Metulini, R., Manisera, M., Zuccolotto, P. (2017), Space-Time Analysis of Movements in Basketball using Sensor Data, "Statistics and Data Science: new challenges, new generations" SIS2017 proceeding. Firenze Uiversity Press. e- ISBN: 978-88-6453-521-0
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The document presents an analysis of basketball player movements using sensor data to investigate the relationship between player trajectories, tactical behavior, and team performance. The study employs data-driven approaches including cluster analysis and multidimensional scaling to identify patterns in player movements during a game. Future research aims to refine these analyses by expanding datasets and applying advanced statistical methods to better understand the impact on team dynamics.
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Space-Time Analysis of Movements in Basketball using Sensor Data, "Statistics and Data Science: new challenges, new generations" SIS2017 proceeding. Firenze Uiversity Press. e- ISBN: 978-88-6453-521-0
- 1. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Space-Time Analysis of Movements in Basketball using Sensor Data Rodolfo Metulini, Marica Manisera & Paola Zuccolotto University of Brescia - Department of Economics and Management Florence - June 29th, 2017
- 2. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Table of contents 1 Aims & Methodological background 2 Data 3 Analysis & Results 4 Future developments 5 References
- 3. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Aims Experts want to explain why and how cooperative players’ movements are expressed because of tactical behaviour Analysts want to explain movements in reaction to a variety of factors and in relation to team performance • Aim: To find any regularities and synchronizations in players’ trajectories, by decomposing the game into homogeneous phases in terms of spatial relations • Future aims: to study cooperative players’ movements in relations to team performance
- 4. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References We use a data-driven approach • Carpita et al. (2013,2015) used cluster analysis and Principal Component Analysis (PCA) to identify the drivers that most affect the probability to win a football match • From network perspective, Wasserman & Faust (1994) analysed passing network. Passos et al. (2011) used centrality measures to identify ‘key” players in water polo
- 5. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References ... making use of visualization tools Sports data tends to be hypervariate, temporal, relational, hierarchical, or a combination thereof, which leads to some fascinating visualization challenges - Basole & Saupe (2016) • Aisch & Quealy (2016) A&Q and Goldsberry (2013) Gds use visualization to tell basketball and football stories on newspapers • Notable academic works include data visualization, among others, in ice hockey (Pileggi et al. 2012), tennis (Polk et al. 2014) and basketball (Losada et al. 2016, Metulini 2016)
- 6. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References ... with a synchronyzed movements perspective Living things and their surrounds are not logically independent of each other. Together, they constitute a unitary planetary system - Tuvey et al. (1995) • Borrowing from the concept of Physical Psychology, Travassos et al. (2013) and Araujo & Davids (2016) expressed players in the court as living things who face with external factors
- 7. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Global Positioning Systems (GPS) • Object trajectories capture the movement of players or the ball • Trajectories are captured using optical- or device-tracking and processing systems • The adoption of this technology and the availability of data is driven by various factors, particularly commercial and technical
- 8. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Play-by-play Play-by-play (or ‘event-log”) reports a sequence of relevant events that occur during a match • Players’ events (shots, fouls) • Technical events (time-outs, start/end of the period) • Large amounts of available data • Web scraping techniques (user-friendly R and Phyton routines)
- 9. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Our dataset • Data refers to a friendly match played on March 22th, 2016 by a team based in the city of Pavia. Data provided by MYagonism MYa • Six players worn a microchip, collecting the position in the x-axis, y-axis, and z-axis • Players’ positioning has been detected at millisecond level, and the dataset records a total of 133,662 observations • After some cleaning and dataset reshaping, the final dataset counts for more than 3 million records
- 10. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References To begin with: data visualization Metulini, 2016 ... 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 ... Motion Chart Tutorial from youtube channel bdsport unibs
- 11. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Computing convex hulls - i
- 12. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Computing convex hulls - ii
- 13. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Hypotesis to test in future analysis Players, in defence, aim to narrow the opponents’ spacings in order to limit the offensive team. Conversely, the aim of the offensive team is to maintain large distances among team-mates, to increase the propensity to shot with good scoring percentages
- 14. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Cluster Analysis & Multidimensional Scaling A method to approach with complexity in team sport analysis consists on segmenting a match into phases (Perin et al. 2013) • To better characterize the synchronized movement of players around the court • To find, through a cluster analysis, a number of homogeneous groups each identifying a specific spatial pattern
- 15. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Analysis 1 We characterize each cluster in terms of players’ position in the court 2 We define whether each cluster corresponds to offensive or defensive actions 3 We compute the transition matrices in order to examine the probability of switching to another group from time t to time t + 1
- 16. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Tools • We apply a k-means cluster analysis to group objects • We group time instants • We choose k = 8 based on the value of the between deviance (BD) / total deviance (TD) ratio for different numbers of clusters • A multidimensional scaling is used to plot each player in a 2-dimensional space such that the between-player average distances are preserved
- 17. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Profiles plot Profile plots representing, for each of the 8 clusters, the average distance among each pair of players
- 18. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Multidimensional scaling Map representing, for each of the 8 clusters, the average position in the x, y axes of the five players
- 19. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Transition matrix Transition matrix reporting the relative frequency subsequent moments (t, t + 1) report a switch from a group to a different one
- 20. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Future research • To increase the complexity, by extending the analysis to multiple matches. We have trajectories data for the Final Eight competition • To extract insights on the relations between particular spatial patterns and the team performance, matching play-by-play with trajectories. • Using convex hulls ... 1. to verify the hypothesis at slide 12 using vector autoregressive (VAR) 2. to find regimes, using Regime Switching and Autoregressive Conditional Duration (ACD) models.
- 21. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References Acknowledgements Big & Open Data Innovation (BODaI) laboratory BODaI Big Data analytics in Sports (BDS) laboratory BDS Paolo Raineri (CEO and cofounder MYagonism MYa ) Tullio Facchinetti (MYagonism, Robotics Laboratory RoLa , University of Pavia) Raffaele Imbrogno (Professor of Sports Games at Foro Italico University, Rome & Staff - Federazione Italiana Pallacanestro (FIP))
- 22. Analysis of Movements in Basketball Metulini Manisera Zuccolotto Aims & Methodologi- cal background Data Analysis & Results Future developments References References 1. Aisch, G., Quealy, K. (2016). Stephen curry 3-point record in context: Off the charts. New York Times. 2. Araujo, D., Davids, K. (2016). Team synergies in sport: Theory and measures. Frontiers in Psychology, 7. 3. Basole, R. C., Saupe, D. (2016). Sports data visualization (guest editors’ introduction). IEEE Computer Graphics and Applications 4. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2013). Football mining with r. Data Mining Applications with R. 5. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P. (2015). Discovering the drivers of football match outcomes with data mining. Quality Technology & Quantitative Management 6. Goldsberry, K. (2013). Pass atlas: A map of where NFL quarterbacks throw the ball. Grantland. 7. Losada, A. G., Theron, R., Benito, A. (2016). Bkviz: A basketball visual analysis tool. IEEE Computer Graphics and Applications, 8. Metulini, R. (2016). Spatio-Temporal Movements in Team Sports: A Visualization approach using Motion Charts. Electronic Journal of Applied Statistical Analysis (EJASA, forthcoming) 9. Passos, P., Davids, K., Araujo, D., Paz, N., Minguens, J., Mendes, J. (2011). Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport 10. Perin, C., Vuillemot, R., Fekete, JD. (2013). Soccerstories: A kick-off for visual soccer analysis. IEEE transactions on visualization and computer graphics 11. Pileggi, H., Stolper, C. D., Boyle, J. M., Stasko, J. T. (2012). Snapshot: Visualization to propel ice hockey analytics. IEEE Transactions on Visualization and Computer Graphics 12. Polk, T., Yang, J., Hu, Y., Zhao, Y. (2014). Tennivis: Visualization for tennis match analysis. IEEE transactions on visualization and computer graphics 13. Travassos, B., Araujo, D., Duarte, R., McGarry, T. (2012). Spatiotemporal coordination behaviors in futsal (indoor football) are guided by informational game constraints. Human Movement Science 14. Turvey, M., Shaw, R. E., Solso, R., Massaro, D. (1995). Toward an ecological physics and a physical psychology. The science of the mind: 2001 and beyond. Electronic Journal of Applied Statistical Analysis 15. Wasserman, S., Faust, K. (1994). Social network analysis: Methods and applications, volume 8. Cambridge university press.