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

Abstract

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 ( $$\:{\varvec{S}}_{\varvec{G}}$$ =37.854%) and CFACL ( $$\:{\varvec{S}}_{\varvec{G}}$$ =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.

Keywords

• Soccer
• Match performance
• Winning determinants
• Machine learning
• Performance analysis
• SHAP