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  • 标题:Two-phased DEA-MLA approach for predicting efficiency of NBA players
  • 本地全文:下载
  • 作者:Radovanović Sandro ; Radojičić Milan ; Savić Gordana
  • 期刊名称:Yugoslav Journal of Operations Research
  • 印刷版ISSN:0354-0243
  • 电子版ISSN:1820-743X
  • 出版年度:2014
  • 卷号:24
  • 期号:3
  • 页码:347-358
  • DOI:10.2298/YJOR140430030R
  • 出版社:Faculty of Organizational Sciences, Belgrade, Mihajlo Pupin Institute, Belgrade, Economics Institute, Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Faculty of Mechanical Engineering, Belgrade
  • 摘要:

    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%.

  • 关键词:data envelopment analysis; efficiency analysis; predictive analytics; machine learning
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