen-Bracker, H.-M., & Kreis, H. (2014). When the Crowd Evaluates Soccer players' Market Values: Accuracy and Evaluation Attributes of an Online Community. Sport Management Review, 17(4), 484-492. https://doi.org/10.1016/j.smr.2013.12.006
Hill, D. F., Skinner, J., & Grosman, A. (2025). A Review of Football Player Metrics and Valuation Methods: A Typological Framework of Football Player Valuations. Managing Sport and Leisure, 1-24.https://doi.org/10.1080/23750472.2025.2459727
Hogan, V., & Massey, P. (2021). Soccer Clubs and Diminishing Returns: The Case of Paris Saint-Germain. International Journal of Sport Finance, 16(3), 127-138. https://doi.org/10.32731/ijsf/163.082021.02
Keppel, P., & Claessens, T. (2020). How the Volunteers of Data Website Transfermarkt Became Influential Players at European top Football Clubs. Follow the Money. https://www.ftm.eu/articles/transfermarkt-volunteers-european-football
Kuhn, M. (2019). The Caret Package. https://topepo.github.io/caret/index.html
LeDell, E. (2018). useR! Machine Learning Tutorial. https://koalaverse.github.io/machine-learning-in-R/
Majewski, S. (2016). Identification of Factors Determining Market Value of the Most Valuable Football Players. Journal of Management and Business Administration. Central Europe, 24(3), 91-104.https://doi.org/10.7206/jmba.ce.2450-7814.177
Metelski, A. (2021). Factors Affecting the Value of Football Players in the Transfer Market. Journal of Physical Education and Sport, 21(2), 1150 - 1155. https://doi.org/10.7752/jpes.2021.s2145
Miao, H., Cachucho, R., & Knobbe, A. (2015). Football Player's Performance and Market Value. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).
Moya-Villanueva, G. R., & Córdova-Ramos, L. A. (2021). Análisis del Precio de los Futbolistas de la Liga Premier Inglesa con Respecto a sus Estadísticas en FIFA 20. Pensamiento Crítico, 26(2), 165-200.
https://doi.org/10.15381/pc.v26i2.18785
Müller, O., Simons, A., & Weinmann, M. (2017). Beyond Crowd Judgments: Data-Driven Estimation of Market Value in Association Football. European Journal of Operational Research, 263(2), 611-624. https://doi.org/10.1016/j.ejor.2017.05.005
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019). A Public Data set of Spatio-Temporal Match Events in Soccer Competitions. Scientific Data, 6(1), 236. https://doi.org/10.1038/s41597-019-0247-7
Peeters, T. (2018). Testing the Wisdom of Crowds in the Field: Transfermarkt Valuations and International Soccer Results. International Journal of Forecasting, 34(1), 17-29. https://doi.org/10.1016/j.ijforecast.2017.08.002
Prockl, F., & Frick, B. (2018). Wage Determination in a Regulated Labor Market: Empirical Evidence from Major League Soccer (No. 39). Paderborn University, Faculty of Business Administration and Economics.
Sánchez, M., Orgaz, B., Ramirez-Campillo, R., Nakamura, F. Y., Luis-Pereira, J. M., Carretero, G., & Sánchez-Sánchez, J. (2022). Factores Asociados al Valor de Mercado de los Futbolistas Profesionales. Cuadernos de Psicologia Del Deporte, 22(3), 140-150.
https://doi.org/10.6018/cpd.457441
Shin, J., & Gasparyan, R. (2014). A Novel Way to Soccer Match Prediction. Stanford University: Department of Computer Science.
Shu-Ching Chen, Mei-Ling Shyu, & Na Zhao. (2005). An Enhanced Query Model for Soccer Video Retrieval Using Temporal Relationships. 21st International Conference on Data Engineering (ICDE'05), 1133-1134. https://doi.org/10.1109/ICDE.2005.20
Simmons, R. (2022). Professional Labor Markets in the Journal of Sports Economics. Journal of Sports Economics, 23(6), 728-748.
https://doi.org/10.1177/15270025211051062
Singh, P., & Lamba, P. S. (2019). Influence of Crowdsourcing, Popularity and Previous Year Statistics in Market Value Estimation of Football Players. Journal of Discrete Mathematical Sciences and Cryptography, 22(2), 113-126. https://doi.org/10.1080/09720529.2019.1576333
Soto-Valero, C. (2017). A Gaussian Mixture Clustering Model for Characterizing Football Players Using the EA Sports' FIFA Video Games System. [Modelo Basado en Agrupamiento de Mixturas Gaussianas para Caracterizar Futbolistas Utilizando el Sistema de Videojuegos FIFA de EA Sports. RICYDE. Revista Internacional de Ciencias Del Deporte, 13(49), 244-259. https://doi.org/10.5232/ricyde2017.04904
https://doi.org/10.5232/ricyde2017.04904
Stanojevic, R., & Gyarmati, L. (2016). Towards Data-Driven Football Player Assessment. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 167-172. https://doi.org/10.1109/ICDMW.2016.0031
Therneau, T. M., & Atkinson, E. J. (2022). An Introduction to Recursive Partitioning Using the RPART Routines. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf
Yaldo, L., & Shamir, L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, 16(1), 18-38. https://doi.org/10.1515/ijcss-2017-0002
Yiğit, A. T., Samak, B., & Kaya, T. (2020). Football Player Value Assessment Using Machine Learning Techniques. International Conference on Intelligent and Fuzzy Systems,289-297. https://doi.org/10.1007/978-3-030-23756-1_36
Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, 1168, 022022. https://doi.org/10.1088/1742-6596/1168/2/022022
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