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Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling

Author

Listed:
  • Nikolaos Stavropoulos

    (Laboratory of Evaluation of Human Biological Performance, School of Physical Education and Sport Science, Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece)

  • Alexandra Papadopoulou

    (Section of Statistics and Operational Research, Department of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Pavlos Kolias

    (Section of Statistics and Operational Research, Department of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

In basketball, the offensive movements on both strong and weak sides and tactical behavior play major roles in the effectiveness of a team’s offense. In the literature, studies are mostly focused on offensive actions, such as ball screens on the strong side. In the present paper, for the first time a second-order Markov model is defined to evaluate players’ interactions on the weak side, particularly for exploring the effectiveness of tactical structures and off-ball screens regarding the final outcome. The sample consisted of 1170 possessions of the FIBA Basketball Champions League 2018–2019. The variables of interest were the type of screen on the weak side, the finishing move, and the outcome of the shot. The model incorporates partial non-homogeneity according to the time of the execution (0–24″) and the quarter of playtime, and it is conditioned on the off-ball screen type. Regarding the overall performance, the results indicated that the outcome of each possession was influenced not only by the type of the executed shot, but also by the specific type of screen that took place earlier on the weak side of the offense. Thus, the proposed model could operate as an advisory tool for the coach’s strategic plans.

Suggested Citation

  • Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1991-:d:618249
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    References listed on IDEAS

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    1. S McClean & P Millard, 2007. "Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 255-261, February.
    2. Georgiou, A. C. & Vassiliou, P. -C. G., 1997. "Cost models in nonhomogeneous Markov systems," European Journal of Operational Research, Elsevier, vol. 100(1), pages 81-96, July.
    3. Kubatko Justin & Oliver Dean & Pelton Kevin & Rosenbaum Dan T, 2007. "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-24, July.
    4. B. Bazanov & P. Võhandu & R. Haljand, 2006. "Factors influencing the teamwork intensity in basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 6(2), pages 88-96, November.
    5. Paul Kvam & Joel S. Sokol, 2006. "A logistic regression/Markov chain model for NCAA basketball," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(8), pages 788-803, December.
    6. M. J. Faddy & S. I. McClean, 1999. "Analysing data on lengths of stay of hospital patients using phase‐type distributions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(4), pages 311-317, October.
    7. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2012. "Income inequality dynamic measurement of Markov models: Application to some European countries," Economic Modelling, Elsevier, vol. 29(5), pages 1598-1602.
    8. Jorge Lorenzo Calvo & Alejandro Menéndez García & Archit Navandar, 2017. "Analysis of mismatch after ball screens in Spanish professional basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(4), pages 555-562, July.
    9. Jaime Sampaio & Manuel Janeira, 2003. "Statistical analyses of basketball team performance: understanding teams’ wins and losses according to a different index of ball possessions," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 3(1), pages 40-49, April.
    10. Dimitriou, V.A. & Georgiou, A.C. & Tsantas, N., 2013. "The multivariate non-homogeneous Markov manpower system in a departmental mobility framework," European Journal of Operational Research, Elsevier, vol. 228(1), pages 112-121.
    11. A. Vaquera & J.V. García-Tormo & M.A. Gómez Ruano & J.C Morante, 2016. "An exploration of ball screen effectiveness on elite basketball teams," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(2), pages 475-485, August.
    12. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    13. L. Lamas & D. De Rose Junior & F. Santana & E. Rostaiser & L. Negretti & C. Ugrinowitsch, 2011. "Space creation dynamics in basketball offence: validation and evaluation of elite teams," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(1), pages 71-84, April.
    14. Leonardo Lamas & Felipe Santana & Matthew Heiner & Carlos Ugrinowitsch & Gilbert Fellingham, 2015. "Modeling the Offensive-Defensive Interaction and Resulting Outcomes in Basketball," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-14, December.
    15. Adrian Raftery & Simon Tavaré, 1994. "Estimation and Modelling Repeated Patterns in High Order Markov Chains with the Mixture Transition Distribution Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 179-199, March.
    16. N. Stavropoulos & P. Kolias & A. Papadopoulou & G. Stavropoulou, 2021. "Game related predictors discriminating between winning and losing teams in preliminary, second and final round of basketball world cup 2019," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 21(3), pages 383-395, May.
    17. W K Ching & E S Fung & M K Ng, 2003. "A higher-order Markov model for the Newsboy's problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 291-298, March.
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