Predicting success in the National Basketball Association: Stability & potential
Task Performance and Analysis
© 2014 Elsevier Ltd. Objectives: To create a more rigorous model of early career success among players in the National Basketball Association (NBA) using growth mixture models. To test the extent to which NBA careers can be predicted by variables that represent past performance and variables that might represent untapped potential. Design: Archival data was collected from measures taken at the pre-draft NBA combine and publicly available data on college and NBA performance. Method: The first three years of players' NBA careers from 2001 to 2006 draft classes were analyzed using a growth mixture model with collected variables predicting latent class. The estimated parameters were then used to forecast the 2007 to 2010 draft classes. Draft order was also predicted with the same variables. Results: NBA player skill formed 3 latent classes of players; only one class performed well in the NBA. Membership in the strongest class was only predicted by age, quality of college program, and players' college performance. Latent class probabilities predicted NBA career trajectory slightly better than draft order in both the estimation model and in the forecast model. NBA draft order was predicted by the same variables as well as arm span and agility. Conclusions: None of the variables analyzed supported an "untapped potential" hypothesis. There is clear evidence for roles of training environment and the stability of skill. The data is consistent with views of deliberate practice and skill acquisition and appears to be consistent with data showing the benefits of being identified as talented, such as the Matthew effect.
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