IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i16p5722-d396082.html
   My bibliography  Save this article

Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis

Author

Listed:
  • Shaoliang Zhang

    (Division of Sport Science & Physical Education, Tsinghua University, Beijing 100084, China)

  • Miguel Ángel Gomez

    (Facultad de Ciencias de la Actividad Física y del Deporte (INEF), Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Qing Yi

    (School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai 200438, China
    Shanghai Key Lab of Human Performance, Shanghai University of Sport, Shanghai 200438, China)

  • Rui Dong

    (China Basketball College, Beijing Sport University, Beijing 100084, China)

  • Anthony Leicht

    (Sport and Exercise Science, James Cook University, Townsville, QLD 4810, Australia)

  • Alberto Lorenzo

    (Facultad de Ciencias de la Actividad Física y del Deporte (INEF), Universidad Politécnica de Madrid, 28040 Madrid, Spain)

Abstract

The FIBA Basketball World Cup is one of the most prominent sporting competitions for men’s basketball, with coaches interested in key performance indicators (KPIs) that give a better understanding of basketball competitions. The aims of the study were to (1) examine the relationship between match KPIs and outcome in elite men’s basketball; and (2) identify the most suitable analysis (multiple linear regression (MLR) vs. quantile regression (QR)) to model this relationship during the men’s basketball tournament. A total of 184 performance records from 92 games were selected and analyzed via MLR and QR, using 10th, 25th, 50th, 75th and 90th quantiles. Several offensive (Paint Score, Mid-Range Score, Three-Point Score, Offensive Rebounds and Turnovers) and defensive (Defensive Rebounds, Steals and Personal Fouls) KPIs were associated with match outcome. The QR model identified additional KPIs that influenced match outcome than the MLR model, with these being Mid-Range Score at the 10th quantile and Offensive Rebounds at the 90th quantile. In terms of contextual variables, the quality of opponent had no impact on match outcome across the entire range of quantiles. Our results highlight QR modelling as a potentially superior tool for performance analysts and coaches to design and monitor technical–tactical plans during match-play. Our study has identified the KPIs contributing to match success at the 2019 FIBA Basketball World Cup with QR modelling assisting with a more detailed performance analysis, to support coaches with the optimization of training and match-play styles.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5722-:d:396082
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/16/5722/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/16/5722/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shaoliang Zhang & Alberto Lorenzo & Changjing Zhou & Yixiong Cui & Bruno Gonçalves & Miguel Angel Gómez, 2019. "Performance profiles and opposition interaction during game-play in elite basketball: evidences from National Basketball Association," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 19(1), pages 28-48, January.
    2. Tsamourtzis Evangelos & Karypidis Alexandros & Athanasiou Nikolaos, 2005. "Analysis of fast breaks in basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 5(2), pages 17-22, November.
    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. 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.
    5. Javier García & J. Sergio Ibáñez & A. Miguel Gómez & Jaime Sampaio, 2014. "Basketball Game-related statistics discriminating ACB league teams according to game location, game outcome and final score differences," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(2), pages 443-452, August.
    6. Jorge Malarranha & Bruno Figueira & Nuno Leite & Jaime Sampaio, 2013. "Dynamic Modeling of Performance in Basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(2), pages 377-387, August.
    7. Gómez Miguel Ángel & Tsamourtzis Evangelos & Lorenzo Alberto, 2006. "Defensive systems in basketball ball possessions," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 6(1), pages 98-107, June.
    8. Gabor Csataljay & Nic James & Mike Hughes & Henriette Dancs, 2013. "Effects of defensive pressure on basketball shooting performance," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(3), pages 594-601, December.
    9. 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.
    10. Shaoliang Zhang & Alberto Lorenzo & Miguel-Angel Gómez & Hongyou Liu & Bruno Gonçalves & Jaime Sampaio, 2017. "Players’ technical and physical performance profiles and game-to-game variation in NBA," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(4), pages 466-483, July.
    11. Erhan Çene, 2018. "What is the difference between a winning and a losing team: insights from Euroleague basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(1), pages 55-68, January.
    12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    13. Miguel Angel Gomez & Lorenzo Gasperi & Corrado Lupo, 2016. "Performance analysis of game dynamics during the 4 game quarter of NBA close games," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(1), pages 249-263, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Kęstutis Matulaitis & Tomas Bietkis, 2021. "Prediction of Offensive Possession Ends in Elite Basketball Teams," IJERPH, MDPI, vol. 18(3), pages 1-11, January.
    3. Zsombor Zilinyi & Ágoston Nagy & Szilvia Borbély & Tamás Sterbenz, 2022. "Bounded Rationality and Heuristics: Do We Only Need to Score in Order to Win Individual Awards in Basketball?," IJERPH, MDPI, vol. 19(4), pages 1-9, February.

    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. 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.
    4. Zsombor Zilinyi & Ágoston Nagy & Szilvia Borbély & Tamás Sterbenz, 2022. "Bounded Rationality and Heuristics: Do We Only Need to Score in Order to Win Individual Awards in Basketball?," IJERPH, MDPI, vol. 19(4), pages 1-9, February.
    5. 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.
    6. 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.
    7. Tomasz Zając & Kazimierz Mikołajec & Paweł Chmura & Marek Konefał & Michał Krzysztofik & Piotr Makar, 2023. "Long-Term Trends in Shooting Performance in the NBA: An Analysis of Two- and Three-Point Shooting across 40 Consecutive Seasons," IJERPH, MDPI, vol. 20(3), pages 1-12, January.
    8. Zongpeng Zhai & Yongbo Guo & Yuanchang Li & Shaoliang Zhang & Hongyou Liu, 2020. "The Regional Differences in Game-Play Styles Considering Playing Position in the FIBA Female Continental Basketball Competitions," IJERPH, MDPI, vol. 17(16), pages 1-11, August.
    9. 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).
    10. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    11. Molyneux, Philip & Pancotto, Livia & Reghezza, Alessio & Rodriguez d'Acri, Costanza, 2022. "Interest rate risk and monetary policy normalisation in the euro area," Journal of International Money and Finance, Elsevier, vol. 124(C).
    12. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    13. Georgios Bertsatos & Plutarchos Sakellaris & Mike G. Tsionas, 2022. "Extensions of the Pesaran, Shin and Smith (2001) bounds testing procedure," Empirical Economics, Springer, vol. 62(2), pages 605-634, February.
    14. Salimata Sissoko, 2011. "Working Paper 03-11 - Niveau de décentralisation de la négociation et structure des salaires," Working Papers 1103, Federal Planning Bureau, Belgium.
    15. Korom, Philipp, 2016. "Inherited advantage: The importance of inheritance for private wealth accumulation in Europe," MPIfG Discussion Paper 16/11, Max Planck Institute for the Study of Societies.
    16. Daniele, Vittorio, 2007. "Criminalità e investimenti esteri. Un’analisi per le province italiane [The effect of organized crime on Foreign Investments. An Empirical Analysis for the Italian Provinces]," MPRA Paper 6417, University Library of Munich, Germany.
    17. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    18. Cuesta, Lizeth & Ruiz, Yomara, 2021. "Efecto de la globalización sobre la desigualdad. Un estudio global para 104 países usando regresiones cuantílicas [Effect of globalization on inequality. A global study for 104 countries using quan," MPRA Paper 111022, University Library of Munich, Germany.
    19. Dutta, Anupam & Bouri, Elie & Rothovius, Timo & Uddin, Gazi Salah, 2023. "Climate risk and green investments: New evidence," Energy, Elsevier, vol. 265(C).
    20. Cowling, Marc & Ughetto, Elisa & Lee, Neil, 2018. "The innovation debt penalty: Cost of debt, loan default, and the effects of a public loan guarantee on high-tech firms," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 166-176.

    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:jijerp:v:17:y:2020:i:16:p:5722-:d:396082. 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.