Sensor Analytics in Basketball Metulini Manisera Zuccolotto State of the art Aims, data & Sensor Analytics in Basketball methods Results Conclusions & future Rodolfo Metulini , Marica Manisera & Paola Zuccolotto developments References University of Brescia - Department of Economics and Management Padua - June 27th, 2017
Sensor Analytics in Table of contents Basketball Metulini Manisera Zuccolotto State of the art Aims, data & 1 State of the art methods Results Conclusions & 2 Aims, data & methods future developments References 3 Results 4 Conclusions & future developments 5 References
Sensor Analytics in Data-driven techniques Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References • 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 a network perspective, Wasserman & Faust (1994) analysed passing networks. Passos et al. (2011) used centrality measures to identify ‘key” players in water polo
Sensor Analytics in Synchronyzed movements analysis Basketball Metulini Manisera Zuccolotto State of the Living things and their surrounds are not logically independent of art each other. Together, they constitute a unitary planetary system - Aims, data & Tuvey et al. (1995) methods Results • Borrowing from the concept of Physical Psychology, Travassos Conclusions & et al. (2013) and Araujo & Davids (2016) expressed players in future developments the court as living things who face with external factors References • Perin et al. (2013) developed a system for visual exploration of phases in football
Sensor Analytics in Visualization tools Basketball Metulini Manisera Zuccolotto State of the Sports data tends to be hypervariate, temporal, relational, hierarchical, or art a combination thereof, which leads to some fascinating visualization Aims, data & challenges - Basole & Saupe (2016) methods Results Conclusions & future developments References A&Q and Goldsberry (2013) Gds use • Aisch & Quealy (2016) 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 Motion charts tutorial ) basketball ( Losada et al. , 2016, Metulini , 2016,
Sensor Analytics in Aims and scope Basketball Metulini Manisera Zuccolotto State of the art Experts want to explain why and how cooperative players’ Aims, data & movements are expressed because of tactical behaviour methods Analysts want to explain movements in reaction to a variety of Results factors and in relation to team performance Conclusions & future developments References • 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
Sensor Analytics in Global Positioning Systems (GPS) Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References • 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
Sensor Analytics in Play-by-play Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References 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)
Sensor Analytics in Our data Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments • Data refers to a friendly match played on March 22th, 2016 by References 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
Sensor Analytics in Data Visualization Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References 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
Sensor Analytics in Convex hulls & Average distances Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods • Motion charts applied to our data show differences in the Results spacing structure of players among offensive and defensive plays. Conclusions & • To corroborate this evidence, we compute average distances future developments among players and Convex Hulls. References • A motivation: players in defence have the objective to narrow the opponents’ spacings in order to limit their play, while the aim of the offensive team is to maintain large distances among team-mates, to increase the propensity to shot with good scoring percentages.
Sensor Analytics in Convex hull - offensive plays Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References
Sensor Analytics in Convex hull - defensive plays Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References
Sensor Analytics in Summary statistics Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Table: Statistics for offensive and defensive plays Results Conclusions & Average distance Convex hull area future attack defence attack defence developments Min 5.418 2.709 11.000 4.500 References 1st Qu. 7.689 3.942 32.000 12.500 Median 8.745 4.696 56.000 18.500 Mean 8.426 5.548 52.460 32.660 3rd Qu. 9.455 5.611 68.500 27.500 Max 10.260 11.640 99.500 133.500
Sensor Analytics in Cluster Analysis & Multidimensional Scaling Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments A method to approach with complexity in team sport analysis References 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
Sensor Analytics in Analyses Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References • First, we characterize each cluster in terms of players’ position in the court • We define whether each cluster corresponds to offensive or defensive actions • We compute the transition matrices in order to examine the probability of switching to another group from time t to time t + 1
Sensor Analytics in Methods Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments • We apply a k -means cluster analysis to group objects References • 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
Sensor Analytics in Define k Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References Plot of the between deviance (BD) / total deviance (TD) ratio for different number of clusters
Sensor Analytics in Profiles plot Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References Profile plots representing, for each of the 8 clusters, the average distance among each pair of players
Sensor Analytics in Multidimensional scaling Basketball Metulini Manisera Zuccolotto State of the art Aims, data & methods Results Conclusions & future developments References Map representing, for each of the 8 clusters, the average position in the x , y axes of the five players
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