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Chinese soccer association super league, 2012–2017: key performance indicators in balance games

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  • Changjing Zhou
  • Shaoliang Zhang
  • Alberto Lorenzo Calvo
  • Yixiong Cui

Abstract

The aim of the present study was to identify the game-related statistics which discriminate between winning, drawing and losing teams in Chinese Soccer Association Super League. The sample included 1056 balance games from the 2012–2017 Chinese Soccer Association Super League. Physical and technical game-related statistics were gathered. A one-way analysis of variance and discriminant analysis of data was done. The results showed that winning teams were significantly higher for the following game statistics: shots, shots on target, 50–50 challenge won, offsides, sprinting distance, sprinting effort, sprinting distance in ball possession and high-speed-running distance in ball possession. Losing teams had significantly higher averages in the variable crosses, passes, forward passes, sprinting distance out of ball possession and high-speed-running distance out of ball possession. Discriminant analysis concluded the following: the variables that discriminate between winning, drawing and losing teams were the shots on target, sprinting distance in ball possession, quality of opposition, passes and forward passes. Coaches and players should be aware of these different profiles in order to design and evaluate practices and competitions for their teams.

Suggested Citation

  • Changjing Zhou & Shaoliang Zhang & Alberto Lorenzo Calvo & Yixiong Cui, 2018. "Chinese soccer association super league, 2012–2017: key performance indicators in balance games," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(4), pages 645-656, July.
    • Handle: RePEc:taf:rpanxx:v:18:y:2018:i:4:p:645-656
    DOI: 10.1080/24748668.2018.1509254
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    Cited by:

    1. Eduard Pons & José Carlos Ponce-Bordón & Jesús Díaz-García & Roberto López del Campo & Ricardo Resta & Xavier Peirau & Tomas García-Calvo, 2021. "A Longitudinal Exploration of Match Running Performance during a Football Match in the Spanish La Liga: A Four-Season Study," IJERPH, MDPI, vol. 18(3), pages 1-10, January.
    2. Christoph Buehren & Dominic Jung, 2022. "Performing without pressure? The effect of ghost games on effort- and skill-based tasks in the football Bundesliga," MAGKS Papers on Economics 202227, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Changjing Zhou & William G. Hopkins & Wanli Mao & Alberto L. Calvo & Hongyou Liu, 2019. "Match Performance of Soccer Teams in the Chinese Super League—Effects of Situational and Environmental Factors," IJERPH, MDPI, vol. 16(21), pages 1-13, November.
    4. Li, Yuesen & Ma, Runqing & Gonçalves, Bruno & Gong, Bingnan & Cui, Yixiong & Shen, Yanfei, 2020. "Data-driven team ranking and match performance analysis in Chinese Football Super League," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    5. Alejandro Sabarit & Rafael E. Reigal & Juan P. Morillo-Baro & Rocío Juárez-Ruiz de Mier & Auxiliadora Franquelo & Antonio Hernández-Mendo & Coral Falcó & Verónica Morales-Sánchez, 2020. "Cognitive Functioning, Physical Fitness, and Game Performance in a Sample of Adolescent Soccer Players," Sustainability, MDPI, vol. 12(13), pages 1-12, June.
    6. 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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