Estimating NBA players salary share according to their performance on court: A machine learning approach

Professional athletes' field performance and salaries is a topic that has attracted the interest of numerous researchers (Garris and Wilkes, 2017, Olbrecht, 2009, Vincent and Eastman, 2009, Wiseman and Chatterjee, 2010, Yilmaz and Chatterjee, 2003, Zimmer and Zimmer, 2001). The general question of interest is whether players deserve their salaries based on their performance statistics. We emphasize that this relationship is not linear and hence linear models are bound to fail in capturing the underlying true association. An additional concern, separate from non-linearity, is model predictability for which internal evaluation has limitations and leads to an over-optimistic performance. These and more matters, discussed later, require delicate treatment which, if not properly addressed, will yield erroneous results.