Spatio-Temporal Movements in Team Sports: A Visualization approach using Motion Charts


To analyze the movements and to study the trajectories of players is a crucial need for a team when it looks to improve its chances of winning a match or to understand its performances. State of the art tracking systems now produce spatio-temporal traces of player trajectories with high definition and frequency that has facilitated a variety of research efforts to extract insight from the trajectories. Despite many methods borrowed from different disciplines (machine learning, network and complex systems, GIS, computer vision, statistics) has been proposed to answer to the needs of teams, a friendly and easy-to-use approach to visualize spatio-temporal movements is still missing. This paper suggests the use of gvisMotionChart function in googleVis R package. I present and discuss results of a basketball case study. Data refers to a match played by an italian team militant in "C-gold" league on March 22nd, 2016. With this case study I show that such a visualization approach could be useful in supporting researcher on preliminar stages of their analysis on sports' movements, and to facilitate the interpretation of results.

Keywords: Spatio-Temporal Movements; Trajectories; Sports Statistics; Motion Charts; GoogleVis.


Arbia, G., Espa, G. and Quah, D. (2008), `A class of spatial econometric methods in the empirical analysis of clusters of firms in the space', Empirical Economics 34(1), 81-103.

Bolt, M. (2015), `Visualizing water quality sampling-events in florida', ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2(4), 73.

Bradley, P., O'Donoghue, P., Wooster, B. and Tordo, P. (2007), `The reliability of prozone matchviewer: a video-based technical performance analysis system', International Journal of Performance Analysis in Sport 7(3), 117{129.

Brillinger, D. R. (2007), `A potential function approach to the

ow of play in soccer', Journal of Quantitative Analysis in Sports 3(1).

Brillinger, D. R. (2008), `Modelling spatial trajectories', pp. 463-475.

Brillinger, D. R., Preisler, H. K., Ager, A. A. and Kie, J. G. (2004),`An exploratory data analysis (eda) of the paths of moving animals', Journal of statistical planning and inference 122(1), 43-63.

Calenge, C., Dray, S. and Royer-Carenzi, M. (2009), `The concept of animals' trajectories from a data analysis perspective', Ecological informatics 4(1), 34-41.

Calenge, C. et al. (2007), `Exploring habitat selection by wildlife with adehabitat', Journal of Statistical Software 22(6), 2-19.

Carpita, M., Sandri, M., Simonetto, A. and Zuccolotto, P. (2013), `Football mining with r', Data Mining Applications with R .

Carpita, M., Sandri, M., Simonetto, A. and Zuccolotto, P. (2015), `Discovering the drivers of football match outcomes with data mining', Quality Technology & Quantitative Management 12(4), 561-577.

Cervone, D., DAmour, A., Bornn, L. and Goldsberry, K. (2016), `A multiresolution stochastic process model for predicting basketball possession outcomes', Journal of the American Statistical Association (just-accepted), 1-45.

Couceiro, M. S., Clemente, F. M., Martins, F. M. and Machado, J. A. T. (2014), `Dynamical stability and predictability of football players: the study of one match', Entropy 16(2), 645-674.

Fonseca, S., Milho, J., Travassos, B. and Araujo, D. (2012), `Spatial dynamics of team sports exposed by voronoi diagrams', Human movement science 31(6), 1652-1659.

Fortune, S. (1987), `A sweepline algorithm for voronoi diagrams', Algorithmica 2(14), 153-174.

Gesmann, M., de Castillo, D. and Gesmann, M. M. (2013), `Package googlevis', Interface between R and the Google Chart Tools .

Heinz, S. (2014), `Practical application of motion charts in insurance', Available at SSRN 2459263 .

Hilpert, M. (2011), `Dynamic visualizations of language change: Motion charts on the basis of bivariate and multivariate data from diachronic corpora', International Journal of Corpus Linguistics 16(4), 435-461.

Kim, K., Grundmann, M., Shamir, A., Matthews, I., Hodgins, J. and Essa, I. (2010), Motion elds to predict play evolution in dynamic sport scenes, in `Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on', IEEE, pp. 840-847.

Moura, F. A., Martins, L. E. B., Anido, R. D. O., De Barros, R. M. L. and Cunha, S. A. (2012), `Quantitative analysis of brazilian football players' organisation on the pitch', Sports Biomechanics 11(1), 85-96.

Passos, P., Davids, K., Araujo, D., Paz, N., Minguens, J. and Mendes, J. (2011), `Networks as a novel tool for studying team ball sports as complex social systems', Journal

of Science and Medicine in Sport 14(2), 170-176.

Perin, C., Vuillemot, R. and Fekete, J.-D. (2013), `Soccerstories: A kick-o for visual soccer analysis', IEEE transactions on visualization and computer graphics 19(12), 2506-2515.

Saka, C. and Jimichi, M. (2015), `Inequality evidence from accounting data visualisation', Available at SSRN 2549400 .

Santori, G. (2014), Application of interactive motion charts for displaying liver transplantation data in public websites, in `Transplantation proceedings', Vol. 46, Elsevier, pp. 2283-2286.

Santos, J. L., Govaerts, S., Verbert, K. and Duval, E. (2012), Goal-oriented visualizations of activity tracking: a case study with engineering students, in `Proceedings of the 2nd

international conference on learning analytics and knowledge', ACM, pp. 143-152.

Schwager, M., Anderson, D. M., Butler, Z. and Rus, D. (2007),

`Robust classication of animal tracking data', Computers and Electronics in Agriculture 56(1), 46-59.

Taki, T., Hasegawa, J.-i. and Fukumura, T. (1996), Development of motion analysis system for quantitative evaluation of teamwork in soccer games, in `Image Processing, 1996. Proceedings., International Conference on', Vol. 3, IEEE, pp. 815-818.

Travassos, B., Araujo, D., Duarte, R. and McGarry, T. (2012), `Spatiotemporal coordination behaviors in futsal (indoor football) are guided by informational game constraints', Human Movement Science 31(4), 932-945.

Wasserman, S. and Faust, K. (1994), Social network analysis: Methods and applications, Vol. 8, Cambridge university press.

Wei, X., Lucey, P., Vidas, S., Morgan, S. and Sridharan, S. (2014), Forecasting events using an augmented hidden conditional random field, in `Asian Conference on Computer

Vision', Springer, pp. 569-582.

Yue, Y., Lucey, P., Carr, P., Bialkowski, A. and Matthews, I. (2014), Learning fine grained spatial models for dynamic sports play prediction, in `2014 IEEE International

Conference on Data Mining', IEEE, pp. 670-679.

Full Text: pdf

Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.