Measuring Olympics achievements based on a parallel DEA approach
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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.
KeywordsData envelopment analysis Olympics achievements Parallel structure Efficiency decomposition
The authors thank the editor-in-chief Professor Endre Boros and the reviewers for their constructive comments and suggestions, which have helped to improve the quality of this paper. The authors also thank Professor Alec Morton for his valuable suggestions and his technical check on this paper. This research is supported by National Natural Science Foundation of China under Grants (No. 61101219, 71271196), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 71121061), the Fund for International Cooperation and Exchange of the National Natural Science Foundation of China (No. 71110107024) and the Fundamental Research Funds for the Central Universities.
- De França, J. M. F., de Figueiredo, J. N., & dos Santos Lapa, J. (2010). A DEA methodology to evaluate the impact of information asymmetry on the efficiency of not-for-profit organizations with an application to higher education in Brazil. Annals of Operations Research, 173(1), 39–56.CrossRefGoogle Scholar
- Du, J., Liang, L., Chen, Y., Cook, W. D., & Zhu, J. (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2), 390–397.Google Scholar
- Du, J., Wang, J., Chen, Y., Chou, S. Y., & Zhu, J. (2014). Incorporating health outcomes in Pennsylvania hospital efficiency: An additive super-efficiency DEA approach. Annals of Operations Research, 221, 161–172.Google Scholar
- Hai, H. L. (2007). Using vote-ranking and cross-evaluation methods to assess the performance of nations at the Olympics. WSEAS Transactions on Systems, 6(6), 1196–1198.Google Scholar
- Khodabakhshi, M., & Aryavash, K. (2014). The fair allocation of common fixed cost or revenue using DEA concept. Annals of Operations Research, 214(1), 187–194.Google Scholar
- Moreno, P., & Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research, 214(1), 99–124.Google Scholar
- Yang, C. H., Lin, H. Y., & Chen, C. P. (2014). Measuring the efficiency of NBA teams: Additive efficiency decomposition in two-stage DEA. Annals of Operations Research, 217(1), 565–589.Google Scholar
- Yang, Y., Ma, B., & Koike, M. (2000). Efficiency-measuring DEA model for production system with k independent subsystems. Journal of the Operations Research Society of Japan-Keiei Kagaku, 43(3), 343–354.Google Scholar