@Article{Zhong2026,
journal="Biology of Sport",
issn="0860-021X",
volume="43",
number="1",
year="2026",
title="Data-driven classification of playing styles and match outcome 
prediction in UEFA Champions League teams",
abstract="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",
author="Zhong, Yonghan
and Xu, Ying
and Zhu, Kecheng
and Diaz-Cidoncha Garcia, Jorge
and Gómez Ruano, Miguel Ángel
and Yi, Qing",
pages="575--586",
doi="10.5114/biolsport.2026.154944",
url="http://dx.doi.org/10.5114/biolsport.2026.154944"
}