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Basketball Analytics Using Spatial Tracking Data

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New Statistical Developments in Data Science (SIS 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 288))

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Abstract

Spatial tracking data are used in sport analytics to study the players’ position during the game in order to evaluate game strategies, players’ roles, performance, also in prospect. From the broad fields of statistics, mathematics, information science and computer science it is possible to draw theories and methods useful to produce innovative results based on speed, distance, players’ separation trajectories. In basketball, spatial tracking data can be combined with play-by-play data, joining results on spatial movements to team performance. In this paper, using tracking data from basketball, we study the spatial pattern of players on the court in order to contribute to the literature of data mining methods for tracking data analysis in sports, with the final objective of suggesting new game strategies to improve team performance.

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References

  1. Bradley, P., O’Donoghue, P., Wooster, B., Tordoff, P.: The reliability of ProZone MatchViewer: a video-based technical performance analysis system. Int. J. Perform. Anal. Sport 7(3), 117–129 (2007)

    Article  Google Scholar 

  2. Bolt, M.D.: Visualizing water quality sampling-events in Florida. ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci. 2(4), 73 (2015)

    Article  Google Scholar 

  3. Brillinger, D.R.: A potential function approach to the flow of play in soccer. J. Quant. Anal. Sport. 3(1), 3 (2007)

    MathSciNet  Google Scholar 

  4. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P.: Football mining with R. Data Min. Appl. R (2013)

    Google Scholar 

  5. Carpita, M., Sandri, M., Simonetto, A., Zuccolotto, P.: Discovering the drivers of football match outcomes with data mining. Qual. Technol. Quant. Manag. 12(4), 561–577 (2015)

    Article  Google Scholar 

  6. Catapult USA Sports Ltd. - Wearable Technology for Elite Sports (2015). http://www.catapultsports.com/

  7. Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., Malvaldi, M.: The harsh rule of the goals: data-driven performance indicators for football teams. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA) 36678, pp. 1–10 (2015)

    Google Scholar 

  8. Cintia, P., Rinzivillo, S., Pappalardo, L.: A network-based approach to evaluate the performance of football teams. In: Machine Learning and Data Mining for Sports Analytics Workshop, Porto, Portugal (2015)

    Google Scholar 

  9. Couceiro, M.S., Clemente, F.M., Martins, F.M., Machado, J.A.T.: Dynamical stability and predictability of football players: the study of one match. Entropy 16(2), 645–674 (2014)

    Article  MathSciNet  Google Scholar 

  10. Fonseca, S., Milho, J., Travassos, B., Araujo, D.: Spatial dynamics of team sports exposed by voronoi diagrams. Hum. Mov. Sci. 31(6), 1652–1659 (2012)

    Article  Google Scholar 

  11. Gesmann, M., de Castillo, D.: Package ‘googleVis’. Interface between R and the Google chart tools (2013)

    Google Scholar 

  12. Goldsberry, K.: Courtvision: new visual and spatial analytics for the NBA. In: 2012 MIT Sloan Sports Analytics Conference (2012)

    Google Scholar 

  13. Gudmundsson, J., Horton, M.: Spatio-temporal analysis of team sports. ACM Comput. Surv. (CSUR) 50(2), 22 (2017)

    Article  Google Scholar 

  14. Heinz, S.: Practical application of motion charts in insurance (2014)

    Google Scholar 

  15. Hilpert, M.: Dynamic visualizations of language change. Int. J. Corpus Linguist. 16(4), 435–461 (2011)

    Article  MathSciNet  Google Scholar 

  16. Kim, J.Y., Kim, T.Y.: Soccer ball tracking using dynamic Kalman filter with velocity control. In: Sixth International Conference on Computer Graphics, Imaging and Visualization, CGIV’09, pp. 367–374. IEEE (2009)

    Google Scholar 

  17. Impire, A.G.: (2015). http://www.bundesliga-datenbank.de/en/products/

  18. Metulini, R.: Spatio-temporal movements in team sports: a visualization approach using motion charts. Electron. J. Appl. Stat. Anal. 10(3), 809–831 (2017)

    Google Scholar 

  19. Metulini, R.: Filtering procedures for sensor data in basketball. Stat. Appl. 15(2), 133–150 (2017)

    Google Scholar 

  20. Metulini, R., Manisera, M., Zuccolotto, P.: Space-time analysis of movements in basketball using sensor data. In: Statistics and Data Science: New Challenges, New Generations SIS2017 Proceeding. Firenze Uiversity Press. e-ISBN: 978-88-6453-521-0 (2017)

    Google Scholar 

  21. Metulini, R., Manisera, M., Zuccolotto, P.: Sensor analytics in basketball. In: Proceedings of the 6th International Conference on Mathematics in Sport. ISBN 978-88-6938-058-7 (2017)

    Google Scholar 

  22. Metulini, R., Manisera, M., Zuccolotto, P.: Modelling the dynamic pattern of surface area in basketball and its effects on team performance. J. Quant. Anal. Sport 14(3), 117–130 (2018)

    Article  Google Scholar 

  23. Moura, F.A., Martins, L.E.B., Anido, R.D.O., De Barros, R.M.L., Cunha, S.A.: Quantitative analysis of Brazilian football players’ organisation on the pitch. Sports Biomech. 11(1), 85–96 (2012)

    Article  Google Scholar 

  24. Passos, P., Davids, K., Araujo, D., Paz, N., Minguens, J., Mendes, J.: Networks as a novel tool for studying team ball sports as complex social systems. J. Sci. Med. Sport 14(2), 170–176 (2011)

    Article  Google Scholar 

  25. Passos, P., Araujo, D., Volossovitch, A.: Performance Analysis in Team Sports. Routledge, London (2016)

    Book  Google Scholar 

  26. Perin, C., Vuillemot, R., Fekete, J.D.: SoccerStories: a kick-off for visual soccer analysis. IEEE Trans. Vis. Comput. Graph. 19(12), 2506–2515 (2013)

    Article  Google Scholar 

  27. Pileggi, H., Stolper, C.D., Boyle, J.M., Stasko, J.T.: Snapshot: visualization to propel ice hockey analytics. IEEE Trans. Vis. Comput. Graph. 18(12), 2819–2828 (2012)

    Article  Google Scholar 

  28. Polk, T., Yang, J., Hu, Y., Zhao, Y.: Tennivis: visualization for tennis match analysis. IEEETrans. Vis. Comput. Graph. 20(12), 2339–2348 (2014)

    Article  Google Scholar 

  29. Sacha, D., Stein, M., Schreck, T., Keim, D.A., Deussen, O.: Feature-driven visual analytics of soccer data. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 13–22 (2014)

    Google Scholar 

  30. Saka, C., Jimichi, M.: Inequality evidence from accounting data visualisation (2015)

    Google Scholar 

  31. Santori, G.: Application of interactive motion charts for displaying liver transplantation data in public websites. Transplant. Proc. 46(7), 2283–2286 (2014)

    Article  Google Scholar 

  32. Santos, J.L., Govaerts, S., Verbert, K., Duval, E.: Goal-oriented visualizations of activity tracking: a case study with engineering students. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 143–152. ACM (2012)

    Google Scholar 

  33. Theron, R., Casares, L.: Visual analysis of time-motion in basketball games. In: International Symposium on Smart Graphics, pp. 196–207. Springer, Berlin Heidelberg (2010)

    Chapter  Google Scholar 

  34. Tracab Corporation. Player Tracking System (2015). http://chyronhego.com/sports-data/player-tracking

  35. Travassos, B., Araujo, D., Duarte, R., McGarry, T.: Spatiotemporal coordination behaviors in futsal (indoor football) are guided by informational game constraints. Hum. Mov. Sci. 31(4), 932–945 (2012)

    Article  Google Scholar 

  36. Travassos, B., Davids, K., Araujo, D., Esteves, P.T.: Performance analysis in team sports: advances from an ecological dynamics approach. Int. J. Perform. Anal. Sport 13(1), 83–95 (2013)

    Article  Google Scholar 

  37. Turvey, M.T., Shaw, R.E.: Toward an ecological physics and a physical psychology. The Science of the Mind: 2001 and Beyond, pp. 144–169 (1995)

    Google Scholar 

  38. Wasserman, S., Katherine, F.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)

    Book  Google Scholar 

  39. Wu, S., Bornn, L.: Modeling offensive player movement in professional basketball. Am. Stat. 72(1), 72–79 (2018)

    Article  Google Scholar 

  40. Zuccolotto, P., Manisera, M., Sandri, M.: Big data analytics for modeling scoring probability in basketball: the effect of shooting under high-pressure conditions. Int. J. Sport Sci. Coach. 13(4), 569–589 (2017)

    Article  Google Scholar 

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Acknowledgements

Research carried out in collaboration with the Big & Open Data Innovation Laboratory (BODaI-Lab), University of Brescia (project nr. 03-2016, title: “Big Data Analytics in Sports”, www.bodai.unibs.it/bdsports/), granted by Fondazione Cariplo and Regione Lombardia. Authors would like to thank MYagonism (https://www.myagonism.com/) for having provided the data.

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Authors and Affiliations

  1. Department of Economics and Management, University of Brescia, C.da S. Chiara, 50, 25125, Brescia, Italy

    Marica Manisera, Rodolfo Metulini & Paola Zuccolotto

Authors
  1. Marica Manisera
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  2. Rodolfo Metulini
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  3. Paola Zuccolotto
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Corresponding author

Correspondence to Rodolfo Metulini .

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Editors and Affiliations

  1. Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’ (DiSIA), Università degli Studi di Firenze, Florence, Italy

    Alessandra Petrucci

  2. Dipartimento Scienze Statistiche, Sapienza Università di Roma, Rome, Italy

    Filomena Racioppi

  3. Dipartimento di Matematica e Fisica, Università della Campania ‘Luigi Vanvitelli’, Caserta, Italy

    Rosanna Verde

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Manisera, M., Metulini, R., Zuccolotto, P. (2019). Basketball Analytics Using Spatial Tracking Data. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_23

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