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Styles of play in professional soccer: an approach of the Chinese Soccer Super League

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  • Carlos Lago-Peñas
  • Miguel Gómez-Ruano
  • Gai Yang

Abstract

Describing and measuring different styles of play that soccer teams can adopt during a match is a very important step towards a more predictive and prescriptive performance analysis. The current study aimed to identify and measure different styles of play in professional soccer. The sample comprises all 240 matches in the Chinese Soccer Super League during the 2016 season. Data were examined using a linear regression analysis and a factor analysis. Five factors had eigenvalues greater than 1 and explained 79.6% of the total variance. The following styles of play were found: Factor 1 (“possession” style of play, correlated with the ball possession, ball possession in opponent half and in the final third of the field, positional attacks, passes, accurate passes, passes forward and back), Factor 2 (set pieces attack, correlated positively with the number of set pieces attacks, and attacks), Factor 3 (counterattacking play, correlated with interceptions, interceptions in opponents half, recovered balls, and number of counterattacks) and Factor 4 and 5 (transitional play, correlated with lost balls, and picking up free balls). These metrics may allow coaches and analysts to classify the teams’ into specific profiles of playing styles.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:rpanxx:v:17:y:2017:i:6:p:1073-1084
    DOI: 10.1080/24748668.2018.1431857
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    References listed on IDEAS

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    1. Craig Wright & Steve Atkins & Remco Polman & Bryan Jones & Lee Sargeson ., 2011. "Factors Associated with Goals and Goal Scoring Opportunities in Professional Soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(3), pages 438-449, December.
    2. 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.
    3. Adam Hewitt & Grace Greenham & Kevin Norton, 2016. "Game style in soccer: what is it and can we quantify it?," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(1), pages 355-372, April.
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    Cited by:

    1. Jasper Beernaerts & Bernard De Baets & Matthieu Lenoir & Nico Van de Weghe, 2020. "Spatial movement pattern recognition in soccer based on relative player movements," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-16, January.
    2. Gong, Bingnan & Zhou, Changjing & Gómez, Miguel-Ángel & Buldú, J.M., 2023. "Identifiability of Chinese football teams: A complex networks approach," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. 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.
    4. 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.

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