Geodesic
Two-Phased DEA-MLA Approach for Predicting Efficiency of NBA Players
Yugoslav journal of operations research, Tome 24 (2014) no. 3, p. 347 .

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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%.
Classification : 90B50, 90C29
Keywords: Data envelopment analysis, Efficiency analysis, Predictive analytics, Machine learning. 
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     author = {Sandro Radovanovi\'c and Milan Radoji\v{c}i\'c and Gordana Savi\'c},
     title = {Two-Phased {DEA-MLA} {Approach} for {Predicting} {Efficiency} of {NBA} {Players}},
     journal = {Yugoslav journal of operations research},
     pages = {347 },
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Sandro Radovanović; Milan Radojičić; Gordana Savić. Two-Phased DEA-MLA Approach for Predicting Efficiency of NBA Players. Yugoslav journal of operations research, Tome 24 (2014) no. 3, p. 347 . http://geodesic.mathdoc.fr/item/YJOR_2014_24_3_a3/