Can a small nation be competitive in the global sporting arms race? The case of Denmark

Abstract

In 2014, the Danish elite sport organisation, Team Danmark (TD), celebrated its 30th anniversary. TD was founded by the government in response to the country’s decline in international standings. This study examines how Denmark’s international performance has developed in the global sporting arms race since then. It analyses how a small nation can improve its international competitiveness despite stagnating funding and growing international competition. The paper argues that the establishment of TD in 1984 is a key factor behind Denmark’s success in elite sport. Measured in absolute terms, by a market share approach, and adjusted for differences in population, wealth, religion and relevant political factors, it is evident that Denmark is performing well and appears to be competitive. Denmark is now the leading nation in Scandinavia and is doing better than almost all other smaller countries in the Summer Olympic disciplines. By examining the development of Danish elite sport policies, the paper shows how the establishment of TD has created an elite sport structure that has helped Denmark to bounce back from its previous decline.

Keywords:

• Summer Olympic Games
• global sporting arms race
• Team Danmark
• elite sport systems
• medal standings
• SPLISS study

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 Denmark does not have a tradition for winter sports comparable to the other Nordic countries. This is due to climatic and topographical reasons. Thus, the following analysis focuses on summer sports only.

2 According to Andreff and Andreff (2014), around 30 studies elaborated on medal prediction models for the Summer Olympics from the 1970s onward.

3 SPLISS: “Sport Policy Factors Leading to International Sporting Success”.

4 The Danish results from the SPLISS 2.0 study can be found in Storm and Jørgensen (2014).

5 Parts of the following two sections build upon Nielsen and Storm (2014), and Storm and Nielsen (2013).

6 For instance, in some sports (e.g. badminton, boxing and tennis) it is impossible to rank quarterfinalists and all are ranked no. 5 in this study. Furthermore, in other sports (judo, taekwondo and wrestling) the ranking of nos. 5–8 is ambiguous because of the repechage system. In this study, losers of bronze medal matches are ranked no. 5 and losers of the preceding repechage round ranked no. 7.

7 For instance, in football there is no competition with eligibility criteria similar to the Olympic competition in between the Olympic Games. In this study, the results from the FIFA U-20 World Cup are included in uneven years and the results from the World Cup in even years. Another example is tennis, where ATP/WTA double rankings cannot be used directly because they include double pairs with the players of different nationality, which is not permitted in the Olympics. In this study, the ranking of nos. 1–8 is based on a simple addition of double points for the two best players per nation. In athletics, rankings based on season’s best results are used. This has the effect that in years with no IAAF World Championships the standing of Kenya is boosted because its distance runners consistently do much better in terms of best times than in actual competitions in championships. In this study, no attempt has been made to correct for this effect.

8 New former Yugoslavian nations are part of the variable. Albania is included as well.

9 N=110 refers to the fact that of the 204 nations taking part in London 2012, 110 achieved a top eight place.

10 De Bosscher et al. (2015) present three models with different dependent variables. It should be noted that only one of the models were based on (the total number of) top eigth placings. This is the model we refer to here in terms of R²-value comparisons.

11 No outliers ±3 standard deviations away from the mean were found regarding the final model. A Kolmogorov–Smirnov test showed that the error term for the final regression model is normally distributed (sign = 0.200). This is further confirmed by a Shapiro–Wilk test for normality (sign = 0.626). Furthermore, visual inspections of scatterplots did not reveal any problems with the aptness of the data. Unacceptable correlations between the independent variables were not found either (i.e. no problems of multicollinearity (VIF < 10)).

12 Tests for aptness of data (please see the Appendix) in the 2015 regression model also reveal that the model lays within acceptable tests scores.

13 We used the same model – dependent and independent variables – for each year, but used data for the specific year in question for each regression. Detailed regression results are displayed in the appendix (Tables A1 and A2). For robustness, each year’s models have been checked for aptness of data – as was the case in the 2012 model described in the text.

14 It is important to stress that existing research is inconclusive as to whether allocating resources to a narrower set of disciplines is necessary to improve international standings. Even though a prioritised approach seems necessary to obtain a high level of international elite sport success, De Bosscher et al. (2015) conclude that it is the absolute amount of resources poured into the elite sport system that seems to matter the most in relation to output.

15 Australia has painfully experienced this effect by way of a consistent decline since its high point at the Sydney Olympics in 2000 as other countries has copied its successful structure.