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Performance Profiles based on Archetypal Athletes

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  • Manuel J. A. Eugster

Abstract

Performance indicators and, on their basis, performance profiles are one of the foundations of performance analysis in sports. Obviously, the crux is to develop performance profiles which allow to evaluate the subjects of interest accurately. The present paper contributes a further approach to the existing toolbox of profiling methods. Performance profiles based on archetypal athletes are not based on typical, i.e., mean, performances but on extreme performances—usually the most interesting aspect in sports. Archetypal athletes (outstanding—positive and/or negative—performers) are computed and performers are related to these archetypal athletes. As the archetypal athletes are interpretable, an easy interpretation of the performers’ profiles follows. The method is demonstrated on basketball statistics and soccer skill ratings.

Suggested Citation

  • Manuel J. A. Eugster, 2012. "Performance Profiles based on Archetypal Athletes," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 12(1), pages 166-187, April.
    • Handle: RePEc:taf:rpanxx:v:12:y:2012:i:1:p:166-187
    DOI: 10.1080/24748668.2012.11868592
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    Cited by:

    1. Stefan Weinert, 2012. "Diversity of the DAX30 Executive Board Members: Aspiration and Reality [Diversity der DAX30-Vorstände: Anspruch und Wirklichkeit]," Duesseldorf Working Papers in Applied Management and Economics 20, Duesseldorf University of Applied Sciences.
    2. Moliner, Jesús & Epifanio, Irene, 2019. "Robust multivariate and functional archetypal analysis with application to financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 195-208.
    3. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.
    4. Epifanio, Irene, 2016. "Functional archetype and archetypoid analysis," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 24-34.
    5. Aleix Alcacer & Irene Epifanio & M Victoria Ibáñez & Amelia Simó & Alfredo Ballester, 2020. "A data-driven classification of 3D foot types by archetypal shapes based on landmarks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
    6. Vinué, Guillermo & Epifanio, Irene & Alemany, Sandra, 2015. "Archetypoids: A new approach to define representative archetypal data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 102-115.

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