Two-phased DEA-MLA approach for predicting efficiency of NBA players
Апстракт
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%.
Кључне речи:
predictive analytics / machine learning / efficiency analysis / data envelopment analysisИзвор:
Yugoslav Journal of Operations Research, 2014, 24, 3, 347-358Издавач:
- Univerzitet u Beogradu - Fakultet organizacionih nauka, Beograd, i dr.
Институција/група
Fakultet organizacionih naukaTY - JOUR AU - Radovanović, Sandro AU - Radojičić, Milan AU - Savić, Gordana PY - 2014 UR - https://rfos.fon.bg.ac.rs/handle/123456789/1242 AB - 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%. PB - Univerzitet u Beogradu - Fakultet organizacionih nauka, Beograd, i dr. T2 - Yugoslav Journal of Operations Research T1 - Two-phased DEA-MLA approach for predicting efficiency of NBA players EP - 358 IS - 3 SP - 347 VL - 24 DO - 10.2298/YJOR140430030R UR - conv_217 ER -
@article{ author = "Radovanović, Sandro and Radojičić, Milan and Savić, Gordana", year = "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%.", publisher = "Univerzitet u Beogradu - Fakultet organizacionih nauka, Beograd, i dr.", journal = "Yugoslav Journal of Operations Research", title = "Two-phased DEA-MLA approach for predicting efficiency of NBA players", pages = "358-347", number = "3", volume = "24", doi = "10.2298/YJOR140430030R", url = "conv_217" }
Radovanović, S., Radojičić, M.,& Savić, G.. (2014). Two-phased DEA-MLA approach for predicting efficiency of NBA players. in Yugoslav Journal of Operations Research Univerzitet u Beogradu - Fakultet organizacionih nauka, Beograd, i dr.., 24(3), 347-358. https://doi.org/10.2298/YJOR140430030R conv_217
Radovanović S, Radojičić M, Savić G. Two-phased DEA-MLA approach for predicting efficiency of NBA players. in Yugoslav Journal of Operations Research. 2014;24(3):347-358. doi:10.2298/YJOR140430030R conv_217 .
Radovanović, Sandro, Radojičić, Milan, Savić, Gordana, "Two-phased DEA-MLA approach for predicting efficiency of NBA players" in Yugoslav Journal of Operations Research, 24, no. 3 (2014):347-358, https://doi.org/10.2298/YJOR140430030R ., conv_217 .