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Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga

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  • T. Kirschstein
  • Steffen Liebscher

Abstract

The paper examines to what extent a player's market value depends on his skills. Therefore, a data set covering 28 performance measures and the market values of about 493 players from 1. and 2. German Bundesliga is analysed. Applying robust analysis techniques, we are able to robustly estimate market values of soccer players. The results show (1) that there are significantly underrated and overrated players and (2) that a player's affiliation to a certain team may contribute to his market value. We conclude that a club's reputation affects the market values of its players and that star players are in tendency overrated.

Suggested Citation

  • T. Kirschstein & Steffen Liebscher, 2019. "Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(7), pages 1336-1349, May.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1336-1349
    DOI: 10.1080/02664763.2018.1540689
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    References listed on IDEAS

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    1. Bernd Frick, 2007. "The Football Players' Labor Market: Empirical Evidence From The Major European Leagues," Scottish Journal of Political Economy, Scottish Economic Society, vol. 54(3), pages 422-446, July.
    2. Steffen Liebscher & Thomas Kirschstein, 2015. "Efficiency of the pMST and RDELA location and scatter estimators," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 63-82, January.
    3. Koller, Manuel & Stahel, Werner A., 2011. "Sharpening Wald-type inference in robust regression for small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2504-2515, August.
    4. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
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    Cited by:

    1. Francesca Pancotto & Giorgio Addessi & Nicola Auteri, 2024. "Soccer Bubble: Is There a Speculative Bubble in the Price of International Soccer Players?," Journal of Sports Economics, , vol. 25(5), pages 535-556, June.
    2. McHale, Ian G. & Holmes, Benjamin, 2023. "Estimating transfer fees of professional footballers using advanced performance metrics and machine learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 389-399.
    3. Mattia Cefis & Maurizio Carpita, 2024. "The higher-order PLS-SEM confirmatory approach for composite indicators of football performance quality," Computational Statistics, Springer, vol. 39(1), pages 93-116, February.
    4. Coates, Dennis & Parshakov, Petr, 2022. "The wisdom of crowds and transfer market values," European Journal of Operational Research, Elsevier, vol. 301(2), pages 523-534.
    5. Kristijan Breznik & Vincenzo Candila & Antonina Milekhina & Marialuisa Restaino, 2025. "Eras of dominance: identifying strong and weak periods in professional tennis," Computational Statistics, Springer, vol. 40(4), pages 2049-2066, April.
    6. Mattia Cefis & Maurizio Carpita, 2025. "On the CTA-PLS test for hierarchical models: an application to the football player’s performance," Computational Statistics, Springer, vol. 40(4), pages 2135-2155, April.
    7. Maurizio Carpita & Paola Pasca & Serena Arima & Enrico Ciavolino, 2023. "Clustering of variables methods and measurement models for soccer players’ performances," Annals of Operations Research, Springer, vol. 325(1), pages 37-56, June.
    8. Lukas Richau & Florian Follert & Monika Frenger & Eike Emrich, 2021. "The Rainmaker?! The impact of investors on transfer fees in the English Premier League," Working Paper 187/2021, Helmut Schmidt University, Hamburg.

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