TWO-PHASED DEA-MLA APPROACH FOR PREDICTING EFFICIENCY OF NBA PLAYERS

In sports calculation of efficiency is considered as one of the most challenging tasks, since many relevant variables must be used. In this paper DEA is used to evaluate NBA player efficiency, which is based on multiple inputs and outputs data. Evaluation was done on 26 NBA player efficiency, who plays on guard position. This way efficiency of each player is provided. But, if we want to generate efficiency for new player, DEA would not be able to provide us answer without conducting DEA analysis again. Therefore, for the purpose of the predicting efficiency of new player machine learning algorithms are used. In this settings, DEA results are used as an input for learning algorithms, which defines efficiency frontier function with high reliability. In this paper linear regression, neural network and support vector machines are used to predict efficiency boundary. Results have shown that neural networks can predict efficiency with less that 1% of error, linear regression with error less than 2%.