Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League
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.
Code (0)
등록된 구현이 없습니다.
Tasks
PositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
6MapNet: Representing soccer players from tracking data by a triplet network
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles…
TripletOffensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role
In a basketball game, scoring efficiency holds significant importance due to the numerous offensive possessions per game. Enhancing scoring efficiency necessitates effective collaboration among players with diverse playi…
ClusteringAn Unsupervised Video Game Playstyle Metric via State Discretization
On playing video games, different players usually have their own playstyles. Recently, there have been great improvements for the video game AIs on the playing strength. However, past researches for analyzing the behavio…
Atari GamesCar RacingDecision MakingLearning Controllable and Diverse Player Behaviors in Multi-Agent Environments
This paper introduces a reinforcement learning framework that enables controllable and diverse player behaviors without relying on human gameplay data. Existing approaches often require large-scale player trajectories, t…
Reinforcement LearningMulti-Modal Trajectory Prediction of NBA Players
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their…
Decision MakingPredictionTrajectory Prediction