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Career path clustering of elite soccer players among European Big-5 nations utilizing Dynamic Time Warping

  • Viktor Wolf EMAIL logo , Ralf Lanwehr , Marcel Bieschke and Daniel Leyhr ORCID logo
Published/Copyright: April 4, 2024
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Journal of Quantitative Analysis in Sports
From the journal Journal of Quantitative Analysis in Sports Volume 20 Issue 3

Abstract

Prior clustering approaches of soccer players have employed a variety of methods based on various data categories, but none of them have focused on clustering by career paths characterized through a time series analysis of yearly performance quality. Therefore, this study aims to propose a methodology how a career path can be represented as a time series of a player’s seasonal qualities and then be clustered with players that have a similar career path. The underlying data focuses on soccer players from the five largest European soccer nations (Big-5). This allows for the identification of different types of career paths of players and the investigation of significant disparities between career paths among the Big-5 nations. In line with our proposed methodological approach, we identified and interpreted 13 different clusters of player career paths. These range from the cluster with the highest player quality scores to the pattern comprising players with the weakest scores. Further, the detected clusters show significant differences regarding variables of soccer players’ early career phase in adolescence (e.g., age of debut in professional soccer, years spent in a youth academy). The presented approach might represent a first step for stakeholders in soccer to get an objective insight in players’ career by utilizing mainly freely available data sources.

Keywords: hierarchical time series clustering; European football; professional soccer; career trajectories
Corresponding author: Viktor Wolf, Data Science, South Westphalia University of Applied Sciences, 59872 Meschede, North Rhine-Westphalia, Germany, E-mail:

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-08-31
Accepted: 2024-03-18
Published Online: 2024-04-04
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/jqas-2023-0080
Keywords for this article
hierarchical time series clustering; European football; professional soccer; career trajectories

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Estimating positional plus-minus in the NBA
  4. No cheering in the background? Individual performance in professional darts during COVID-19
  5. Spatial roles in hockey special teams
  6. Career path clustering of elite soccer players among European Big-5 nations utilizing Dynamic Time Warping
  7. The strategic jump-the order effect on winning “The Final Three” in long jump competitions
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