Predicting full retirement attainment of NBA players

Foutzopoulos, Giorgos and Pandis, Nikolaos and Tsagris, Michail (2024): Predicting full retirement attainment of NBA players.

Abstract

The aim of this analysis is to predict whether an National Basketball Association (NBA) player will be active in the league for at least 10 years so as to be qualified for NBA's full retirement scheme which allows for the maximum benefit payable by law. We collected per game statistics for players during their second year, drafted during the years 1999 up to 2006, for which, information on their career longetivity is known. By feeding these statistics of the sophomore players into statistical and machine learning algorithms we select the important statistics and manage to accomplish a satisfactory predictability performance. Further, we visualize the effect of each of the selected statistics on the estimated probability of staying in the league for more than 10 years. Finally, as an illustration, we collected data from players that were drafted 11 years ago (and some are still active) and estimated their probability of surviving in the league for at least 10 years.

• Item Type: MPRA Paper
• Language: English
• Keywords: BA, career duration, exit discrimination, retirement scheme
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

References

Barnes, J. C. (2008). Relationship of selected pre–NBA career variables to NBA players’ career longevity. The Sport Journal, (April-02).

Breiman, L. (2001). Random Forests. Machine Learning, 45:5–32.

Coates, D. and Oguntimein, B. (2010). The Length and Success of NBA Careers: Does College Production Predict Professional Outcomes? International Journal of Sport Finance, 5(1).

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20:273–297.

Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1.

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5):1189–1232.

Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American statistical Association, 76(376):817–823.

Fynn, K. D. and Sonnenschein, M. (2012). An Analysis of the Career Length of Professional Basketball Players. The Macalester Review, 2(2).

Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24(1):44–65.

Greg, R. and Developers, G. (2024). gbm: Generalized Boosted Regression Models. R package version 2.1.9.

Groothuis, P. A. and Hill, J. R. (2004). Exit discrimination in the NBA: A duration analysis of career length. Economic Inquiry, 42(2):341–349.

Groothuis, P. A. and Hill, J. R. (2018). Career Duration in the NBA: Do Foreign Players Exit Early? Journal of Sports Economics, 19(6):873–883.

Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: applications to nonorthogonal problems. Technometrics, 12(1):69–82.

James, G. (1954). Tests of linear hypotheses in univariate and multivariate analysis when the ratios of the population variances are unknown. Biometrika, 41(1/2):19–43.

Johns, W., Walley, K. C., Seedat, R., Thordarson, D. B., Jackson, B., and Gonzalez, T. (2021). Career Outlook and Performance of Professional Athletes After Achilles Tendon Rupture: A Systematic Review. Foot and Ankle International, 42(4):495–509.

Kester, B. S., Behery, O. A., Minhas, S. V., and Hsu, W. K. (2017). Athletic performance and career longevity following anterior cruciate ligament reconstruction in the National Basketball Association. Knee Surgery, Sports Traumatology, Arthroscopy, 25:3031–3037.

Khalil, L. S., Jildeh, T. R., Tramer, J. S., Abbas, M. J., Hessburg, L., Mehran, N., and Okoroha, K. R. (2020). Effect of Achilles Tendon Rupture on Player Performance and Longevity in National Basketball Association Players. Orthopaedic Journal of Sports Medicine, 8(11).

Kursa, M. B., Jankowski, A., and Rudnicki, W. R. (2010). Boruta–a system for feature selection. Fundamenta Informaticae, 101(4):271–285.

Kursa, M. B. and Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11).

Martin, C. L., Arundale, A. J., Kluzek, S., Ferguson, T., Collins, G. S., and Bullock, G. S. (2021). Characterization of Rookie Season Injury and Illness and Career Longevity among National Basketball Association Players. JAMA Network Open, 4(10).

Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2023). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-13.

Miguel, C. G., M´ılan, F. J., Soares, A. L., Quinauad, R. T., K´os, L. D., Palheta, C. E., Mendes, F. G., and Carvalho, H. M. (2019). Modelling the relationship between NBA draft and the career longevity of players using generalized additive models. Revista de Psicolog´ıa del Deporte, 28(3):0065–70.

Petersen, A. M., Jung, W.-S., Yang, J.-S., and Stanley, H. E. (2011). Quantitative and empirical demonstration of the Matthew effect in a study of career longevity. Proceedings of the National Academy of Sciences, 108(1):18–23.

Psathas, A., Rallatou, D., and Tsagris, M. (2023). Skin tone of nba players and performance statistics. is there a relationship? Communications in Statistics: Case Studies, Data Analysis and Applications, 9(3):234–251.

Staw, B. M. and Hoang, H. (1995). Sunk Costs in the NBA: Why Draft Order Affects Playing Time and Survival in Professional. Administrative Science Quarterly, 40(3):474–494.

Sz´ekely, G. J., Rizzo, M. L., et al. (2004). Testing for equal distributions in high dimension. InterStat, 5(16.10):1249–1272.

Team, R. C. (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288.

Wright, M. N. and Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1):1–17.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2):301–320.