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Measuring Olympics achievements based on a parallel DEA approach

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  • Xiyang Lei
  • Yongjun Li
  • Qiwei Xie
  • Liang Liang

Abstract

Measuring the performance of participating nations in the Olympic Games is an important application of data envelopment analysis (DEA). Prior literature only considers participating nations’ performance in the Summer Olympic Games. It may be unfair to some nations who are good at the Winter Olympics, but poor at the Summer Olympics. Therefore, we believe it is better to consider the two Olympics together when measuring performance of participants. This paper treats the two Olympics as a parallel system in which each subsystem corresponds to a Summer Olympics or a Winter Olympics, and extends a parallel DEA approach to evaluate the efficiency of each participant. An efficiency decomposition procedure is proposed to obtain the efficiency rang of each Olympic subsystem. Finally, we apply the proposed approach to the latest real data set of the 2012 Summer Olympics and 2010 Winter Olympics. Copyright Springer Science+Business Media New York 2015

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  • Xiyang Lei & Yongjun Li & Qiwei Xie & Liang Liang, 2015. "Measuring Olympics achievements based on a parallel DEA approach," Annals of Operations Research, Springer, vol. 226(1), pages 379-396, March.
    • Handle: RePEc:spr:annopr:v:226:y:2015:i:1:p:379-396:10.1007/s10479-014-1708-1
    DOI: 10.1007/s10479-014-1708-1
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    Cited by:

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    2. Meng, Fanyong & Xiong, Beibei, 2021. "Logical efficiency decomposition for general two-stage systems in view of cross efficiency," European Journal of Operational Research, Elsevier, vol. 294(2), pages 622-632.
    3. Färe, Rolf & Karagiannis, Giannis, 2017. "The denominator rule for share-weighting aggregation," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1175-1180.
    4. Yongjun Li & Wenhui Hou & Weiwei Zhu & Feng Li & Liang Liang, 2021. "Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs," Annals of Operations Research, Springer, vol. 304(1), pages 233-261, September.
    5. Qingxian An & Fanyong Meng & Beibei Xiong, 2018. "Interval cross efficiency for fully ranking decision making units using DEA/AHP approach," Annals of Operations Research, Springer, vol. 271(2), pages 297-317, December.
    6. Pedro Garcia‐del‐Barrio & Carlos Gomez‐Gonzalez & José Manuel Sánchez‐Santos, 2020. "Popularity and Visibility Appraisals for Computing Olympic Medal Rankings," Social Science Quarterly, Southwestern Social Science Association, vol. 101(5), pages 2137-2157, September.
    7. Chenpeng Feng & Rong Zhou & Jingjing Ding & Xiangze Xiao & Mingyue Pu, 2023. "A Method for Allocation of Carbon Emission Quotas to Provincial-Level Industries in China Based on DEA," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    8. Calzada-Infante, Laura & Lozano, Sebastián, 2016. "Analysing Olympic Games through dominance networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1215-1230.
    9. Sebastián Lozano, 2017. "Technical and environmental efficiency of a two-stage production and abatement system," Annals of Operations Research, Springer, vol. 255(1), pages 199-219, August.
    10. Alexandre de Cássio Rodrigues & Carlos Alberto Gonçalves & Tiago Silveira Gontijo, 2019. "A two-stage DEA model to evaluate the efficiency of countries at the Rio 2016 Olympic Games," Economics Bulletin, AccessEcon, vol. 39(2), pages 1538-1545.
    11. Sebastián Lozano & Gabriel Villa, 2023. "Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games," Annals of Operations Research, Springer, vol. 322(2), pages 879-919, March.
    12. Sekitani, Kazuyuki & Zhao, Yu, 2021. "Performance benchmarking of achievements in the Olympics: An application of Data Envelopment Analysis with restricted multipliers," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1202-1212.
    13. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    14. Xie, Qiwei & Xu, Qifan & Zhu, Da & Rao, Kaifeng & Dai, Qianzhi, 2020. "Fair allocation of wastewater discharge permits based on satisfaction criteria using data envelopment analysis," Utilities Policy, Elsevier, vol. 66(C).
    15. Wenli Liu & Ying-Ming Wang & Shulong Lv, 2017. "An aggressive game cross-efficiency evaluation in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 241-258, December.
    16. Julio Cesar Mosquera Gutierres & Rafael Coradi Leme & Rodrigo Luiz Mendes Mota & Paulo E. Steele Santos, 2021. "Regulatory efficiency decomposition for utilities’ parallel subsystems," Operational Research, Springer, vol. 21(1), pages 331-347, March.
    17. Villa, G. & Lozano, S., 2016. "Assessing the scoring efficiency of a football match," European Journal of Operational Research, Elsevier, vol. 255(2), pages 559-569.

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