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Identifying keys to win in the Chinese professional soccer league

Author

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  • Lijuan Mao
  • Zhaofang Peng
  • Hongyou Liu
  • Miguel-Angel Gómez

Abstract

Quantifying correlations between soccer match statistics and match results is an effective way to identify key performance indicators of soccer competitions. In the current study, generalized linear modelling was employed to identify relationships between 21 performance-related variables and the match outcome (win, draw, loss). Data of all the 480 matches of the 2014 and 2015 season in the Chinese Football Association Super League were collected and analyzed. The cumulative logistic regression was run in the modelling taking the value of each performance-related variable as an independent variable to predict the logarithm of the odds of winning. Relationships were evaluated with magnitude-based inferences and were expressed as effects of a two-standard-deviation increase in the value of each variable on the change in the probability of a team winning a match. Modelling was performed in four match contexts of team and opposition end-of-season rank (classified as upper and lower ranked teams). Shot on Target (positive), Shot Accuracy (positive), Cross Accuracy (trivial), Tackle (trivial) and Yellow Card (trivial) were the five variables that showed consistent effects in all four match contexts, other effects varied depending on the strength of team and opposition. Quantified relationships can provide useful information to coaches and performance analysts in practice of different match scenarios.

Suggested Citation

  • Lijuan Mao & Zhaofang Peng & Hongyou Liu & Miguel-Angel Gómez, 2016. "Identifying keys to win in the Chinese professional soccer league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(3), pages 935-947, December.
    • Handle: RePEc:taf:rpanxx:v:16:y:2016:i:3:p:935-947
    DOI: 10.1080/24748668.2016.11868940
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    Cited by:

    1. S. E. Hill, 2022. "In-game win probability models for Canadian football," Journal of Business Analytics, Taylor & Francis Journals, vol. 5(2), pages 164-178, July.
    2. 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).

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