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Performance Measurement of Major League Baseball Teams Using Network DEA

In: Data Envelopment Analysis

Author

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  • Herbert F. Lewis

    (Stony Brook University)

Abstract

Data envelopment analysis (DEA) has been extensively applied to measure the performance of individual athletes and teams in a variety of sports as well as to analyze nations competing in the Olympics. Most of the models presented in the literature are single-stage DEA models which treat the underlying process of converting inputs into outputs as a “black box.” In many situations, analysts are interested in investigating the sources of inefficiency within the organization in order to improve organizational performance. To accomplish this, researchers have developed two-stage and network DEA methodologies. In this chapter, we model an MLB team as comprised of a front office operation which consumes money in the form of player salaries to acquire offensive and defensive talent and an on-field operation which uses the talent to outscore opponents and win games. We present a network DEA methodology to measure performance of the front office operation, the on-field operation, and the overall team. Finally, we conduct two industry-wide studies of Major League Baseball which utilize the network DEA methodology.

Suggested Citation

  • Herbert F. Lewis, 2014. "Performance Measurement of Major League Baseball Teams Using Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 475-535, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-8068-7_20
    DOI: 10.1007/978-1-4899-8068-7_20
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    Citations

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    Cited by:

    1. Qing Zhu & Renxian Zuo & Yuze Li & Shan Liu, 2021. "A system evaluation of NBA rookie contract execution efficiency with stacked Autoencoder and hybrid DEA," Operational Research, Springer, vol. 21(4), pages 2771-2807, December.
    2. Abdullah Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    3. Abdullah Korkut Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    4. Arnab Adhikari & Adrija Majumdar & Gaurav Gupta & Arnab Bisi, 2020. "An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: evidence from cricket," Annals of Operations Research, Springer, vol. 284(1), pages 1-32, January.
    5. 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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