1 Differences between geographic areas (continents) in the distribution of medals at the Beijing Olympic Games 2008 and at the London Olympic Games. Prokopios Chatzakis, National and Kapodistrian University of Athens, Faculty of Physical Education and Sport Science 1 Abstract The primary purpose of this study was to investigate differences between geographic areas (continents) in the number of medals won at the Beijing Olympic Games 2008 and the London Olympic Games. Secondly, given the world economic crisis between 2008 and, a possible difference in the distribution of medals between the two Olympic Games was investigated. The data were analyzed using the nonparametric Kruskal-Wallis ANOVA and potential follow-up Mann-Whitney tests. The results showed that there are significant differences between the 5 geographic areas in the total medals won at the Beijing Olympic Games (p=0.01), as well as at the London Olympic Games (p=0.007). Significant differences were also found between the 5 continents in the distribution of silver medals in both Olympic Games (0.014 and 0.028, respectively) and of bronze medals in the London Olympic Games (p=0.004). These differences were mainly due to differences between Europe and Africa, and due to specific differences in the distribution of medals between some of the geographic areas. There were no significant differences between the 2008 and the Olympic Games in the number of medals won. The hypothesis of the world economic crisis having an impact on the number of medals won at the Games compared to the 2008 Games was not confirmed. 1 Under the supervision of Professor George Vagenas, Sports Statistics, Faculty of Physical Education and Sport Science, National and Kapodistrian University of Athens. Introduction The Modern Olympic Games started in 1896 in Athens and since then they are organized every four years in various countries. Baron Pierre de Coubertin founded the International Olympic Committee in 1894 in an effort to revive the ancient Olympic Games. Since then 30 Summer and 22 Winter Olympic Games have been
2 organized and the Olympic Movement seems to have an enormous growth. That is why the Olympic Games have become the subject of study and many papers have been published trying to explain various aspects of this institution. Evidence in the existing literature show that social, economic and geographic factors influence sports systems and ultimately affect the number of medals that a country will win in the Olympic Games. In a recent paper about socioeconomic factors that may affect the participation and medal counts in the Olympic Games, the researchers hypothesized that political and economic variables such as Gross Domestic Product (GDP), population and political system may have an influence on the numbe r of athletes sent and medals won. According to Johnson & Ali (2004), these hypotheses are based on the fact that high GDP may offer better opportunities and better training conditions, as well as a large population means a larger pool of potential athletes from which to select successful contenders. Regarding certain political systems, for example single party or communism, have a specific approach to this type of sports competition and may have different results compared with other political systems. The results indeed showed that the richest countries send more female athletes, while those with the largest population send much more athletes than the other countries. Furthermore, the host country and its neighbors seem to have a bigger participation in the Olympic Games. Contrariwise, the political system of a country doesn t seem to have a great effect on participation levels. The factors analyzed above also, especially the country s wealth and population, seem to have a great effect on the number of medals won in the Olympic Games according to the findings (Johnson & Ali, 2004). Rathke and Woitek (2008) also consider GDP and population size as two very important variables that can be used to predict or justify the Olympic success and achievements of certain countries. Although the impact of GDP is always positive concerning the medals share, the population size can only have an impact when a country is wealthy, too. It is possible that these findings are a result of the financial support and investments, the organization and advanced training methods, even the culture of the country. In addition, a group of researchers have analyzed the medal shares at Olympic Games of Beijing (2008) and tried to measure the success of each country depending
3 on their GDP and number of gold, silver and bronze medals. The authors mention Gross National Product, the political system, climate, relative population size and hosting activity as the main predicting factors of success and performance in the Olympic Games. By using the Data Envelopment Analysis (DEA) they evaluated the efficiency of the countries and they compared their results with their potentialities (Wu, Zhou, & Liang, 2010). An analysis of Winter Olympic Games identified some extra determinants of Olympic success (Otamendi & Doncel, 2014). Based on the results of the Winter Olympic Games of Vancouver (2010), these factors are the geography in relation to the climate and the tradition of the country in certain sports. Moreover, if a country wants to improve its results and medal share must have a well developed talent identification system, which is usually found in economically, socially and politically developed countries. As for the Paralympics the determinants of successful performance in the Games seem to be almost the same with the Summer Olympic Games. GDP per capita, population size, being the host country of the Paralympics and being a former communist country are positively related to the success and the performance of the countries at the Games, calculated by the medals share. Furthermore, deviations from the ideal weather conditions (for example if the climate of the country is very hot or cold) have a negative influence at the medal count of the country. These findings confirm that in every case the same variables have a great influence at the medals share, and those are the wealth of the country (if a country is rich, the athletes will have all the benefits, the equipment and the support they need, etc.), the number of the athletes that participate (the biggest the population is the more athletes will participate usually, especially if the country is wealthy enough), the climatic conditions and other geographic and political factors (Buts, Du Bois, Heyndels, & Jegers, 2011). Another interesting study has examined the World Records development through a geopolitical prism. Countries were distributed in geographical world regions, which are, North America, Western and Eastern Europe, Russia, Oceania, China, North Pacific, Africa, Asia, Caribbean and South America. As the authors say, the WR by geographic region is linearly related to Olympic medals won. The two major geographic regions that hold most of the WR and Olympic medals are North America
4 and Western Europe. All these facts clearly show that economic and geopolitical factors, as well as the development of the country or the geographic region that is being studied, can strongly affect the possibility of the attainment of a WR or a good medals share at the Olympic Games (Guillaume et al., 2009). The primary purpose of this study was to investigate whether there are significant differences between the geographic areas (continents) in the number of Olympic medals obtained at the Olympic Games of Beijing (2008) and London (). A secondary purpose was to examine the potential impact of the world econ omic crisis on the London Games in the Olympic medals distribution. Methods The sample consisted of the gold, silver, bronze and total medals of the 99 countries that won at least 1 medal in any one of these two Olympic Games. The data were obtained from the official website of the International Olympic Committee (IOC). These countries were grouped and classified in the following geographic areas continents: (1) North America, (2) South America, (3) Europe, (4) Asia and Oceania and (5) Africa. This classification is different from the one of Guillaume et al. (2009), but it is quite similar to these of many World Sports Federations, such as FIFA, IAAF etc, with some small alterations. The classification of the geographic regions of this study tries to take into account geopolitical, socioeconomic and other important factors analyzed above, in order to avoid methodological errors. To investigate potential differences between the 5 geographic areas non parametric analysis of variance using Kruskal-Wallis and meta ANOVA Mann Whitney tests were performed. These types of tests were selected as the data were highly skewed (asymmetric). The difference between the 2008 and Olympic Games in terms of medals won was tested with the non parametric Wilcoxon Signed Ranks Test for Dependent Samples. The statistical program used for the analyses was IBM SPSS Statistics 22 and statistical significance was tested at the a=0.05.
5 Results As it appears in Table 1, the results showed that there are significant differences between the 5 geographic areas in the total medals won at the Olympic Games of Beijing (p=0.01), as well as at the Olympic Games in London (p=0.007). There were also statistically significant differences between the 5 continents in the distribution of silver medals in both Olympic Games (0.014 and 0.028, respectively) and of bronze medals in the London Olympic Games (p=0.004). Also, the difference between the 5 continents in the distribution of Gold medals at the Beijing Olympic Games was marginally not significant (p=0.058). The rest of the comparisons were not statistically significant. Table 1 Differences in Olympic Medals between the 5 Continents Olympic Games Olympic Games GOLD SILVER BRONZE TOTAL GOLD SILVER BRONZE TOTAL Chi-Square 9,128 12,438 8,239 13,277 6,683 10,903 15,363 14,057 df Asymp. Sig.,058,014,083,010,154,028,004,007 a. Kruskal Wallis Test b. Grouping Variable: GEO.AREA The Mann Whitney tests showed that the observed differences are mainly due to differences between Europe and Africa, while there are other specific differences in the distribution of medals between some of the geographic areas. As shown in table 2, Europe and Africa differ significantly in all medal categories, specifically in the number of gold, silver, bronze and total medals won in both Olympic Games, with Europe predominating in every case. Furthermore, Europe has significantly better distribution of silver medals won in both Olympic Games than Asia & Oceania (p=0.034 and p=0.042, respectively), while Asia & Oceania have better distribution of bronze and total medals won at the London Olympic Games, compared with Africa.
6 Mann-Whitney Test Table 2 Pairwise Comparisons between Continents Olympic Games Olympic Games GOLD SILVER BRONZE TOTAL GOLD SILVER BRONZE TOTAL N.A. S.A.,393,696,767,776,543,559,695,450 N.A. - Europe,414,275,130,132,687,353,507,390 N.A. Asia & Oceania,616,812,477,817,240,473,610,828 N.A. - Africa,121,265,599,276,073,109,063,026 S.A. - Europe,104,062,382,090,356,150,299,165 S.A. Asia & Oceania,554,699,822,707,716,871,396,494 S.A. - Africa,858,494,324,332,348,543,248,423 Europe Asia & Oceania,090,034,449,069,119,042,403,145 Europe - Africa,008,001,004,000,023,002,000,000 Asia & Oceania - Africa,157,177,066,086,505,247,000,013 a. Grouping Variable: GEO.AREA As for the examination of the second hypothesis, that the world economic crisis between 2008 and, might have an impact on the London Games compared to the Beijing 2008 Games, the non parametric Wilcoxon Signed Ranks Test for Dependent Samples showed that there were no significant changes in the distribution of medal shares between the two Games in all 5 geographic regions examined together (table 3). Table 3 Comparison of the two Olympic Games in all medals categories. TOTAL - TOTAL2008 GOLD - GOLD2008 SILVER - SILVER2008 BRONZE - BRONZE2008 Z -,135 b -,296 b -,190 b -,540 c Asymp. Sig. (2-tailed),893,767,849,589 a. Wilcoxon Signed Ranks Test / b. Based on positive ranks. / c. Based on negative ranks.
7 Discussion & Conclusions As previously mentioned, there are various factors that can affect the performance of the countries at the Olympic Games, assessed by the total number of medals won. Countries with a great evolution in many aspects of science, sports, economy, human development and society in general, will manage to reach superiority in sports performance. So, it is true that the world s economic giants govern the sports world, too (Sotudeh, Salesi, Didegah, & Bazgir, ). This can be also confirmed by the fact that USA, China, Russia and Great Britain, four of the biggest economies in the world took the 4 first places of the medal shares, by collecting the 34% and the 35 % of the total medals in both Olympic Games. More specifically, USA collected 12% and 11%, China collected 10% and 9%, Russia collected 8% and 9% and Great Britain collected 5% and 7% of the total medals at the Olympic Games of Beijing (2008) and the Olympic Games of London (), respectively. There is also a great interest in the analysis of these percentages, as two of the countries mentioned above happen to be the host countries of the last two Olympic Games, and the literature show that the host factor has a positive impact on the medals shared by the host country. The results of the present study are discussed in light of previous relevant research, which provides some evidence on the differences between the geographic areas in terms of economic status, social characteristics, and climatic conditions, which seem to have an impact on their differences on Olympic performance. The hypothesis that the world economic crisis would had an impact on the Olympic Games compared to the 2008 Olympic Games was not confirmed, possibly due to the fact that the crisis that began internationally around this period was unable to cause immediate negative effects on the Olympic Games, as most of the countries had already organized their Olympic preparation, funding, and all supporting investments, which are usually planned years prior to each Olympic Games. The results appear to have some practical usefulness in terms of explaining differentially Olympic success in terms of geographical specificities
8 relevant to specialization in certain categories of sports under specific climatic conditions. References Buts, C., Du Bois, C., Heyndels, B., & Jegers, M. (2011). Socioeconomic Determinants of Success at the Summer Paralympics. Journal of Sports Economics, 14(2), Guillaume, M., El Helou, N., Nassif, H., Geoffroy, B., Len, S., Thibault, V., et al. (2009). Success in Developing Regions: World Records Evolution through a Geopolitical Prism. PLoS ONE, 4(10), e7573. Johnson, D. K. N., & Ali, A. (2004). A tale of two seasons: participation and medal counts at the summer and winter Olympic Games. Social Science Quarterly, 85(4), Otamendi, J., & Doncel, L. M. (2014). Medal Shares in Winter Olympic Games by Sport: Socioeconomic Analysis After Vancouver Social Science Quarterly, 95(2), Rathke, A., & Woitek, U. (2008). Economics and the Summer Olympics: an efficiency analysis. Journal of Sports Economics, 9(5), Sotudeh, H., Salesi, M., Didegah, F., & Bazgir, B. (). Does scientific productivity influence athletic performance? An analysis of countries performances in sciences, sport sciences and Olympic Games. International Journal of Information Science and Management, 10(2), Wu, J., Zhou, Z., & Liang, L. (2010). Measuring the performance of nations at Beijing Summer Olympics using integer-valued DEA model. Journal of Sports Economics, 11(5),