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 st...
| Publicado en: | Journal of Sports Sciences Vol. 40; no. 14; pp. 1629 - 1641 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158669700&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158669700 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640414 5BV jtl: Journal of Sports Sciences issn: 02640414 maglogo: Y pubinfo: dt: Jul2022 vid: 40 iid: 14 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 158669700 157842056 158669700 158669700 10.1080/02640414.2022.2096771 158669700 ppf: 1629 ppct: 12 formats: tig: atl: Characterizing player's playing styles based on player vectors for each playing position in the Chinese Football Super League. aug: au: Li, Yuesen Zong, Shouxin Shen, Yanfei Pu, Zhiqiang Gómez, Miguel-Ángel Cui, Yixiong affil: School of Sports Engineering, Beijing Sport University, Beijing, China sug: subj: Sporting Events Soccer Athletes Task Performance and Analysis Evaluation Athletic Performance Chinese Persons Human Team Sports Movement Talent Identification, Sports Cluster Analysis Machine Learning ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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