Winning determinants between top and second division in Chinese professional football leagues: an interpretable machine learning approach

This study investigated the impact of winning determinants in two professional soccer leagues. The sample was composed of 1,440 Chinese Super Football League (CSL) and Chinese Football Association China League (CFACL) matches (CSL matches = 720; CFACL matches = 720) during the 2017-2019 seasons. The study employed eXtreme Gradient Boosting (XGBoost) to assess the importance of 25 indicators exhibiting significant differences (p < 0.05) in their association with match outcomes, and the SHapley Additive explanations (SHAP) was utilized to interpret these findings. The results showed that scoring performance indicators, such as Shots On Target Inside Box (SOTIB), Shots, and Shots On Target (SOT), significantly influenced outcomes in both the CSL ([Formula: see text]=37.854%) and CFACL ([Formula: see text]=38.934%), with SOTIB being the most impactful. Additionally, this study found that defensive feature clearances were highly influential in both leagues, ranking second only to SOTIB of variable importance. Meanwhile, defensive feature fouls were a more significant factor in determining match outcomes in the CFACL than in the CSL. In both the CSL and CFACL, players must prioritize precision in shooting within the penalty area rather than merely increasing the frequency of shots. For CFACL teams, if consistent high-quality passing is unattainable, effective use of set pieces (e.g., free kicks) could serve as an alternative strategy to organize attacks. These findings can assist coaches in formulating tailored tactical strategies suited to the distinct demands of each league level.

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Publication Details

Journal
BMC Sports Science Medicine and Rehabilitation
Published
2025-04-21
DOI
https://doi.org/10.1186/s13102-025-01130-5
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
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article

Winning determinants between top and second division in Chinese professional football leagues: an interpretable machine learning approach

Dingmeng Ren, Honglin Song, Pengyu Pan, Tianbiao Liu et al.
BMC Sports Science Medicine and Rehabilitation
Sports Analytics and Performance
article

Winning determinants between top and second division in Chinese professional football leagues: an interpretable machine learning approach

Dingmeng Ren, Honglin Song, Pengyu Pan, Tianbiao Liu, Bo Yuan, Jiaxuan Zhu, Liang Zheng
article en

Abstract

This study investigated the impact of winning determinants in two professional soccer leagues. The sample was composed of 1,440 Chinese Super Football League (CSL) and Chinese Football Association China League (CFACL) matches (CSL matches = 720; CFACL matches = 720) during the 2017-2019 seasons. The study employed eXtreme Gradient Boosting (XGBoost) to assess the importance of 25 indicators exhibiting significant differences (p < 0.05) in their association with match outcomes, and the SHapley Additive explanations (SHAP) was utilized to interpret these findings. The results showed that scoring performance indicators, such as Shots On Target Inside Box (SOTIB), Shots, and Shots On Target (SOT), significantly influenced outcomes in both the CSL ([Formula: see text]=37.854%) and CFACL ([Formula: see text]=38.934%), with SOTIB being the most impactful. Additionally, this study found that defensive feature clearances were highly influential in both leagues, ranking second only to SOTIB of variable importance. Meanwhile, defensive feature fouls were a more significant factor in determining match outcomes in the CFACL than in the CSL. In both the CSL and CFACL, players must prioritize precision in shooting within the penalty area rather than merely increasing the frequency of shots. For CFACL teams, if consistent high-quality passing is unattainable, effective use of set pieces (e.g., free kicks) could serve as an alternative strategy to organize attacks. These findings can assist coaches in formulating tailored tactical strategies suited to the distinct demands of each league level.

BMC Sports Science Medicine and RehabilitationVol. 17(1)
German Sport University Cologne (DE), Hebei University of Technology (CN), Beijing Normal University (CN), Beijing Sport University (CN)
Openalex Percentile: Top 88%
Sports Analytics and Performance
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