A1 Journal article (refereed)
Talent identification in soccer using a one-class support vector machine (2019)

Jauhiainen, S., Äyrämö, S., Forsman, H., & Kauppi, J-P. (2019). Talent identification in soccer using a one-class support vector machine. International Journal of Computer Science in Sport, 18(3), 125-136. https://doi.org/10.2478/ijcss-2019-0021

JYU authors or editors

Publication details

All authors or editors: Jauhiainen, S.; Äyrämö, S.; Forsman, H.; Kauppi, J-P.

Journal or series: International Journal of Computer Science in Sport

eISSN: 1684-4769

Publication year: 2019

Volume: 18

Issue number: 3

Pages range: 125-136

Publisher: Sciendo

Publication country: Poland

Publication language: English

DOI: https://doi.org/10.2478/ijcss-2019-0021

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67083

Abstract

Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support vector machine (one-class SVM) on a dataset (N=951) collected from 14-year-old junior soccer players to detect potential future elite players. The mean area under the receiver operating characteristic curve (AUC-ROC) over the tested hyperparameter combinations was 0.763 (std 0.007). The most accurate model was obtained when physical tests, measuring, for example, technical skills, speed, and agility, were used. According to our results, the proposed approach could be useful to support decision-makers in the process of talent identification.

Keywords: data mining; machine learning; talent; sport-specific skills; recognition; football

Free keywords: talent identification; anomaly detection; one-class svm

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Related projects

Ministry reporting: Yes

VIRTA submission year: 2019

JUFO rating: 1