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Dynamic Network DEA approach to basketball games efficiency

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  • G. Villa
  • S. Lozano

Abstract

Although Data Envelopment Analysis (DEA) has been widely applied to sports, not many studies are related to basketball. Two types of approaches have been developed to measure the efficiency in basketball so far: those focused on the assessment of players and those that assess the performance of the teams. Assuming that the number of points scored in a basketball game greatly influences the appeal of a game, in this paper, a new approach focused on the measurement of the scoring efficiency of the two teams that play a game is addressed. To do that, the performance of each team in each quarter and the carry-overs between successive quarters must be taken into account. This leads to a Dynamic Network DEA model with two subprocess (corresponding to the home and visitor teams) running in each quarter. A scoring efficiency can be computed for each team in each quarter as well as for each team overall, for each quarter overall and for the whole game. The proposed approach is applied to the matches played during the 2014–2015 NBA season.

Suggested Citation

  • G. Villa & S. Lozano, 2018. "Dynamic Network DEA approach to basketball games efficiency," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1738-1750, November.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:11:p:1738-1750
    DOI: 10.1080/01605682.2017.1409158
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    Cited by:

    1. Qingyou Yan & Fei Zhao & Xu Wang & Guoliang Yang & Tomas Baležentis & Dalia Streimikiene, 2019. "The network data envelopment analysis models for non-homogenous decision making units based on the sun network structure," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 1221-1244, December.
    2. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    3. Dina A. M. Miragaia & João J. M. Ferreira & Cédric T. Vieira, 2024. "Efficiency of Non-profit Organisations: a DEA Analysis in Support of Strategic Decision-Making," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3239-3265, March.
    4. Chia-Ming Chang & Li-Wei Liu & Huey-Hong Hsieh & Ko-Chia Chen, 2020. "A Multilevel Analysis of Organizational Support on the Relationship between Person-Environment Fit and Performance of University Physical Education Teachers," IJERPH, MDPI, vol. 17(6), pages 1-17, March.
    5. Che-Wei Chang, 2022. "Developing a Multicriteria Decision-Making Model Based on a Three-Layer Virtual Internet of Things Algorithm Model to Rank Players’ Value," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    6. Del Barrio-Tellado, María José & Gómez-Vega, Mafalda & Gómez-Zapata, Jonathan Daniel & Herrero-Prieto, Luis César, 2021. "Urban public libraries: Performance analysis using dynamic-network-DEA," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    7. Wei Yin & Zhixiao Ye & Wasi Ul Hassan Shah, 2023. "Indices Development for Player’s Performance Evaluation through the Super-SBM Approach in Each Department for All Three Formats of Cricket," Sustainability, MDPI, vol. 15(4), pages 1-20, February.

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