| Summary: | In sports, a calculation of efficiency is considered to be one of the most
challenging tasks. In this paper, DEA is used to evaluate an efficiency of
the NBA players, based on multiple inputs and multiple outputs. The
efficiency is evaluated for 26 NBA players at the guard position based on
existing data. However, if we want to generate the efficiency for a new
player, we would have to re-conduct the DEA analysis. Therefore, to predict
the efficiency of a new player, machine learning algorithms are applied. The
DEA results are incorporated as an input for the learning algorithms,
defining thereby an efficiency frontier function form with high reliability.
In this paper, linear regression, neural network, and support vector machines
are used to predict an efficiency frontier. The results have shown that
neural networks can predict the efficiency with an error less than 1%, and
the linear regression with an error less than 2%.
|
|---|
