IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i6p3176-d520297.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/6/3176/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/6/3176/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nimai Parmar & Nic James & Mike Hughes & Huw Jones & Gary Hearne, 2017. "Team performance indicators that predict match outcome and points difference in professional rugby league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 1044-1056, November.
    2. Gomez, Miguel-Angel & Reus, Marc & Parmar, Nimai & Travassos, Bruno, 2020. "Exploring elite soccer teams’ performances during different match-status periods of close matches’ comebacks," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    3. Serafeim Moustakidis & Spyridon Plakias & Christos Kokkotis & Themistoklis Tsatalas & Dimitrios Tsaopoulos, 2023. "Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics," Future Internet, MDPI, vol. 15(5), pages 1-18, May.
    4. Fiona Carmichael & Dennis Thomas, 2014. "Team performance: production and efficiency in football," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 10, pages 143-165, Edward Elgar Publishing.
    5. Athalie J Redwood-Brown & Peter G O’Donoghue & Alan M Nevill & Chris Saward & Caroline Sunderland, 2019. "Effects of playing position, pitch location, opposition ability and team ability on the technical performance of elite soccer players in different score line states," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-21, February.
    6. Bonhomme, Stphane & Robin, Jean-Marc, 2009. "Consistent noisy independent component analysis," Journal of Econometrics, Elsevier, vol. 149(1), pages 12-25, April.
    7. Fernando Castelló-Sirvent & Pablo Pinazo-Dallenbach, 2021. "Corruption Shock in Mexico: fsQCA Analysis of Entrepreneurial Intention in University Students," Mathematics, MDPI, vol. 9(14), pages 1-31, July.
    8. Matkovskyy, Roman, 2013. "To the Problem of Financial Safety Estimation: the Index of Financial Safety of Turkey," MPRA Paper 47673, University Library of Munich, Germany.
    9. Jha, Raghbendra & Murthy, K. V. Bhanu, 2003. "An inverse global environmental Kuznets curve," Journal of Comparative Economics, Elsevier, vol. 31(2), pages 352-368, June.
    10. Rodríguez-Fuentes, Carlos Javier & Hernández-López, Montserrat, 1997. "Análisis de diferencias estructurales interregionales determinantes en el impacto de la política monetaria," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 7, pages 141-157, Junio.
    11. Ivaldi, Enrico, 2013. "Proposal of a country risk index based on a factorial analysis - Una proposta di indice di rischio paese basato sull’analisi fattoriale: una applicazione ai paesi del sud del Mediterraneo e ai paesi d," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 66(2), pages 231-249.
    12. Vesselina Dimitrova & Georgi Marinov & Lino Manosperta, 2019. "Developing Low-Carbon Tourism In Puglia: Case Study Of I. Archeo.S Project," Economic Archive, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 2 Year 20, pages 16-32.
    13. Noor Nahar Begum & Sarabia Rahman, 2016. "An Analytical Study on Investors¡¯ Preference towards Mutual Fund Investment: A Study in Dhaka City, Bangladesh," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(10), pages 184-191, October.
    14. Coppola, A. & Ianuario, S. & Chinnici, G. & Di Vita, G. & Pappalardo, G. & D'Amico, D., 2018. "Endogenous and Exogenous Determinants of Agricultural Productivity: What Is the Most Relevant for the Competitiveness of the Italian Agricultural Systems?," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(2).
    15. De Nicola, Arianna & Gitto, Simone & Mancuso, Paolo, 2013. "Airport quality and productivity changes: A Malmquist index decomposition assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 67-75.
    16. Anselmo Ruiz-de-Alarcón-Quintero & Blanca De-la-Cruz-Torres, 2024. "An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance," Data, MDPI, vol. 9(9), pages 1-9, August.
    17. Henk Kiers, 1994. "Simplimax: Oblique rotation to an optimal target with simple structure," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 567-579, December.
    18. Dolores Gallardo-Vázquez, 2023. "Attributes influencing responsible tourism consumer choices: Sustainable local food and drink, health-related services, and entertainment," Oeconomia Copernicana, Institute of Economic Research, vol. 14(2), pages 645-686, June.
    19. Iara Oliveira Fernandes & José Fernandes de Melo Filho & Karolina Oliveira Rocha Montenegro & Ésio de Castro Paes & Sergio Ricardo Matos Almeida & João Albany Costa & Franceli da Silva, 2024. "Physical and Chemical Attributes of Yellow Oxisol With the Application of Cassava Wastewater After Intensive Mechanical Preparation," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 11(6), pages 113-113, April.
    20. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3176-:d:520297. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.