TAILIEUCHUNG - Two-phased DEA-mla approach for predicting efficiency of NBA players
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%. | Yugoslav Journal of Operations Research 24 (2014), Number 3, 347-358 DOI: TWO-PHASED DEA-MLA APPROACH FOR PREDICTING EFFICIENCY OF NBA PLAYERS Sandro RADOVANOVIĆ University of Belgrade, Faculty of Organizational Sciences, Serbia Milan RADOJIČIĆ University of Belgrade, Faculty of Organizational Sciences, Serbia Gordana SAVIĆ University of Belgrade, Faculty of Organizational Sciences, Serbia Received: April 2014 / Accepted: August 2014 Abstract: 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%. Keywords: Data envelopment analysis; Efficiency analysis; Predictive analytics; Machine learning. MSC: 90B50, 90C29. 348 S. Radovanović, M. Radojičić, G. Savić / Two-phased DEA-MLA Approach 1. INTRODUCTION Contemporary sports have a significant impact on the world economy, and therefore increasing attention is paid to the analysis of sports teams and athletes. Consequently, it has become necessary to determine their impact, not only on the field, but also in the economy and society as a whole.
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