IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1924-d1042674.html
   My bibliography  Save this article

Long-Term Trends in Shooting Performance in the NBA: An Analysis of Two- and Three-Point Shooting across 40 Consecutive Seasons

Author

Listed:
  • Tomasz Zając

    (Human Performance Laboratory, The Jerzy Kukuczka Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland)

  • Kazimierz Mikołajec

    (Department of Basketball and Football, The Jerzy Kukuczka Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland)

  • Paweł Chmura

    (Department of Team Games, Wrocław University of Health and Sport Sciences, I.J., Paderewskiego 35, 51-612 Wrocław, Poland)

  • Marek Konefał

    (Department of Biological and Motor Sport Bases, Wrocław University of Health and Sport Sciences, I.J., Paderewskiego 35, 51-612 Wrocław, Poland)

  • Michał Krzysztofik

    (Institute of Sport Sciences, The Jerzy Kukuczka Academy of Physical Education in Katowice, 40-065 Katowice, Poland)

  • Piotr Makar

    (Faculty of Physical Education, Gdańsk University of Physical Education and Sport, 80-336 Gdańsk, Poland)

Abstract

This study aims to depict two-point and three-point shooting trends and explore their influence on game outcomes in the NBA across 40 consecutive seasons. Therefore, the following game-related statistics were considered: total points per game (PPG), games played (GP), field goals made (FGM), field goal attempts (FGA), field goal percentage (FG%), two-point field goals made (2PM), two-point field goal attempts (2PA), two-point field goal percentage (2P%), three-point field goals made (3PM), three-point field goal attempts (3PA), three-point field goal percentage (3P%), and a three-point field goal to two-point field goal ratio (3P/2P). The fixed-base indexes and inter-decade ANOVAs or Friedman tests were used as the main statistical tools. The number of 3PA significantly increased over time, while the number of 2PA decreased. A significant increase in 3P% was also observed, whereas 2P% remained relatively stable over the analyzed period. This study also revealed a higher number of ball possessions and more points scored per game, especially in the last decade of NBA competition.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1924-:d:1042674
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1924/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/3/1924/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Martí Casals & A. Jose Martinez, 2013. "Modelling player performance in basketball through mixed models," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 13(1), pages 64-82, April.
    3. Gabor Csataljay & Peter O’Donoghue & Mike Hughes & Henriette Dancs, 2009. "Performance indicators that distinguish winning and losing teams in basketball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 9(1), pages 60-66, April.
    4. 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.
    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. 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.
    2. 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.
    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.
    4. 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.
    5. Bakkenbüll, Linn-Brit, 2017. "Physical constitution matters for athletic performance and salary of NBA players," Discussion Papers of the Institute for Organisational Economics 1/2017, University of Münster, Institute for Organisational Economics.
    6. Pierpalo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach," Annals of Operations Research, Springer, vol. 325(1), pages 419-440, June.
    7. 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.
    8. Nimai Parmar & Nic James & Mike Hughes & Huw Jones & Gary Hearne, 2017. "Team performance indicators that predict match outcome and points difference in professional rugby league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 1044-1056, November.
    9. Assanskiy, Artur & Shaposhnikov, Daniil & Tylkin, Igor & Vasiliev, Gleb, 2022. "Prove them wrong: Do professional athletes perform better when facing their former clubs?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    10. Zoltan Boros & Kata Toth & Gergely Csurilla & Tamas Sterbenz, 2022. "A Comparison of 5v5 and 3x3 Men’s Basketball Regarding Shot Selection and Efficiency," IJERPH, MDPI, vol. 19(22), pages 1-12, November.
    11. 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.
    12. Radivoj Mandić & Saša Jakovljević & Frane Erčulj & Erik Štrumbelj, 2019. "Trends in NBA and Euroleague basketball: Analysis and comparison of statistical data from 2000 to 2017," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-17, October.
    13. Kęstutis Matulaitis & Tomas Bietkis, 2021. "Prediction of Offensive Possession Ends in Elite Basketball Teams," IJERPH, MDPI, vol. 18(3), pages 1-11, January.
    14. 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.
    15. 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).
    16. Patric Dolmeta & Raffaele Argiento & Silvia Montagna, 2023. "Bayesian GARCH modeling of functional sports data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 401-423, June.

    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:20:y:2023:i:3:p:1924-:d:1042674. 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.