Abstract
One promising application of data analytics in soccer is the identification of groups of players with similar playing styles. This can enhance recruitment processes and assist in tactical decisions, such as substitutions during a match. Previous research has explored various methods to measure a player’s contribution, identify key variables for player positions, and predict market value using machine learning techniques. However, these efforts often focus on traditional player positions or a limited set of gameplay characteristics, lacking a comprehensive analysis of player participation. This paper introduces a novel approach to clustering similar soccer players using supervised clustering and dimensionality reduction. Unlike previous studies, our method aims to refine the well-known positions of players and incorporates a wide range of in-game metrics to provide a holistic view of each player’s playing style.
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Notes
- 1.
Marginal contributions in the context of machine learning refers to a measure that quantifies the influence that each feature has on the ability of a ML model to predict.
- 2.
A decision rule is an if-then statement, consisting of a condition or antecedent and a prediction. Its usefulness is measured by two values: precision and recall.
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Acknowledgments
This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government, project TED2021-131295B-C32 from the State Research Agency, and DIGITAL2022 CLOUDAI02/S8760000 from the European Commission. Partial support is also provided by the Spanish Ministry of Science and Innovation under projects PID2021-124975OB-I00 and PID2021-123673OB-C31, partially financed with FEDER funds. Also, PDC2022-133161-C32 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.
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Vidal, A.S., Sanchez-Anguix, V., Alberola, J.M. (2025). A Supervised Clustering Approach to Detect Similar Soccer Players. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_10
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