Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League
This work addresses the need for better player analysis in football clubs for scouting and match preparation, but it is incremental as it builds on existing Player Vectors and NMF methods.
This study tackled the problem of characterizing playing styles in football by integrating spatial information with technical performance data from the Chinese Football Super League, resulting in the discovery of 18 distinct playing styles across positions and identifying trends and multifunctional styles in high-rated players.
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 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.