Spatial movement pattern recognition in soccer based on relative player movements

PLoS One. 2020 Jan 16;15(1):e0227746. doi: 10.1371/journal.pone.0227746. eCollection 2020.

Abstract

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms
  • Athletes / statistics & numerical data
  • Athletic Performance / statistics & numerical data*
  • Basketball / statistics & numerical data
  • Computer Simulation
  • Datasets as Topic
  • Humans
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Running / statistics & numerical data*
  • Soccer / statistics & numerical data*
  • Spatio-Temporal Analysis*

Grants and funding

This work was supported by the Research Foundation Flanders (FWO - https://www.fwo.be) under a grant (FWO 11ZN118) awarded to Jasper Beernaerts. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.