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Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited

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  • Vagenas, George
  • Vlachokyriakou, Eleni

Abstract

The present study revisited the problem of estimating Olympic success by critical demo-economic indicators. The sample consisted of the 75 winner countries at the Athens 2004 Olympic Games (not previously analyzed). Medal totals were log-linearly regressed on land, population, GDP, urban population, inflation, growth rate, unemployment, labor force, health expenditures, ex-host, and team size. Multiple regression assumptions were tested with proper diagnostics including collinearity. Olympic team size was the best single predictors of Olympic medals (R2=0.690, p<0.001), and as an alternative criterion variable was significantly regressed on population, growth rate, health expenditure, and unemployment (R2=0.563, p<0.001). Medal totals were significantly regressed on population, ex-host, health expenditure, growth rate, and unemployment (R2=0.541, p<0.001). The classical population-GDP model extracted only 28% of the variance in total medals (R2=0.277, p<0.001), and this was slightly improved when combined with unemployment (R2=0.365, p<0.001). It appears that the size of the Olympic team plays the role of transmitting the composite impact of a country's size and economy to the end-phase of Olympic success. Winning Olympic medals depends on the combined potential of population, wealth, growth rate, unemployment, ex-host, and social-sport expenditures. Larger and wealthier countries win more medals by “producing” larger Olympic teams as a result of possessing more athletic talents and better support for social and sport related activities.

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  • Vagenas, George & Vlachokyriakou, Eleni, 2012. "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 Management Review, Elsevier, vol. 15(2), pages 211-217.
    • Handle: RePEc:eee:spomar:v:15:y:2012:i:2:p:211-217
    DOI: 10.1016/j.smr.2011.07.001
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    5. 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.
    6. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. 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.
    8. Luis Antonio Andrade Rosas, 2019. "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, Nóesis. Revista de Ciencias Sociales y Humanidades, vol. 28, pages 15-37, 55.
    9. Emilio Gómez-Déniz & Nancy Dávila-Cárdenes & Alejandro Leiva-Arcas & María J. Martínez-Patiño, 2021. "Measuring Efficiency in the Summer Olympic Games Disciplines: The Case of the Spanish Athletes," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    10. Choi, Hyeseung & Woo, Hyungsoo & Kim, Ji-Hyun & Yang, Jae-Suk, 2019. "Gravity model for dyadic Olympic competition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 447-455.
    11. Yun Hyeong Choi & Qingyuan Wei & Luyao Zhang & Seong-Jin Choi, 2022. "The Impact of Cultural Distance on Performance at the Summer Olympic Games," SAGE Open, , vol. 12(1), pages 21582440221, March.
    12. M. Flegl & L. A. Andrade, 2018. "Measuring countries’ performance at the Summer Olympic Games in Rio 2016," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 823-846, November.

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