IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i16p1991-d618249.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/16/1991/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/16/1991/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kęstutis Matulaitis & Tomas Bietkis, 2021. "Prediction of Offensive Possession Ends in Elite Basketball Teams," IJERPH, MDPI, vol. 18(3), pages 1-11, January.
    2. Jorge Serna & Verónica Muñoz-Arroyave & Jaume March-Llanes & M. Teresa Anguera & Queralt Prat & Aaron Rillo-Albert & David Falcón & Pere Lavega-Burgués, 2021. "Effect of Ball Screen and One-on-One on the Level of Opposition and Effectiveness of Shots in the ACB," IJERPH, MDPI, vol. 18(5), pages 1-16, March.
    3. Iván Prieto-Lage & Christopher Vázquez-Estévez & Adrián Paramés-González & Juan Carlos Argibay-González & Xoana Reguera-López-de-la-Osa & Alfonso Gutiérrez-Santiago, 2022. "Ball Screens in the Men’s 2019 Basketball World Cup," IJERPH, MDPI, vol. 20(1), pages 1-13, December.
    4. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2021. "Spatial Performance Indicators and Graphs in Basketball," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 725-738, August.
    5. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2023. "Spatial performance analysis in basketball with CART, random forest and extremely randomized trees," Annals of Operations Research, Springer, vol. 325(1), pages 495-519, June.
    6. Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Tim De Feyter & Marie-Anne Guerry & Komarudin, 2017. "Optimizing cost-effectiveness in a stochastic Markov manpower planning system under control by recruitment," Annals of Operations Research, Springer, vol. 253(1), pages 117-131, June.
    9. P.-C.G. Vassiliou, 2021. "Non-Homogeneous Markov Set Systems," Mathematics, MDPI, vol. 9(5), pages 1-25, February.
    10. Yiannis Nikolaidis, 2015. "Building a basketball game strategy through statistical analysis of data," Annals of Operations Research, Springer, vol. 227(1), pages 137-159, April.
    11. Shaoliang Zhang & Miguel Ángel Gomez & Qing Yi & Rui Dong & Anthony Leicht & Alberto Lorenzo, 2020. "Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-11, August.
    12. Damásio, Bruno & Nicolau, João, 2024. "Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    13. Joseph Price & Justin Wolfers, 2010. "Racial Discrimination Among NBA Referees," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(4), pages 1859-1887.
    14. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    15. Gabel Alan & Redner Sidney, 2012. "Random Walk Picture of Basketball Scoring," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-20, March.
    16. Lorenzo Gasperi & Daniele Conte & Anthony Leicht & Miguel-Ángel Gómez-Ruano, 2020. "Game Related Statistics Discriminate National and Foreign Players According to Playing Position and Team Ability in the Women’s Basketball EuroLeague," IJERPH, MDPI, vol. 17(15), pages 1-10, July.
    17. Jaime Sampaio & Tim McGarry & Julio Calleja-González & Sergio Jiménez Sáiz & Xavi Schelling i del Alcázar & Mindaugas Balciunas, 2015. "Exploring Game Performance in the National Basketball Association Using Player Tracking Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.
    18. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    19. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    20. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1991-:d:618249. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.