, Volume 55, Issue 3–4, pp 823–846 | Cite as

Measuring countries’ performance at the Summer Olympic Games in Rio 2016

  • M. FleglEmail author
  • L. A. Andrade
Application Article


Summer Olympic games in Rio 2016 were the biggest and the most important sport event in 2016. Athletes’ performance at Olympics is always of a high interest and serve as a basis for various parametric and non-parametric analyses. In this article, we construct data envelopment analysis model to analyze countries’ performance in Summer Olympic games in Rio 2016. The traditional model structure is based on GDP-population theory. In this article, we go beyond this traditional model structure and introduce economic active population and corruption factors into the model. Similarly, the Olympic success is measured regarding medal ranking of each country. Nevertheless, we enlarge traditional golden, silver and bronze medals output structure, including medal ranking up to 8th position. This model structure enables us to also measure performance of lower performed countries that are traditionally not ranked in the medal rankings. As a complement to the achieved results, we decompose the results regarding World Bank’s income classification to be able to make conclusion of countries’ performance.


Corruption Data envelopment analysis Economic active population Income classification Rio 2016 Sport performance 



The authors would like to thank to La Salle University in México City, Mexico for the support in carrying out this work, which was done under university grant projects.


  1. 1.
    Rio. Rio 2016—athletics schedule and results. (2016). Accessed 21 Sept 2016
  2. 2.
    Müller, M.: What makes an event a mega-event? Definitions and sizes. Leisure Stud. 34(6), 627–642 (2015). CrossRefGoogle Scholar
  3. 3.
    Kasimati, E., Dawson, P.: Assessing the impact of the 2004 Olympic Games on the Greek economy: a small macroeconometric model. Econ. Model. 26(1), 139–146 (2009). CrossRefGoogle Scholar
  4. 4.
    Atkinson, G., Mourato, S., Szymanski, S., Ozdemiroglu, E.: Are we willing to pay enough to ‘back the bid’? Valuing the intangible impacts of London’s bid to host the 2012 Summer Olympic Games. Urban Stud. 45(2), 419–444 (2008). CrossRefGoogle Scholar
  5. 5.
    Walton, H., Longo, A., Dawson, P.: Contingent valuation of 2012 London Olympics: a regional perspective. J. Sports Econ. 9(3), 304–317 (2008). CrossRefGoogle Scholar
  6. 6.
    End, C.M., Dietz-Uhler, B., Harrick, E.A., Jacquemotte, L.: Identifying with winners: a reexamination of sport fans’ tendency to BIRG. J. Appl. Soc. Psychol. 32, 1017–1030 (2002)CrossRefGoogle Scholar
  7. 7.
    Davis, M.C., End, ChM: A winning proposition: the economic impact of successful National Football League franchises. Econ. Inq. 48(1), 39–50 (2010)CrossRefGoogle Scholar
  8. 8.
    Lozano, S., Villa, G., Guerrero, F., Cortés, P.: Measuring the performance of nations at the Summer Olympics using data envelopment analysis. J. Oper. Res. Soc. 53(5), 501–511 (2002). CrossRefGoogle Scholar
  9. 9.
    Li, Y., Lei, X., Dai, Q., Liang, L.: Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis. Eur. J. Oper. Res. 243(3), 964–973 (2015). CrossRefGoogle Scholar
  10. 10.
    Li, Y., Liang, L., Chen, Y., Morita, H.: Models for measuring and benchmarking Olympics achievements. Omega 36, 933–940 (2008). CrossRefGoogle Scholar
  11. 11.
    Zhang, D., Li, X., Meng, W., Liu, W.: Measuring the performance of nations at the Olympic Games using DEA models with different preferences. J. Oper. Res. Soc. 60(7), 983–990 (2009). CrossRefGoogle Scholar
  12. 12.
    Wu, J., Liang, L., Yang, F.: Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method. Eur. J. Oper. Res. 197, 722–730 (2009). CrossRefGoogle Scholar
  13. 13.
    Churilow, L., Flitman, A.: Towards fair ranking of Olympic achievements: the case of Sydney 2000. Comput. Oper. Res. 33, 2057–2082 (2006). CrossRefGoogle Scholar
  14. 14.
    Bernard, A.B., Busse, M.R.: Who wins the Olympic games: economic resources and medal totals. Rev. Econ. Stat. 86(2), 413–417 (2004). CrossRefGoogle Scholar
  15. 15.
    Vagenas, G., Vlachokyriakou, E.: Olympic medals and demo-economic factors: novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited. Sport Manag. Rev. 15(2), 211–217 (2012). CrossRefGoogle Scholar
  16. 16.
    Potts, T.: Governance, corruption and Olympic success. Appl. Econ. 46(31), 3882–3891 (2014). CrossRefGoogle Scholar
  17. 17.
    Masters, A.: Corruption in sport: from the playing field to the field of policy. Policy Soc. 34, 111–123 (2015). CrossRefGoogle Scholar
  18. 18.
    Transparency International. 2016. Global corruption report: sport. Routledge. ISBN: 978-1-315-69570-9. Accessed 25 Nov 2016
  19. 19.
    Graycar, A.: Corruption: classification and analysis. Policy Soc. 34, 87–96 (2015). CrossRefGoogle Scholar
  20. 20.
    Gorse, S., Chadwick, S.: The prevalence of corruption in international sport: a statistical analysis. Centre for the International Business of Sport, Coventry. (2011). Accessed 1 Dec 2016
  21. 21.
    Cooper, W.W., Seiford, L.M., Zhu, J.: Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, vol. 164, 2nd edn. Springer, Berlin (2011). CrossRefGoogle Scholar
  22. 22.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978). CrossRefGoogle Scholar
  23. 23.
    Sexton, T.R., Silkman, R.H., Hogan, A.J.: Data Envelopment Analysis: Critique and Extensions. Measuring Efficiency: An Assessment of Data Envelopment Analysis, vol. 32, pp. 73–105. Jossey-Bass, San Francisco (1986)Google Scholar
  24. 24.
    World Bank.: World Bank open data. World Bank. (2016). Accessed 15 Oct 2016
  25. 25.
    Transparency International.: Corruption Perception Index. (2016). Accessed 10 Oct 2016
  26. 26.
    Andrade Rosas, L.A., Flegl, M.: Quantitative and qualitative impact of GDP on sport performance and its relation with corruption and other social factors. Nóesis Revista de Ciencias Sociales y Humanidades 28(55), 15–37 (2019). CrossRefGoogle Scholar
  27. 27.
    Flegl, M., Andrade, L.: Rio 2016—Olympic Sport Economic Data (2016).

Copyright information

© Operational Research Society of India 2018

Authors and Affiliations

  1. 1.Facultad de NegociosUniversidad La Salle MéxicoMexico CityMexico
  2. 2.Facultad de NegociosUniversidad La Salle MéxicoMexico CityMexico

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