Data-driven classification of playing styles and match outcome 
prediction in UEFA Champions League teams

Yonghan Zhong; Ying Xu; Kecheng Zhu; Jorge Diaz-Cidoncha Garcia; Miguel Ángel Gómez Ruano; Qing Yi

doi:10.5114/biolsport.2026.154944

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

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abstract:

Original paper

Data-driven classification of playing styles and match outcome prediction in UEFA Champions League teams

Yonghan Zhong

^{1, 2}

,

Ying Xu

¹

,

Kecheng Zhu

³

,

Jorge Diaz-Cidoncha Garcia

⁴

,

Miguel Ángel Gómez Ruano

⁵

,

Qing Yi

¹

College of Physical Education, Dalian University, 116622 Dalian, China
School of Athletic Performance, Shanghai University of Sport, 200438 Shanghai, China
Applied Technology College of Dalian Ocean University, 116023 Dalian, China
Fédération Internationale de Football Association FIFA-Strasse 20 P.O. Box 8044 Zurich Switzerland
Facultad de Ciencias de la Actividad Física y del Deporte-Inef Madrid, Universidad Politécnica de Madrid, Madrid, Spain

Biol Sport.2026;43:575–586

DOI: https://doi.org/10.5114/biolsport.2026.154944

Online publish date: 2025/11/03

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This study proposes a data-driven framework for classifying UEFA champions League teams into possession-based and counterattacking styles and predicting match outcomes based on key performance indicators (KPIs). Dimensionality reduction via an autoencoder was combined with K-means clustering to identify underlying tactical patterns beyond traditional possession metrics. Feature selection was performed using LASSO, Boruta, and XGBoost to determine the most relevant KPIs. Predictive models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and LightGBM, were evaluated using AUC and F1 Score. SVM achieved the highest performance for possession-based teams, whereas KNN outperformed other models for counterattacking teams. The results revealed distinct style-specific performance profiles. For possession-based teams, higher possession and key passes correlated negatively with winning probability, while crosses and long-range shots were positively associated with success. In counterattacking teams, increased possession and key passes improved match outcomes, whereas crosses and shots from outside the box showed negative associations. Defensive actions, particularly clearances, were strongly associated with improved defensive stability and match success, especially among counterattacking teams. This framework improves the accuracy of tactical classification and provides interpretable associations between KPIs and match outcomes. The findings can inform style-specific tactical planning and performance monitoring, enabling coaches to adjust offensive or defensive training priorities according to team strategy

keywords:

Data-driven classification of playing styles and match outcome prediction in UEFA Champions League teams

Yonghan Zhong 1, 2 , Ying Xu 1 , Kecheng Zhu 3 , Jorge Diaz-Cidoncha Garcia 4 , Miguel Ángel Gómez Ruano 5 , Qing Yi 1

Football, Playing style, Machine learning, Feature selection, Match analysis

Yonghan Zhong

^{1, 2}

,

Ying Xu

¹

,

Kecheng Zhu

³

,

Jorge Diaz-Cidoncha Garcia

⁴

,

Miguel Ángel Gómez Ruano

⁵

,

Qing Yi

¹