How do Soccer Teams Coordinate Consecutive Passes? A Visual Analytics System for Analysing the Complexity of Passing Sequences Using Soccer Flow Motifs.
Resumo
The analysis of passing strategies plays a major role in soccer. Soccer managers use scouting, video footage, and soccer data feed to collect information about tactics and player performance. However, the nature of passing strategies is complex enough to reflect what is happening in the match and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing analysis popularized by FC Barcelona's tiki-taka. In this paper, we describe a visual analytics system to analyze the sequence and trajectory of consecutive passing sequences. We describe a two-phase clustering algorithm that extracts typical trajectory clusters in passing sequences, which result in eight predominant clusters. The combined analysis of the sequence and trajectory clusters allow experts to perform multi or single-game analysis in various ways. We show the potential of our approach in case studies using data from the Brazilian and Turkish leagues and report feedback from soccer experts.
Palavras-chave:
Computers and graphics, Formatting, Guidelines
Referências
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J. Wilson. Inverting The Pyramid: The History of Soccer Tactics. 9781568587387, Nation Books (2013)
D. Link, H. Weber. Using individual ball possession as a performance indicator in soccer. Proceeding of the 2015 KDD workshop on large-scale sports analytics, Sydney, Australia (2015)
Shao L., D. Sacha, B. Neldner, M. Stein, T. Schreck. Visual-interactive search for soccer trajectories to identify interesting game situations. Visualization and Data Analysis (2016)
L. Gyarmati, X. Anguera. Automatic extraction of the passing strategies of soccer teams. Proceedings of the 2015 KDD workshop on large-scale sports analytics (2015), pp. 0-3
M. Hughes, I. Franks. Analysis of passing sequences, shots and goals in soccer. J Sports Sci, 23 (5) (2005), pp. 509-514
R. Rein, D. Raabe, D. Memmert. “Which pass is better?” novel approaches to assess passing effectiveness in elite soccer. Hum Movem Sci, 55 (2017), pp. 172-181
J. Gudmundsson, T. Wolle. Football analysis using spatio-temporal tools. Comput Environ Urban Syst, 47 (2014), pp. 16-27
P. Lucey, A. Bialkowski, P. Carr, S. Morgan, I. Matthews, Y. Sheikh. Representing and discovering adversarial team behaviors using player roles. Proceedings of the 2013 IEEE conference on computer vision and pattern recognition (CVPR) (2013), pp. 2706-2713
J. Peña, H. Touchette. A network theory analysis of football strategies (2012)
Wei X., Sha L., P. Lucey, S. Morgan, S. Sridharan. Large-Scale analysis of formations in soccer. Proceedings of the 2013 international conference on digital image computing: techniques and applications (DICTA) (2013), pp. 1-8
P. Lucey, D. Oliver, P. Carr, J. Roth, I. Matthews. Assessing team strategy using spatiotemporal data. Proceedings of the nineteenth ACM SIGKDD international conference on knowledge discovery and data mining (2013), pp. 1366-1374
A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan, I. Matthews. Identifying team style in soccer using formations learned from spatiotemporal tracking data. Proceedings of the 2014 IEEE international conference on data mining workshop (2014), pp. 9-14
R. Milo, S. Itzkovitz, N. Kashtan, D.M.B. Chklovskii. Network motifs : simple building blocks of complex networks
Science, 298 (5594) (2002), pp. 824-827
P. Lucey, A. Bialkowski, M. Monfort, P. Carr, I. Matthews. Quality vs quantity: improved shot prediction in soccer using strategic features from spatiotemporal data. The venue is MIT SLOAN Sports Analytics Conference
(2015). http://www.sloansportsconference.com/wp-content/uploads/2015/02/SSAC15-RP-Finalist-Quality-vs-Quantity.pdf
A. Bialkowski, P. Lucey, P. Carr, Yue Y., S. Sridharan, I. Matthews. Large-scale analysis of soccer matches using spatiotemporal tracking data. Proceedings of the 2014 IEEE international conference on data mining (2014), pp. 725-730
L. Gyarmati, H. Kwak, P. Rodriguez. Searching for a unique style in soccer. Soc Inf Netw (2014)
M. Stein, T. Breitkreutz, J. Haussler, D. Seebacher, C. Niederberger, T. Schreck, et al. Revealing the invisible: visual analytics and explanatory storytelling for advanced team sport analysis. Proceedings of the 2018 international symposium on big data visual and immersive analytics (2018), pp. 1-9
C. Perin, R. Vuillemot, C.D. Stolper, J.T. Stasko, J. Wood, S. Carpendale. State of the art of sports data visualization
Comput Graph Forum, 37 (3) (2018), pp. 663-686
WhoScored. Man. city vs leicester. https://www.whoscored.com/Matches/1285050/Live/, 2019. Footscope. Footoscope: Fifa world cup south africa. http://www.footoscope.com/worldcup2010/, 2010.
A. Rusu, D. Stoica, E. Burns, B. Hample, K. McGarry, R. Russell. Dynamic visualizations for soccer statistical analysis. Proceedings of the 2010 fourteenth international conference information visualisation (IV) (2010), pp. 207-212
P. Legg, Chung D., M. Parry, M. Jones, Long R., I. Griffiths, et al. Matchpad: interactive glyph-based visualization for real-time sports performance analysis. Comput Graph Forum, 31 (3pt4) (2012), pp. 1255-1264
K. Goldsberry. Courtvision: new visual and spatial analytics for the NBA. Proceedings of the MIT sloan sports analytics conference (2012)
H. Pileggi, C.D. Stolper, J.M. Boyle, J.T. Stasko. Snapshot: visualization to propel ice hockey analytics. IEEE Trans Vis Comput Graph, 18 (12) (2012), pp. 2819-2828
P.A. Legg, Chung D.H.S., M.L. Parry, R. Bown, M.W. Jones, I.W. Griffiths, et al. Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop. IEEE Trans Vis Comput Graph, 19 (12) (2013), pp. 2109-2118
C. Perin, R. Vuillemot, J.D. Fekete. Soccerstories: a kick-off for visual soccer analysis. IEEE Trans Vis Comput Graph, 19 (12) (2013), pp. 2506-2515
H. Janetzko, D. Sacha, M. Stein, T. Schreck, D. Keim, O. Deussen. Feature-driven visual analytics of soccer data
Proceedings of the 2014 IEEE conference on visual analytics science and technology (VAST) (2014), pp. 13-22
Rosenthal S.. Football drawings. http://www.susken-rosenthal.de/fussballbilder/, 2019.
M. Stein, D. Sacha. Enhancing parallel coordinates : statistical visualizations for analyzing soccer data. Electron Imag (2016), pp. 1-8
M. Stein, H. Janetzko, A. Lamprecht, T. Breitkreutz, P. Zimmermann, B. Goldlucke, et al. Bring it to the pitch: combining video and movement data to enhance team sport analysis. IEEE Trans Vis Comput Graph, 24 (01) (2018), pp. 13-22
V. Machado, R. Leite, F. Moura, S. Cunha, F. Sadlo, J.L. Comba. Visual soccer match analysis using spatiotemporal positions of players. Comput Graph, 68 (2017), pp. 84-95
Wu Y., Xie X., Wang J., Deng D., Liang H., Zhang H., et al. Forvizor: visualizing spatio-temporal team formations in soccer. IEEE Trans Vis Comput Graph, 25 (1) (2019), pp. 65-75. Sentio Sports feed. https://sentiosports.com/, 2019.
D. Sumpter. Soccermatics: Mathematical adventures in the beautiful game. Bloomsbury Sigma (2016)
J.O. Wobbrock, A.D. Wilson, Li Y. Gestures without libraries, toolkits or training: a 1 recognizer for user interface prototypes. Proceedings of the twentieth annual ACM symposium on user interface software and technology UIST 07, 85 (2007), p. 159
Li Y. Protractor: a fast and accurate gesture recognizer. Proceedings of the twenty-eighth international conference on human factors in computing systems (2010), pp. 2169-2172
T.M. Kodinariya, P.R. Makwana. Review on determining number of cluster in K-means clustering. Int J Adv Res Comput Sci Manag Stud, 1 (6) (2013), pp. 2321-7782
D. Holten, J.J. van Wijk. Force-directed edge bundling for graph visualization. Proceedings of the eleventh Eurographics/IEEE – VGTC conference on visualization EuroVis’09, Chichester, UK (2009), pp. 983-998
Publicado
28/10/2019
Como Citar
COMBA, João Luiz Dihl ; GARCIA, Rafael; MALQUI, Jose Luis Sotomayor ; ALEMDAR, Hande; ROMERO, Noemí Maritza Lapa .
How do Soccer Teams Coordinate Consecutive Passes? A Visual Analytics System for Analysing the Complexity of Passing Sequences Using Soccer Flow Motifs.. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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DOI: https://doi.org/10.5753/sibgrapi.2019.9820.
