Economic development as major determinant of Olympic medal wins: predicting performances of Russian and Chinese teams at Sochi Games

Th is paper examines determinants of women's participation and performance in the Olympics. Female inclusion and success are not merely functions of size, wealth, and host advantage, but a more complex process involving the socioeconomic status of women and, more weakly, broad societal attitudes on gender issues. Female labor force participation and educational attainment in particular are tightly correlated with both participation and outcomes, even controlling for per capita income. Female educational attainment is strongly correlated with both the breadth of participation across sporting events and success in those events. Host countries and socialist states also are associated with unusually high levels of participation and medaling by female athletes. Medal performance is aff ected by large-scale boycotts. Opening competition to professionals may have leveled the playing fi eld for poorer countries. But the historical record for women's medal achievement is utterly distorted by the doping program in the former East Germany, which specifi cally targeted women. At its peak in the 1970s and 1980s, the program was responsible for 17 percent of the medals awarded to women, equivalent to the medal hauls of the Soviet or American team in 1972, the last Olympics not marred by widespread abuse of performance-enhancing drugs.JEL Codes: J16, L83, F53, Z13

Section: Participation and Performancecontrasting

confidence: 79%

contrasting

confidence: 72%

mentioning

confidence: 99%

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What Goes into a Medal: Women's Inclusion and Success at the Olympic Games*

Noland

Stahler

2015

“…Otamendi and Doncel (2018) raised the issue of whether the medal win distribution is better anticipated by forecasting models or by sports experts who have a deep knowledge of the different Olympic sport disciplines. They compared five expert predictions published in the press with three forecasting models, respectively, used for the 2010 Vancouver Winter Olympics (Otamendi and Doncel, 2014a), the 2012 London Summer Olympics (Otamendi and Doncel, 2014b), and the 2014 Sochi Winter Games (Andreff, 2013). Relying on indicators to test the performance of a forecast such as a ratio of exactly predicted results, Pearson, Kendall, and Spearman correlations adjusting the forecast of ex ante statistical distribution to the ex post observed statistical distribution, the authors concluded that sports experts’ predictions are more accurate as regards the detailed medal distribution within a given sport discipline while econometric models perform better when it comes to medal wins distribution across participating nations.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting National Medal Totals at the Summer Olympic Games Reconsidered

Scelles

Andreff

Bonnal

et al. 2020

Self Cite

Objective This article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English. Method Econometric testing not only resorts to a Tobit model as usual but also to a Hurdle model. Results Most variables have a significant impact on national team medal totals; it appears to be negative for most regions other than North America except Western Europe and Oceania (not significant). Then, two models (Tobit and Hurdle) are implemented to forecast national medal totals at the 2016 and 2020 Summer Olympics. Conclusion Both models are complementary for the 2016 forecast. The 2020 forecast is consistent with Olympic Medals Predictions, although some striking differences are found.

“…The literature contains several references on this topic as previously stated. Some refer to the Winter Games (Kuper and Sterken, ; Andreff, ; Otamendi and Doncel, ; Pfau, ), others to the Summer Games (Forrest, Sanz, and Tena, ; Otamendi and Doncel, ), and others to both editions of the Games (Johnson and Ali, ). We adopted a model that predicts the outcome for each sport.…”

Section: Expected Socioeconomic Potential: Sport‐agnostic Econometricmentioning

confidence: 99%

Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Otamendi

Doncel

Martín‐Gutiérrez

2020

Objective Develop a framework to measure the extent to which countries meet their performance expectations at major sporting events using economists, experts, and fan knowledge. Method Long‐term expectations based on socioeconomic potential are calculated using sport‐agnostic econometric modeling. Short‐term expectations based on performance and competitiveness are calculated using betting odds, which incorporate both expert knowledge and “wisdom of the crowd.” Robust statistics based on the chi‐squared distribution are used to measure the extent to which countries’ expectations are met. Our method is tested using real data from the Rio 2016 Summer Olympic Games. Results Twelve countries over‐ or underperformed in terms of meeting either long‐ or short‐term expectations. Conclusions Uzbekistan was the only country that exceeded both long‐ and short‐term expectations.