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Spatial movement pattern recognition in soccer based on relative player movements

Jasper Beernaerts (UGent) , Bernard De Baets (UGent) , Matthieu Lenoir (UGent) and Nico Van de Weghe (UGent)
(2020) PLOS ONE. 15(1).
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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.
Keywords
KERMIT, TIME COORDINATION DYNAMICS, SCORE-BOX POSSESSIONS, HEART-RATE RESPONSES, PROFESSIONAL SOCCER, PLAYING TACTICS, FOOTBALL, BEHAVIOR, GAME, VARIABLES, STYLES

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MLA
Beernaerts, Jasper, et al. “Spatial Movement Pattern Recognition in Soccer Based on Relative Player Movements.” PLOS ONE, edited by Filipe Manuel Clemente, vol. 15, no. 1, 2020, doi:10.1371/journal.pone.0227746.
APA
Beernaerts, J., De Baets, B., Lenoir, M., & Van de Weghe, N. (2020). Spatial movement pattern recognition in soccer based on relative player movements. PLOS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227746
Chicago author-date
Beernaerts, Jasper, Bernard De Baets, Matthieu Lenoir, and Nico Van de Weghe. 2020. “Spatial Movement Pattern Recognition in Soccer Based on Relative Player Movements.” Edited by Filipe Manuel Clemente. PLOS ONE 15 (1). https://doi.org/10.1371/journal.pone.0227746.
Chicago author-date (all authors)
Beernaerts, Jasper, Bernard De Baets, Matthieu Lenoir, and Nico Van de Weghe. 2020. “Spatial Movement Pattern Recognition in Soccer Based on Relative Player Movements.” Ed by. Filipe Manuel Clemente. PLOS ONE 15 (1). doi:10.1371/journal.pone.0227746.
Vancouver
1.
Beernaerts J, De Baets B, Lenoir M, Van de Weghe N. Spatial movement pattern recognition in soccer based on relative player movements. Clemente FM, editor. PLOS ONE. 2020;15(1).
IEEE
[1]
J. Beernaerts, B. De Baets, M. Lenoir, and N. Van de Weghe, “Spatial movement pattern recognition in soccer based on relative player movements,” PLOS ONE, vol. 15, no. 1, 2020.
@article{8643422,
  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.}},
  articleno    = {{e0227746}},
  author       = {{Beernaerts, Jasper and De Baets, Bernard and Lenoir, Matthieu and Van de Weghe, Nico}},
  editor       = {{Clemente, Filipe Manuel}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{KERMIT,TIME COORDINATION DYNAMICS,SCORE-BOX POSSESSIONS,HEART-RATE RESPONSES,PROFESSIONAL SOCCER,PLAYING TACTICS,FOOTBALL,BEHAVIOR,GAME,VARIABLES,STYLES}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{16}},
  title        = {{Spatial movement pattern recognition in soccer based on relative player movements}},
  url          = {{http://doi.org/10.1371/journal.pone.0227746}},
  volume       = {{15}},
  year         = {{2020}},
}

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