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Spatial Performance Indicators and Graphs in Basketball

  • Paola ZuccolottoEmail author
  • Marco Sandri
  • Marica Manisera
Original Research
  • 24 Downloads

Abstract

Assessing the scoring probability of teams and players in different areas of a court map is an important topic in basketball analytics, in order to define both game strategies and training programmes. In this contribution we propose a spatial statistical method based on classification trees, aimed to define a partition of the court in rectangles with maximally different shooting performances. Each analyzed team/player is characterized by its/his own partition, so comparisons can be made among different teams/players. In addition, shooting efficiency measures computed within the rectangles can be used to define spatial shooting performance indicators.

Keywords

Basketball analytics Sports statistics Decision trees Performance analysis 

Notes

Acknowledgements

We thank the anonymous reviewers for stimulating suggestions.

References

  1. Alagappan, M. (2012). From 5 to 13: Redefining the positions in basketball. In 2012 MIT Sloan sports analytics conference. http://www.sloansportsconference.com
  2. Ante, P., Slavko, T., & Igor, J. (2014). Interdependencies between defence and offence in basketball. Sport Science, 7(2), 62–66.Google Scholar
  3. Araújo, D., & Davids, K. (2016). Team synergies in sport: Theory and measures. Frontiers in Psychology, 7, 1449.CrossRefGoogle Scholar
  4. Araújo, D., Davids, K., & Hristovski, R. (2006). The ecological dynamics of decision making in sport. Psychology of Sport and Exercise, 7(6), 653–676.CrossRefGoogle Scholar
  5. Araújo, D., Davids, K. W., Chow, J. Y., Passos, P., & Raab, M. (2009). The development of decision making skill in sport: An ecological dynamics perspective. In Perspectives on cognition and action in sport (pp. 157–169). Nova Science Publishers, Inc.Google Scholar
  6. Avugos, S., Köppen, J., Czienskowski, U., Raab, M., & Bar-Eli, M. (2013). The “hot hand” reconsidered: A meta-analytic approach. Psychology of Sport and Exercise, 14(1), 21–27.CrossRefGoogle Scholar
  7. Bianchi, F., Facchinetti, T., & Zuccolotto, P. (2017). Role revolution: Towards a new meaning of positions in basketball. Electronic Journal of Applied Statistical Analysis, 10(3), 712–734.Google Scholar
  8. Bornn, L., Cervone, D., Franks, A., & Miller, A. (2017). Studying basketball through the lens of player tracking data. In Handbook of statistical methods and analyses in sports (pp. 245–269). Chapman and Hall/CRC.Google Scholar
  9. Breiman, L., Friedman, J. H., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.Google Scholar
  10. Cervone, D., D’Amour, A., Bornn, L., & Goldsberry, K. (2016). A multiresolution stochastic process model for predicting basketball possession outcomes. Journal of the American Statistical Association, 111(514), 585–599.CrossRefGoogle Scholar
  11. Clemente, F. M., Martins, F. M. L., Kalamaras, D., & Mendes, R. S. (2015). Network analysis in basketball: Inspecting the prominent players using centrality metrics. Journal of Physical Education and Sport, 15(2), 212.Google Scholar
  12. Deshpande, S. K., & Jensen, S. T. (2016). Estimating an NBA player’s impact on his team’s chances of winning. Journal of Quantitative Analysis in Sports, 12(2), 51–72.CrossRefGoogle Scholar
  13. Duarte, R., Araújo, D., Correia, V., Davids, K., Marques, P., & Richardson, M. J. (2013). Competing together: Assessing the dynamics of team-team and player-team synchrony in professional association football. Human Movement Science, 32(4), 555–566.CrossRefGoogle Scholar
  14. Engelmann, J. (2017). Possession-based player performance analysis in basketball (adjusted +/- and related concepts). In Handbook of statistical methods and analyses in sports (pp. 215–227). Chapman and Hall/CRC.Google Scholar
  15. Erčulj, F., & Štrumbelj, E. (2015). Basketball shot types and shot success in different levels of competitive basketball. PLoS ONE, 10(6), e0128885.CrossRefGoogle Scholar
  16. Fearnhead, P., & Taylor, B. M. (2011). On estimating the ability of NBA players. Journal of Quantitative Analysis in Sports.  https://doi.org/10.2202/1559-0410.1298.CrossRefGoogle Scholar
  17. Fewell, J. H., Armbruster, D., Ingraham, J., Petersen, A., & Waters, J. S. (2012). Basketball teams as strategic networks. PLoS ONE, 7(11), e47445.CrossRefGoogle Scholar
  18. Franks, A. M., D’Amour, A., Cervone, D., & Bornn, L. (2016). Meta-analytics: Tools for understanding the statistical properties of sports metrics. Journal of Quantitative Analysis in Sports, 12(4), 151–165.CrossRefGoogle Scholar
  19. Gabel, A., & Redner, S. (2012). Random walk picture of basketball scoring. Journal of Quantitative Analysis in Sports, 8(1), 1–20.CrossRefGoogle Scholar
  20. García, J., Ibáñez, S. J., De Santos, R. M., Leite, N., & Sampaio, J. (2013). Identifying basketball performance indicators in regular season and playoff games. Journal of Human Kinetics, 36(1), 161–168.CrossRefGoogle Scholar
  21. Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295–314.CrossRefGoogle Scholar
  22. Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys (CSUR), 50(2), 22.CrossRefGoogle Scholar
  23. Gupta, A. A. (2015). A new approach to bracket prediction in the NCAA men’s basketball tournament based on a dual-proportion likelihood. Journal of Quantitative Analysis in Sports, 11(1), 53–67.CrossRefGoogle Scholar
  24. Koh, K. T., Wang, C. K. J., & Mallett, C. (2011). Discriminating factors between successful and unsuccessful teams: A case study in elite youth Olympic basketball games. Journal of Quantitative Analysis in Sports, 7(3), 21.  https://doi.org/10.2202/1559-0410.1346.CrossRefGoogle Scholar
  25. Koh, K. T., Wang, C. K. J., & Mallett, C. (2012). Discriminating factors between successful and unsuccessful elite youth Olympic female basketball teams. International Journal of Performance Analysis in Sport, 12(1), 119–131.CrossRefGoogle Scholar
  26. Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 3(3), 1–22.CrossRefGoogle Scholar
  27. Lamas, L., De Rose, Jr D., Santana, F. L., Rostaiser, E., Negretti, L., & Ugrinowitsch, C. (2011). Space creation dynamics in basketball offence: Validation and evaluation of elite teams. International Journal of Performance Analysis in Sport, 11(1), 71–84.CrossRefGoogle Scholar
  28. Lopez, M. J., & Matthews, G. J. (2015). Building an NCAA men’s basketball predictive model and quantifying its success. Journal of Quantitative Analysis in Sports, 11(1), 5–12.CrossRefGoogle Scholar
  29. Manisera, M., Sandri, M., & Zuccolotto, P. (2019). BasketballAnalyzeR: The R package for basketball analytics. In Conference “smart statistics for smart applications”, Pearson, SIS 2019, 19th–21st June 2019 (pp. 395–402).Google Scholar
  30. Manner, H. (2016). Modeling and forecasting the outcomes of NBA basketball games. Journal of Quantitative Analysis in Sports, 12(1), 31–41.CrossRefGoogle Scholar
  31. Metulini, R., Manisera, M., & Zuccolotto, P. (2017a). Sensor analytics in basketball. In Proceedings of the 6th international conference on mathematics in sport.Google Scholar
  32. Metulini, R., Manisera, M., & Zuccolotto, P. (2017b). Space-time analysis of movements in basketball using sensor data. In Statistics and data science: New challenges, new generations—Proceedings of the conference of the Italian Statistical Society, Florence 28–30 June 2017.Google Scholar
  33. Metulini, R., Manisera, M., & Zuccolotto, P. (2018). Modelling the dynamic pattern of surface area in basketball and its effects on team performance. Journal of Quantitative Analysis in Sports, 14(3), 117–130.CrossRefGoogle Scholar
  34. Miller, A. C., & Bornn, L. (2017). Possession sketches: Mapping NBA strategies. In MIT Sloan sports analytics conference 2017.Google Scholar
  35. Oliver, D. (2004). Basketball on paper: Rules and tools for performance analysis. Sterling: Potomac Books Inc.Google Scholar
  36. Özmen, U. M. (2012). Foreign player quota, experience and efficiency of basketball players. Journal of Quantitative Analysis in Sports, 8(1), 1–18.CrossRefGoogle Scholar
  37. Page, G. L., Barney, B. J., & McGuire, A. T. (2013). Effect of position, usage rate, and per game minutes played on NBA player production curves. Journal of Quantitative Analysis in Sports, 9(4), 337–345.Google Scholar
  38. Passos, P., Araújo, D., & Volossovitch, A. (2016). Performance analysis in team sports. New York: Taylor & Francis.CrossRefGoogle Scholar
  39. Passos, P., Davids, K., Araújo, D., Paz, N., Minguéns, 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, 14(2), 170–176.CrossRefGoogle Scholar
  40. Pearl, J. (2000). Causality: Models, reasoning and inference (Vol. 29). Berlin: Springer.Google Scholar
  41. Piette, J., Pham, L., & Anand, S. (2011). Evaluating basketball player performance via statistical network modeling. In MIT Sloan sports analytics conference.Google Scholar
  42. Ruiz, F. J., & Perez-Cruz, F. (2015). A generative model for predicting outcomes in college basketball. Journal of Quantitative Analysis in Sports, 11(1), 39–52.CrossRefGoogle Scholar
  43. Sandri, M. (2020). The R package BasketballAnalyzeR. CRC Press, chap 6. In P. Zuccolotto & M. Manisera, Basketball data science. With applications in R.Google Scholar
  44. Sandri, M., Zuccolotto, P., & Manisera, M. (2018). BasketballAnalyzeR: An R package for the analysis of basketball data. https://github.com/sndmrc/BasketballAnalyzeR. Accessed 28 Nov 2019.
  45. Schumaker, R. P., Solieman, O. K., & Chen, H. (2010). Sports data mining. Berlin: Springer.CrossRefGoogle Scholar
  46. Schwarz, W. (2012). Predicting the maximum lead from final scores in basketball: A diffusion model. Journal of Quantitative Analysis in Sports, 8(4).Google Scholar
  47. Shortridge, A., Goldsberry, K., & Adams, M. (2014). Creating space to shoot: Quantifying spatial relative field goal efficiency in basketball. Journal of Quantitative Analysis in Sports, 10(3), 303–313.CrossRefGoogle Scholar
  48. Skinner, B., & Goldman, M. (2017). Optimal strategy in basketball. In Handbook of statistical methods and analyses in sports (pp. 229–244). Chapman and Hall/CRC.Google Scholar
  49. Travassos, B., Araújo, D., Davids, K., Esteves, P. T., & Fernandes, O. (2012). Improving passing actions in team sports by developing interpersonal interactions between players. International Journal of Sports Science & Coaching, 7(4), 677–688.CrossRefGoogle Scholar
  50. Vračar, P., Štrumbelj, E., & Kononenko, I. (2016). Modeling basketball play-by-play data. Expert Systems with Applications, 44, 58–66.CrossRefGoogle Scholar
  51. Wu, S., & Bornn, L. (2018). Modeling offensive player movement in professional basketball. The American Statistician, 72(1), 72–79.CrossRefGoogle Scholar
  52. Yuan, L. H., Liu, A., Yeh, A., Kaufman, A., Reece, A., Bull, P., et al. (2015). A mixture-of-modelers approach to forecasting NCAA tournament outcomes. Journal of Quantitative Analysis in Sports, 11(1), 13–27.CrossRefGoogle Scholar
  53. Zhang, T., Hu, G., & Liao, Q. (2013). Analysis of offense tactics of basketball games using link prediction. In 2013 IEEE/ACIS 12th international conference on computer and information science (ICIS) (pp. 207–212). IEEE.Google Scholar
  54. Zuccolotto, P., & Manisera, M. (2020). Basketball data science. With applications in R. CRC Press.Google Scholar
  55. Zuccolotto, P., Manisera, M., & Sandri, M. (2018). Big data analytics for modeling scoring probability in basketball: The effect of shooting under high-pressure conditions. International Journal of Sports Science & Coaching, 13(4), 569–589.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Big&Open Data Innovation Laboratory (BODaI-Lab)University of BresciaBresciaItaly

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