Characterizing player’s playing styles based on player vectors for each playing position in the Chinese Football Super League

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 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight 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 different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.

KEYWORDS:

• Soccer
• match analysis
• style of play
• performance profile
• machine learning

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary material

Supplemental data for this article can be accessed online https://doi.org/10.1080/02640414.2022.2096771

Additional information

Funding

This work was supported in part by the National Key Research and Development Program of China under grants 2020AAA0103404, and by National Natural Science Foundation of China under grants 72071018 and 72101032. The corresponding author (Y.C.) was supported by the Fundamental Research Funds for the Central Universities of China (2021TD008)