IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i11p4078-d368566.html
   My bibliography  Save this article

Deliberate Practice, Functional Performance and Psychological Characteristics in Young Basketball Players: A Bayesian Multilevel Analysis

Author

Listed:
  • Ahlan B. Lima

    (Department of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis, Santa Catarina SC 88040-900, Brazil)

  • Juarez V. Nascimento

    (Department of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis, Santa Catarina SC 88040-900, Brazil)

  • Thiago J. Leonardi

    (School of Physical Education, Physiotherapy and Dance, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul 90690-200, Brazil)

  • André L. Soares

    (Department of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis, Santa Catarina SC 88040-900, Brazil)

  • Roberto R. Paes

    (Faculty Physical Education, University of Campinas, Campinas, São Paulo 13083-851, Brazil)

  • Carlos E. Gonçalves

    (Faculty Sport Sciences and Physical Education, University of Coimbra, 3040-156 Coimbra, Portugal)

  • Humberto M. Carvalho

    (Department of Physical Education, School of Sports, Federal University of Santa Catarina, Florianópolis, Santa Catarina SC 88040-900, Brazil)

Abstract

Background: Early sport specialization has increased its popularity mostly based on the deliberate practice theory premises. In this study, we examined the influence of the age of onset of deliberate basketball practice on body size, functional performance (countermovement jump, line drill and yo-yo intermittent recovery level 1), motivation for achievement and competitiveness, motivation for deliberate practice and sources of enjoyment among young Brazilian basketball players. In addition, we adjusted for the influence of gender, age group, maturity status and state basketball federation on the outcomes. Methods: The sample included 120 female and 201 male adolescent basketball players aged 14.0 (1.7) years, on average. We grouped players by the age of onset of deliberate basketball practice as related to biologic maturation milestones (pre-puberty deliberate practice onset, mid-puberty deliberate practice onset and late-puberty deliberate practice onset). Results: There was no substantial variation among contrasting players by the onset of deliberate practice in all of the outcomes. Adjusting for gender, male players with late-puberty deliberate practice onset had better functional performance than players with pre- and mid-puberty onset of practice. Females players with late-puberty deliberate practice onset had slightly worst functional performance than players with pre- and mid-puberty onset of practice. Conclusions: Early deliberate basketball practice does not appear to provide an advantage for the development of physiological functions. Likewise, enjoyment, motivation for deliberate practice and motivation for achievement and competition do not appear to be negatively influenced by early deliberate basketball practice. The debate about the relationship between time spent in deliberate practice and performance development in young athletes will need to emphasize the coaching pedagogical quality and the training environment and account for informal practice and deliberate play.

Suggested Citation

  • Ahlan B. Lima & Juarez V. Nascimento & Thiago J. Leonardi & André L. Soares & Roberto R. Paes & Carlos E. Gonçalves & Humberto M. Carvalho, 2020. "Deliberate Practice, Functional Performance and Psychological Characteristics in Young Basketball Players: A Bayesian Multilevel Analysis," IJERPH, MDPI, vol. 17(11), pages 1-14, June.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:4078-:d:368566
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/11/4078/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/11/4078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    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. Luis Miguel Fernández-Galván & Daniel Boullosa & Pedro Jiménez-Reyes & Víctor Cuadrado-Peñafiel & Arturo Casado, 2021. "Examination of the Sprinting and Jumping Force-Velocity Profiles in Young Soccer Players at Different Maturational Stages," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    2. Sergio J. Ibáñez & María Isabel Piñar & David García & David Mancha-Triguero, 2023. "Physical Fitness as a Predictor of Performance during Competition in Professional Women’s Basketball Players," IJERPH, MDPI, vol. 20(2), pages 1-21, January.

    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. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    2. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    3. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    4. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    5. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    6. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    7. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    8. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    9. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    10. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    11. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    12. D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.
    13. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    14. Jonas Moss & Riccardo De Bin, 2023. "Modelling publication bias and p‐hacking," Biometrics, The International Biometric Society, vol. 79(1), pages 319-331, March.
    15. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    16. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    17. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    18. Alina Ferecatu & Arnaud Bruyn & Prithwiraj Mukherjee, 2024. "Silently killing your panelists one email at a time: The true cost of email solicitations," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1216-1239, July.
    19. Loke Schmalensee & Pauline Caillault & Katrín Hulda Gunnarsdóttir & Karl Gotthard & Philipp Lehmann, 2023. "Seasonal specialization drives divergent population dynamics in two closely related butterflies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.

    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:11:p:4078-:d:368566. 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.