Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review.
In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with comp...
| Published in: | European Journal of Sport Science Vol. 21; no. 4; pp. 481 - 497 |
|---|---|
| Main Authors: | , , , , , , , , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Apr2021
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150428444&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150428444 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17461391 IX3 jtl: European Journal of Sport Science issn: 17461391 maglogo: Y pubinfo: dt: Apr2021 vid: 21 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 150428444 145602181 150428444 150428444 10.1080/17461391.2020.1747552 150428444 ppf: 481 ppct: 16 formats: tig: atl: Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review. aug: au: Goes, F.R. Meerhoff, L.A. Bueno, M.J.O. Rodrigues, D.M. Moura, F.A. Brink, M.S. Elferink-Gemser, M.T. Knobbe, A.J. Cunha, S.A. Torres, R.S. Lemmink, K.A.P.M. affil: Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands sug: subj: Data Analytics Athletic Performance Evaluation Soccer Movement Evaluation Task Performance and Analysis Methods Human Systematic Review Sports Science Conceptual Framework Models, Theoretical Spatial Behavior ab: In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases, resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis of tactical behaviour, albeit in – sometimes radically – different ways. Accordingly, we present a multidisciplinary framework where each domain's contributions to feature construction, modelling and interpretation can be situated. We discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which is pivotal in unlocking the potential of position tracking data in sports analytics. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|