IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v5y2022i1p11-171d745075.html
   My bibliography  Save this article

All-NBA Teams’ Selection Based on Unsupervised Learning

Author

Listed:
  • João Vítor Rocha da Silva

    (Department of Statistics, Federal University of Bahia, Salvador CEP: 40.170-110, Brazil)

  • Paulo Canas Rodrigues

    (Department of Statistics, Federal University of Bahia, Salvador CEP: 40.170-110, Brazil)

Abstract

All-NBA Teams’ selections have great implications for the players’ and teams’ futures. Since contract extensions are highly related to awards, which can be seen as indexes that measure a players’ production in a year, team selection is of mutual interest for athletes and franchises. In this paper, we are interested in studying the current selection format. In particular, this study aims to: (i) identify the factors that are taken into consideration by voters when choosing the three All-NBA Teams; and (ii) suggest a new selection format to evaluate players’ performances. Average game-related statistics of all active NBA players in regular seasons from 2013-14 to 2018-19, were analyzed using LASSO (Logistic) Regression and Principal Component Analysis (PCA). It was possible: (i) to determine an All-NBA player profile; (ii) to determine that this profile can cause a misrepresentation of players’ modern and versatile gameplay styles; and (iii) to suggest a new way to evaluate and select players, through PCA. As the results of this paper a model is presented that may help not only the NBA to better evaluate players, but any basketball league; it also may be a source to researchers that aim to investigate player performance, development, and their impact over many seasons.

Suggested Citation

  • João Vítor Rocha da Silva & Paulo Canas Rodrigues, 2022. "All-NBA Teams’ Selection Based on Unsupervised Learning," Stats, MDPI, vol. 5(1), pages 1-18, February.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:1:p:11-171:d:745075
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/5/1/11/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/5/1/11/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David J. Berri & Martin B. Schmidt & Stacey L. Brook, 2004. "Stars at the Gate," Journal of Sports Economics, , vol. 5(1), pages 33-50, February.
    2. Page Garritt L & Fellingham Gilbert W & Reese C. Shane, 2007. "Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-18, October.
    3. Kevin J. Stiroh, 2007. "Playing For Keeps: Pay And Performance In The Nba," Economic Inquiry, Western Economic Association International, vol. 45(1), pages 145-161, January.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Chih-Hai Yang & Hsuan-Yu Lin, 2012. "Is There Salary Discrimination by Nationality in the NBA?," Journal of Sports Economics, , vol. 13(1), pages 53-75, February.
    6. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    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. Harald Oberhofer & Marian Schwinner, 2017. "Do Individual Salaries Depend On the Performance of the Peers? Prototype Heuristic and Wage Bargaining in the NBA," WIFO Working Papers 534, WIFO.
    2. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2018. "Sparse principal component regression for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 180-196.
    3. Artem Sokolov & Daniel E Carlin & Evan O Paull & Robert Baertsch & Joshua M Stuart, 2016. "Pathway-Based Genomics Prediction using Generalized Elastic Net," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
    4. Sendil K. Ethiraj & Pranav Garg, 2012. "The Division of Gains from Complementarities in Human-Capital-Intensive Activity," Organization Science, INFORMS, vol. 23(3), pages 725-742, June.
    5. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    6. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    7. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    8. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    9. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    10. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    11. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    12. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    13. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    14. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    16. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    17. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    18. Daniel Weimar & Katrin Scharfenkamp, 2019. "Effort reduction of employer‐to‐employer changers: Empirical evidence from football," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 40(3), pages 277-291, April.
    19. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    20. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.

    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:jstats:v:5:y:2022:i:1:p:11-171:d:745075. 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.