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Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis

Author

Listed:
  • Claudio A. Casal

    (Department of Science of Physical Activity and Sport, Catholic University of Valencia “San Vicente Mártir”, 46900 Valencia, Spain)

  • José L. Losada

    (Department of Social Psychology and Quantitative Psychology, University of Barcelona, 08001 Barcelona, Spain)

  • Daniel Barreira

    (Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, 4099-002 Porto, Portugal)

  • Rubén Maneiro

    (Department of Science of Physical Activity and Sport, Pontifical University of Salamanca, 37001 Salamanca, Spain)

Abstract

The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions, showing fewer number of goals and a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main discriminating performance factors of football. This information allows us to increase knowledge about the key performance indicators (KPI) in football.

Suggested Citation

  • Claudio A. Casal & José L. Losada & Daniel Barreira & Rubén Maneiro, 2021. "Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3176-:d:520297
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    References listed on IDEAS

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    1. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    2. P. D. Jones & N. James & S. D. Mellalieu, 2004. "Possession as a performance indicator in soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 4(1), pages 98-102, August.
    3. Hongyou Liu & Will Hopkins & A. Miguel Gómez & S. Javier Molinuevo, 2013. "Inter-operator reliability of live football match statistics from OPTA Sportsdata," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(3), pages 803-821, December.
    4. Kerys Harrop & Alan Nevill, 2014. "Performance indicators that predict success in an English professional League One soccer team," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(3), pages 907-920, December.
    5. David Adams & Ryland Morgans & Joao Sacramento & Stuart Morgan & Morgan D Williams, 2013. "Successful short passing frequency of defenders differentiates between top and bottom four English Premier League teams," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(3), pages 653-668, December.
    6. Carlos Lago-Peñas & Miguel Gómez-Ruano & Gai Yang, 2017. "Styles of play in professional soccer: an approach of the Chinese Soccer Super League," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 1073-1084, November.
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