Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League
Yuesen Li, Shouxin Zong, Yanfei Shen, Zhiqiang Pu, Miguel-\'Angel, G\'omez, Yixiong Cui

TL;DR
This study characterizes playing styles of football players in the Chinese Super League by integrating spatial data into Player Vectors and applying NMF, revealing 18 distinct styles and their performance contributions.
Contribution
It introduces a novel approach combining spatial information with Player Vectors and NMF to identify detailed playing styles in professional football.
Findings
Identified 18 distinct playing styles in CSL players.
Player styles of forwards and midfielders align with performance trends.
High-rated players exhibit multifunctional playing styles.
Abstract
Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and ten types of match events with the corresponding coordinates for all the lineup players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into 8 positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract…
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Taxonomy
MethodsCircular Smooth Label
