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Papers in Evolutionary Economic Geography
# 20.26
Does Relatedness drive the Diversification of Countries’ Success in Sports? Louis Knuepling & Tom Broekel
Does Relatedness drive the Diversification
of Countries’ Success in Sports?1
Louis Knuepling, Institute of Economic and Cultural Geography, Leibniz University
Hannover, Germany. E-Mail: [email protected]
Tom Broekel, Business School, Stavanger University, Norway. E-Mail: [email protected]
Abstract Research Question: The sources of countries’ success in sporting events have been
investigated for a long time. In addition to socioeconomic factors, sport specialization and
competitive balance have been identified as impacting medal yields. Nevertheless, there
remains little understanding of how countries become successful in sports they did not succeed
in before.
Research Methods: We argue that the concept of related diversification, which is particularly
popular in Evolutionary Economic Geography, can also be applied to sports and can help to
explain why certain sporting countries become successful. We substantiate our argument with
an empirical study of the evolution of national medal portfolios covering 61 sports at the
Olympic Summer Games between 1896 and 2016 using this framework.
Results and Findings: Our empirical findings quantify the relatedness of sports emerging from
physical, financial, cultural, and organizational similarities. We confirm that diversification
processes based on countries’ existing strengths in particular sports are a (small) driving force
behind the Olympic success of countries and help to explain their success in sports in which
they have been previously unsuccessful.
Implications: Our results highlight how examining hidden opportunities for diversification by
incorporating sport-relatedness in a competitive positioning strategy can be extremely
informative for sport policy makers.
Keywords: Relatedness; Olympic Games; diversification; sport policy; elite sport
JEL classification: L83, Z20, O57,
1 This is a pre-print version of the article published by Taylor & Francis Group in European Sport Management Quarterly, available online: http://www.tandfonline.com/10.1080/16184742.2020.1770830.
Introduction
Most of the scientific studies analyzing nations’ success at sport events such as the Olympic
Games concentrate on just four factors: population size, GDP (per capita), political regime
(communist/socialist or not), and host advantage. There are limited studies that consider
additional factors such as comparative advantage/specialization (Du Bois & Heyndels, 2007;
Seiler, 2013; Tcha & Perchin, 2003) or competitive balance (Truyens, De Bosscher, & Heyndels,
2016). Moreover, there are hardly any studies that differentiate between sports (Forrest, McHale,
Sanz, & Tena, 2016). This contrasts with the influence of prioritizing particular sports by policy
on countries’ subsequent success in these sports (De Bosscher, Shibli, & Weber, 2019).
Similarly, non-scientific articles of the ‘Hamburger Abendblatt’ (Leoni, 2012) and ‘The
Guardian’ (Gibson, 2016) attribute the extraordinary success of the British team at the Olympic
Games in London 2012 and Rio de Janeiro 2016 to a change in national sport policy toward
increased funds, national competition, and a focus of support on sports with a competitive
balance and podium potential for the next Games. For example, this strategy was adopted by
the Dutch Olympic Committee, which calls for ‘smart’ investments into a few selected sports
(NOC*NSF, 2016).
This raises questions about how to select the right sports to focus on. In this context,
competitive balance and the number of potential medals a discipline offers are general criteria,
but do not allow for country-specific support policies. Relying on sports that yielded medals in
the past implies the stabilization of the status quo. It does not help to identify the potential to
broaden the medal portfolio and thereby possibly spread and consequently reduce risks.
This paper proposes the concept of identifying sports that are currently un- or
underdeveloped at the national level by combining insights from the literature on regional
economic diversification (e.g. Boschma, Balland, & Kogler, 2015) and sports management.
Therefore, we present the concept of related diversification, which emerged from evolutionary
economic geography, and is based on the theory that economic entities are more likely to
become successful in activities that are related to their current activities in terms of knowledge
and other resources. In essence, this theory reflects the resource-side of the competitive
positioning strategy approach, which has recently been used to investigate the prioritization in
elite sport funding (Weber, De Bosscher, & Kempf, 2019a). Consequently, we argue that a
‘translated version’ of this theory is applicable to the development of countries’ success in elite
sports. We support this argument by means of an empirical study using 61 sports at the Olympic
Games between 1896 and 2016. The results confirm that countries are more likely to become
successful in sports that are related to their current strengths.
The theoretical basis of the related diversification approach and its transfer to the context
of Olympic sports is the subject of the next section. Subsequently, we construct a novel measure
of sport relatedness, followed by the results of the empirical analysis. The findings are then
discussed and exemplified by the application of our framework to the case of Great Britain.
Related Diversification in Sports Success in Sports
Basic socioeconomic factors such as population size and GDP are widely accepted as two of
the best predictors for the number of medals a country wins at the Olympic Games (Berdahl,
Uhlmann, & Bai, 2015; Grimes, Kelly, & Rubin, 1974; Johnson & Ali, 2000, 2004; Lozano,
Villa, Guerrero, & Cortés, 2002). Assuming equally distributed talent worldwide, more people
equals more potential sports talent. Financial resources are crucial for exploiting this talent pool,
although both factors (population and financial investments) tend to be subject to diminishing
returns to scale (Bernard & Busse, 2004; De Bosscher, Heyndels, De Knop, van Bottenburg, &
Shibli, 2008; Hoffmann, Chew, & Ramasamy, 2002). During the Cold War, communist
countries outperformed market economies due to their centralized approach to elite sport
(Andreff, W. Andreff, & Poupaux, 2008; Grimes et al., 1974; Hoffmann et al., 2002). The
existence of a positive effect of hosting the Olympics is also confirmed in a number of studies
(Andreff et al., 2008; Bernard & Busse, 2004; Lui & Suen, 2008; Moosa & Smith, 2004). Being
a neighboring country (with the benefit of low transportation costs and adaption to the climate)
(Johnson & Ali, 2000) and being the upcoming host (Bredtmann, Crede, & Otten, 2016;
Maennig & Wellbrock, 2008; Nevill, Balmer, & Winter, 2009) are also associated with better
Olympic results.
Crucially, some factors influencing countries’ success in sports are specific to disciplines.
For instance, Forrest et al. (2016) find that the effect of GDP per capita differs largely across
sports. This is most likely caused by the differences between sports in terms of their resource-
intensity. Richer countries are more successful in a broader range of sports and in more
resource-intensive ones, whereas poorer countries have to specialize (Du Bois & Heyndels,
2007; Tcha & Pershin, 2003). Rathke & Woitek (2008) show, that relative to its socioeconomic
endowments, Mexico produces an inefficient number of medals in all sports, whereas Mongolia
is efficient by specializing in weightlifting and wrestling. Success in sports is also persistent
over time (Bernard & Busse, 2004; Hunt & Morgan, 1995; Lui & Suen, 2008). On the other
side, the number of medals available at Olympic Games and other world-level championships
remains rather stable, whereas increasing numbers of countries are developing (specialized)
capabilities at the elite sport level (De Bosscher et al., 2019; Truyens et al., 2016). To avoid
declining medal shares induced by increasing competition, countries extend their investments
in elite sport (De Bosscher, De Knop, van Bottenburg, Shibli, & Bingham, 2009; Green, 2004;
Houlihan & Zheng, 2013). In this context, specialization and prioritization of funding is a
prominent and successful strategy (De Bosscher et al., 2019; Green, 2004; Houlihan & Zheng,
2013): Countries tend to devote funds to sports in which they have been successful in the past
to maintain and increase their comparative advantages, whereas other countries refrain from
investing in sports dominated by other countries (De Bosscher et al., 2019).
In a recent study, Weber, De Bosscher, Shibli, and Kempf (2019b) embed the market
potential for sports in the theory of the competitive positioning of firms proposed by Hooley,
Piercy, Nicoulaud, and Rudd (2017). This includes the market side (marked-based view, MBV)
and the resource side (resource-based view, RBV). Regarding the former, competitive balance
indicates the height of the entry barrier to a sport (Truyens et al., 2016). It can be driven by the
number of medals available, but also by the situational character of the sport, such as the method
of result assessment (judgement vs. measurement) (Weber et al., 2019b). Meanwhile, the
resource side refers to the endowments of a country, such as geography (water surface,
mountains), culture (tradition of judo in Japan), and economic resources (capital-intensity of
sports) (De Bosscher et al., 2019). Based on interviews, Weber et al. (2019a) find that National
Sport Associations indeed devote funds according to both the market and the resource side.
Relatedness in Economics and Economic Geography
In Economics and Economic Geography, researchers have studied firms’, regions’, and nations’
technological and economic diversification patterns over a long time. The relatedness approach
has received considerable attention in this literature (Balland, Boschma, Crespo, & Rigby, 2019;
Boschma, et al. 2015; Kogler, Rigby, & Tucker, 2013). Based on the idea of path-dependent
economic development, researchers argue that economic entities do not diversify in a random
manner but rather do so conditional on their existing set of activities. That is, they are more
likely to try and more likely to be successful in entering2 a new field of activities when this is
2 In this paper, the notion of ‘entry’ always describes the development of success/capabilities in a specific field
related to their existing strengths and competitive advantages. Relatedness thereby implies the
new field being somewhat similar (but not identical) in terms of the competences, resources,
and capabilities required to be successful in it. For instance, a region that has previously had
success in the production of bicycles and coaches is more likely to diversify (and be successful)
in car manufacturing than a region specialized in the manufacturing of textiles (Boschma &
Wenting, 2007). This is because its labor force already possesses crucial skills (mechanical
engineering), and the configuration of its infrastructure is also closer to the needs of the new
industry. Moreover, innovators and entrepreneurs find the new knowledge easier to learn and
to develop further, as it corresponds more with their knowledge basis.
The relevance of relatedness has been confirmed in numerous studies explaining the
economic diversification of firms, regions, and nations (Boschma et al. 2015; He, Yan, & Rigby,
2016; Lo Turco & Maggioni, 2016; Neffke, Henning, & Boschma, 2011; Rigby, 2015) and has
recently been applied to the music industry (Klement & Strambach, 2019).
In the following, we put forward different causes of relatedness between sports and
develop how this concept of related diversification can be applied to the context of sports, and
hence how it may be used to gain a new perspective and better understand in which sports
countries become successful at the elite level.
Sport-Relatedness
Physical relatedness is most obvious at the level of events within the same kind of sport. For
instance, a 100m sprinter is very likely to run the 200m at a similar competitive level. The same
holds for swimmers who are often competitive at multiple distances. In part, this is a result of
the similar physical requirements for a successful execution. Apart from strength, stamina
(running, swimming), tactics (different ball games), and eye-hand-coordination (table tennis,
archery) are examples of aspects that could cause physical relatedness between sports.
This suggests the idea of modularity of sports and their relatedness through similar
components. For instance, a world-class pole vaulter needs speed on the runway and a strong
take-off but also highly developed gymnastic skills. The sport consists of different components
(running, jumping, gymnastics) that can be trained individually. Although the combination is
specific to sports, the individual components are not, which is why many sports comprise at
(industry, sport, etc.) in which the entity (country, region) did not succeed previously. ‘Diversification’ is nearly synonymous in that respect. Diversification into a specific new activity can be called an ‘entry.’
least some components of other sports. Individual athletes might be able to succeed in different
sports that are related by their components, though this rationale makes much more sense on
the regional and national level. There are instructions to many sports on the internet and elite
athletes share training drills via social media, but learning requires a form of ‘trial-and-error’
(Witt, Broekel, & Brenner, 2007). Direct supervision by an expert is important especially when
it comes to perception and sensation during the execution. To reproduce successful athletes,
countries therefore draw upon former elite athletes as top-level coaches (Rynne, 2014).
Furthermore, one reason for the establishment of national training centers is to enable athletes
and coaches of different sports to join in training and learn specific components (Lee & Price,
2016). A pole-vaulter lacking bounce or gymnastic skills might improve through advice from a
professional long jump or gymnastics coach. On the other hand, he might learn only a little
from rowers or swimmers.
Another type of relatedness may arise from similar institutional frameworks and overlap
in infrastructure or capital requirements. Sports can have the same methods of result assessment
(measuring, scoring, judging) or jointly use facilities, such as a gymnasium (handball,
basketball) or an aquatics center (swimming, diving, water polo) (Zheng & Chen, 2016). Other
sports are technology-based and expensive, such as sailing, rowing, golf, cycling, equestrian,
and fencing, which all require high-end material, whereas running, boxing, or weightlifting do
not (see De Bosscher et al., 2019).
Sports may also be related in a cultural and historical sense. The modern pentathlon, for
example, comprises events from equestrian, fencing, shooting, swimming, and running which
all developed from, or were at least frequently performed in a military context (Heck, 2014). A
long history of armed forces, such the former colonial powers have, might therefore induce
success in modern pentathlon and its components. As the athletics events were among the first
belonging to the Olympic program, countries with a long history of participation at the Games
might have established organizational structures and competencies in athletics events in general,
whereas more recent members of the Olympic community (Kenya or Jamaica) occupy niches3
(endurance, sprint) that are, in turn, highly based on physical endowments.
These physical and infrastructural requirements, institutional settings, and historical
developments have been identified as important internal resources of countries necessary for
success in elite sport (Weber et al., 2019a). Countries are endowed with more or less favorable
3 74 out of 77 Jamaican Olympic medals were awarded in sprinting. 89 out of 102 Kenyan Olympic medals were awarded in endurance (800m to marathon) or 4x400m relay (Olympic.org).
conditions depending on how closely their resources match the ones required to become
successful in a specific sport. Furthermore, we assume that the current comparative strengths
of a country (their most successful sports) reflect their endowments with these resources.
Consequently, it will be easier for countries to develop successful athletes in sports that are
related to those in which they are already successful. In this case, relatedness refers to the
overlap in resource requirements.
Imagine country J, which is unsuccessful in diving but performs relatively well in
swimming and trampoline or artistic gymnastics. Hence, it will easily find coaches with
expertise in gymnastics, and swimming pools exist that can be adapted for diving. Its population
is also likely to provide favorable physical characteristics, as the country is successful in
gymnastics. Clearly, country J has a promising foundation to advance in diving. The situation
is opposite for a country S that is only successful in weightlifting. Its coaches, infrastructure,
and general expertise are of almost no use to improve in diving. Accordingly, it will be harder
and hence more unlikely that S will become successful in diving than country J.
The following chapters present how an empirical measure of relatedness between sports
can be calculated and whether relatedness has been a driver of the diversification process
(market entries) of countries at the Olympic Games between 1896 and 2016.
Method Dataset
To measure countries’ success in sports, we rely on a dataset of all Summer Olympic medalists
from 1896 to 2008 retrieved from The Guardian (2016). The medals of the Olympic Games of
London 2012 and Rio de Janeiro 2016 are added manually from the website Olympic.org (2018).
Winter Olympics are not part of the dataset. We leave this to future research.
During Olympic history, some sports completely disappeared from the program, others
were newly introduced, and some disciplines and events experienced minor changes (e.g.
differences in weight categories by 1kg). Moreover, the number of medals distributed per event
is restricted (gold, silver, one or two bronze medals4). To ensure that sports can be traced over
time and the number of medals per sport is sufficiently large, the individual Olympic events are
4 Bronze medals are awarded to both semi-finalists in some sports with knock-out system, like Judo and Taekwondo.
aggregated to 61 sports. It is more straightforward for ball games (e.g. football, handball,
basketball), because they are very heterogeneous and cannot be aggregated. Lacking an
objective criterion, all events in which the same athlete could in principle be able to win an
Olympic medal are aggregated (e.g. 100m breaststroke and 200m breaststroke to breaststroke).
All weight categories in each combat sport are aggregated to one sport, respectively (taekwondo,
judo, etc.)5.
To further increase the comparability of sports over time, all sports that were part of less
than four Olympic Games (e.g. rugby seven’s, golf) or were staged only in the founding period
of the Olympic Games (e.g. tug of war) are excluded. As our manual classification is subjective,
the empirical results are substantiated with another, broader, aggregation of events into 42 sports
(e.g. the swimming styles, fencing styles, and equestrian disciplines are further aggregated, see
Appendix 1). On this basis, the final dataset comprises 13,917 medals won by athletes from 135
countries in 61 sports at the Olympic Summer Games between 1896 and 2016.
Mapping Sport-Relatedness
The basic assumption is that a country’s success in a sport reflects how its physical, knowledge,
infrastructure, and cultural conditions correspond to the conditions necessary for success in this
sport. We approximate success with the presence of an Olympic medal. We assume that sport
training systems are organized in a country-specific fashion, with interactions between sports
being more likely to take place within countries than between countries.
Under these assumptions, a co-occurrence approach in the style of Breschi, Lissoni, and
Malerba (2003) and Hidalgo, Klinger, Barabási, and Hausmann (2007) can be used to construct
a measure of sport-relatedness. In contrast to these studies, which rely on patent and country
export data, we apply the approach to the co-occurrence of Olympic medals at the country level.
Specifically, the relatedness between each pair of sports 𝒊 and 𝒋 at time 𝒕 is based on their co-
occurrences (𝒄𝒊𝒋 ), i.e., the number of countries (𝒏 ) that won medals both in 𝒊 and 𝒋 at the
respective Olympic Games. 𝒎 represents that a country ‘has won at least one medal’ in that
sport.
5 See Appendix 1 for a documentation of the aggregation.
𝑐 , = 𝑛∈ , , ,
(1)
For example, if Canada, France, and Russia all won medals in both rowing and sailing
at the Olympic Games in Sydney, then 𝒄𝒓𝒐𝒘𝒊𝒏𝒈, 𝒔𝒂𝒊𝒍𝒊𝒏𝒈, 𝟐𝟎𝟎𝟎 = 𝟑 (three co-occurrences). As the 61
sports contains a different numbers of events, the probability index, a version of the association
strength (see for a discussion van Eck & Waltman, 2009), is applied to correct the co-
occurrences for the ‘size’, i.e., number of potential medals in the sports:
𝜑 , =(𝑐 ,𝑇 )
(𝑠 ,𝑇 ∗
𝑠 ,𝑇 − 𝑠 , +
𝑠 ,𝑇 ∗
𝑠 ,𝑇 − 𝑠 , ) ∗ 𝑇
2
(2)
𝑆 = ∑ 𝑐 , ; 𝑆 = ∑ 𝑐 , (3)
𝑇 = ∑ 𝑐 ,, (4)
Put simply, the numerator indicates the share the co-occurrences of 𝑖 and 𝑗 have in the
sample 𝑇 of all co-occurrences of sports in portfolios of countries at time t. It is divided by the
probability of these sports to co-occur by chance, which is calculated with a random draw
(denominator). The resulting values of relatedness 𝜑 range from 0 to infinite, with 𝜑 ≥ 1
indicating deviation from statistical independence/randomness (van Eck & Waltman, 2009).
Each pair of sports receives one value indicating their degree of relatedness.
Figure 1 shows the correlation of the relatedness values between each two Olympic
Games (across all sports present at both of these Games). High values indicate a stronger time
stability of the relatedness measure. However, the correlations are rather weak, even between
two consecutive Olympics. This is a clear drawback of the approach: medals are only a rough
approximation of success because medal decisions are highly situational. They depend on
weather conditions, refereeing decisions, and many other internal (current fitness level), and
external (e.g. spread of a disease in the Olympic village) effects. Therefore, we treat all medals
equal instead of weighing gold, silver and bronze6. To further smoothen the rather unreliable
data of a single Olympics, the relatedness between each pair of sports is adjusted by averaging
the relatedness values from the first Olympics (1896) to the respective year (e.g. the mean of
all Games from 1896 to 1968 for the Games in Mexico City 1968). The consequential increase
6 De Bosscher et al. (2008) and Lui and Suen (2008) show that weighted and unweighted medal counts are highly correlated.
in stability over time is shown in the last column of Figure 1, which indicates the correlation of
the cumulatively calculated relatedness of the respective year to the cumulative relatedness
values of 2016.
Figure 1. Correlation table of the pairwise relatedness values between sports over time. Source: Own calculation and visualization, data: The Guardian (2016), Olympic.org (2018). Notes: The last column shows correlations of the cumulatively calculated relatedness of 2016 with all other cumulatively calculated years.
Figure 2 illustrates the resulting ‘sports space’ of 2016, a network visualization of the
relatedness between sports. Each sport is represented by a node (circle)7. For better visibility,
7 The non-random connections (φ>1) are inputted to the force-directed drawing algorithm (Fruchterman-Reingold), i.e., more similar sports are plotted more closely to each other. For all visualizations we use the statistical software R (package ‘igraph’ for network visualization [Csardi & Nepusz, 2006]).
only the strongest 35%8 of links between sports are displayed. One of the first impressions is
that sports belonging to the same group of sports (colors) tend to be co-located (athletics,
aquatics, combat sports, cycling), but the visual inspection also aligns with a number of our
theoretical arguments. The different swimming styles are closely related, while synchronized
swimming and (synchronized) diving are closer to trampoline. In fact, the direct link between
diving and trampoline is among the strongest (Table 1). This might be explained by the more
technical components in diving compared with swimming. More generally, sports that require
high financial investment, such as equestrian events (eventing, jumping, dressage), sailing,
rowing, and cycling are co-located in the bottom-left of the network. The capital intensity might
also explain the strong links between triathlon and jumping as well as hockey and eventing
(Table 1).
The right part of the network comprises many sports that require technical skills and
accuracy (e.g. table tennis, archery, fencing, gymnastics). In addition to the obvious physical
similarities (e.g. between the fencing styles), there are interesting component-related
connections; for example, the links between archery and badminton as well as between table
tennis and pistol (shooting), which all require excellent eye–hand coordination.
The strong relationships between different types of ball games (e.g. football and beach
volleyball) are likely rooted in the culture (high affinity of the population to ball games in
general), although component relatedness (eye–hand(foot) coordination, knowledge about
tactics) also applies there (e.g. handball and water polo). At second glance, some less obvious
but nonetheless insightful connections emerge. For instance, cross cycling and canoe/kayak
slalom (Table 1) are both forms of ‘outdoor’ sports and require balance, endurance, and
adaptability to different surface conditions.
Moreover, it becomes clear that no single resource requirement exclusively explains a
connection between two sports. Triathlon, for example, is located between its components
endurance, freestyle (swimming), and cycling. This can be interpreted as a physical or as
component relatedness. However, it might also be the capital intensity that explains its location
in the network. Clearly, this deserves further research in the future.
8 This value has been chosen for purely aesthetic reasons.
Figure 2. The 'sports space' (2016). Source: Own calculation and visualization, following Hidalgo et al. (2007): ‘product space’ and Boschma et al. (2015): ‘technology space’, data: The Guardian (2016), Olympic.org (2018). Notes: Each node (circle) represents one sport. Sports are positioned based on their non-random relations with other sports (cumulative relatedness 2016). The force-directed algorithm draws more strongly related sports closer to each other
Table 1. Strongest links in the ‚sports space’.
Source: own calculation, data: The Guardian (2016), Olympic.org (2018). Notes: selected links, to show a variety of different strongly related sports.
Empirical Specification
The co-occurrence structure of sports relates well visually to the components of sport-
relatedness identified in the theory section. With an entry model, we test if and to what degree
it actually explains the diversification of countries into new sports (see as comparison Boschma
et al., 2015, for the entry of technologies to the patent portfolio of US cities). In this model,
observations are country–sport–year combinations (e.g. ‘Australia – rowing – 2004’) and the
dependent variable is binary. A positive value (1) equals an ‘entry’ of a country to a sport, i.e.,
at these Games the country wins a medal in this sport for the first time. The dataset is limited
to those cases for which an entry is actually possible. Hence, all sports that are not part of the
respective Games, all countries that decide to not take part in the respective Games (e.g. mutual
boycotts of the USA and the Soviet Union in 1980 and 1984, respectively), and countries that
do not exist anymore (e.g. Soviet Union after 1992) are excluded. In addition, all sports in which
a country has already won a medal in the past, i.e., in which it has already successfully
‘diversified,’ are excluded for that country. The last point is a very strong restriction, as, for
example, a medal in sprinting would be a great success for Germany which won its last medal
in this sport in 1996.
The main explanatory variable indicates how closely a potentially ‘entered’ sport is
related to the current strengths of the country. We term this ‘proximity’ (𝑃𝑟𝑜𝑥), as it should
reflect the degree to which the resource endowments of a country correspond to those
potentially required for the respective sport. In practice, we calculate Equation 5. Here, the
proximity of country 𝑛 to sport 𝑖 at time 𝑡 is defined as the sum (across all other sports 𝑗) of the
products between the number of medals (NOM) country n has won in the 𝑗 sport at the
respective Games (𝑡) and the relatedness between sport 𝑖 and the 𝑗 sport in 𝑡. Thereby, we
only consider sports 𝑗 that belong to the comparative strengths s of the country, i.e., 𝑗 occupies
a larger share in the country’s medal tally than it does in all medals awarded at these Games.
𝑃𝑟𝑜𝑥 , , = ∑ (𝑁𝑂𝑀 , ,∈ ∗ 𝜑 , ) (5)
Accordingly, proximity increases when: firstly, the respective sport is related to many
sports in which the country had success before; secondly, the value of relatedness is higher; and
thirdly, the country has won several medals in the related sport. The largest value of proximity
is observed for the USA to backstroke for the 1908 Games (127.23). It is driven by the large
number of medals the USA has won in related sports at the previous Games. For an easier
interpretation and to reduce the strong effect of high medal counts in some sports, the variable
is transformed into the ‘relatedness density’ as implemented by Boschma et al. (2015). We
divide the sum of the relatedness values of all sports j that are both related to sport i and belong
to the comparative strengths s of the country, by the sum of the relatedness values of all sports
that are related to 𝑖 (Equation 6). Thus, we capture the percentage of relatedness to sport 𝑖
already present in the country’s portfolio. Thereby, the magnitude of success (number of medals)
in each related sport is reduced to whether its share in the regional medal portfolio exceeds the
share across all countries. Nevertheless, proximity and relatedness density are highly correlated
(0.85).
𝑅𝐷 , , = ∑ ,∈ ,
∑ ,∗ 100 (6)
To avoid endogeneity, both relatedness density and proximity are included with a time-
lag of one period. Consequently, we cannot calculate our explanatory variable if the sport was
in the previous Olympic program and if the country chose not to participate at the previous
Games. Neither can we calculate a proximity value before a country has won its first medal at
the Olympic Games. Excluding these cases reduces our dataset from an initial 222,345 cases
(27 Olympics x 61 sports x 135 countries) to 61,768.
Control variables
Proximity and relatedness density partly depend on size. The larger the set of sports a country
is specialized in, the higher the likelihood that some related sports are among these just by
chance. Therefore, we control for the ‘portfolio size’ (𝑃𝑆) (number of sports in which a country
is specialized) at the previous Games. The sport management literature has shown that in
addition to financial, cultural, and geographic factors, ‘competitive balance’ (CB) is a main
reason for the prioritization of funding to specific sports (De Bosscher et al., 2019), and
therefore can be incorporated as a possible factor behind diversification. Our measure of
competitive balance (Equation 7) is the percentage of all medal-winning countries (at the three
most recent Games) that won a medal in this sport (at the three most recent Games) (see Truyens
et al., 2016, PMW-measure9). To isolate the effect of competition from the size effect of sports,
we additionally control for ‘medals available per sport’ (NOM).
9 Contrary to our measure, they divide the number of medal-winning countries by the number of participating countries in each sport. We believe that our measure is more reliable, as the entry barriers for participation are extremely different (sprinting vs. sailing), which might artificially increase the competitive imbalance for sprinting relative to sailing.
𝐶𝐵 , = ∑ ,∑
(7)
Comprehensive socioeconomic data is only available from 1960 (population) or 1990
(GDP per capita) onward. We assume that these factors primarily influence the number of
medals won in total and less in what sports these are won. As we already include the medal
portfolio size, we do not expect these to be of great predictive power. Nevertheless, we add
these variables (their mean value in the three years preceding the respective Games10 ) in a
model that contains only the years following the dissolution of the Soviet Union in a robustness
check. As the literature confirms diminishing returns to scale for these two factors (Bernard &
Busse, 2004), we include both variables in their natural logarithm.
Table 2. Descriptive statistics of the dataset.
Source: own calculation, data: The Guardian (2016), Olympic.org (2018).
As outlined above, there are several reasons for why being the host or the next host of
the Olympic Games increases medal yields considerably. We therefore consider a variable ‘host’
(‘current’, ‘next’, ‘previous’) with the reference category ‘no’ to test this effect in the context
of diversification. The increasing number of events per sport and the participation of more
countries over time sharply increases the share of non-entries (0). Therefore, we consider fixed
effects for the points of observation (each Olympic Games). Descriptive statistics of the dataset
are displayed in Table 2. We do not observe issues of multicollinearity or the presence of outliers
(as indicated by observations with high leverage). The main model (model 2 in Table 4) can be
written in simple form as:
𝐸𝑛𝑡𝑟𝑦 , , = 𝛽0 + 𝛽1𝑃𝑟𝑜𝑥 , , + 𝛽2𝑃𝑆 , + 𝛽 𝐶𝐵 , + 𝛽 𝑁𝑂𝑀 , + 𝐻𝑜𝑠𝑡 , + 𝑌𝑒𝑎𝑟 (8)
10 Population and GDP data are retrieved from the World Bank (2018). Gaps for few countries are filled with United Nations Data (2018) and the Taiwanese national statistics (2018). Population data are extrapolated with the previous and/or posterior growth rate for few missing country–year observations.
Results To get a better impression of the data’s dimensions, the number of ‘entries’ (1,206) (from 1900
to 2016) per country, sport, and year are displayed in Table 3. The number increases sharply
when new sports are introduced in the early Olympics. Subsequently, it remains relatively stable,
as the Olympic sport portfolios become more saturated and are primarily driven by new
countries entering the Olympic sports scene after World War II. It rises again after 1996 with
the dissolution of the Soviet Union. The countries with the highest number of entries tend to be
those that were not part of, or not extremely successful at the earliest Olympics. The USA,
France, or Germany, for example, ‘entered’ many sports when they were part of the Olympic
program for the first time, a period for which we cannot predict their entrance probabilities due
to a lack of information. Italy, the Soviet Union, Spain, Poland, and Australia (35 to 45 entries)
rank highest in this table. As expected, weightlifting, boxing, and judo, with their variety of
weight classes, are the most frequently ‘entered’ sports.
Table 3. Entries to national sport portfolios by country, sport, and year
Source: own calculation, data: The Guardian (2016), Olympic.org (2018).
The first regression model confirms that competitive balance has a significant positive
effect on the probability of winning a medal in a sport for the first time (0.022***11). The effect
11 Raw coefficients in brackets. Coefficients transformed to odds ratios are written in percentages.
goes beyond the mere number of medals available (0.030***), upon which our measure of
balance depends. Countries specialized in more distinct sports also have higher likelihoods of
adding even more sports to their portfolio (0.184***). However, the predictive accuracy of the
regression models is low. This is expected and reasonable given the high uncertainty and
complexity of Olympic competitions. Expressed in odds-ratios, being specialized in one more
sport increases the probability of an entry by about 20%12 , whereas each 1% increase in
competitive balance increases the probability of an entry by approximately 2.2%. The additional
effect of proximity (model 2) is slightly smaller (0.015**). Relatedness density, which is on the
same scale as competitive balance (0–100), has a clearer and more pronounced effect
(0.040***). An increase of 1% makes an entry to this sport 4% more likely. However, the
general probability of entering a sport is low (only 2% of all medal wins are entries).
Accordingly, large changes in relatedness density are necessary to considerably increase or
decrease the likelihood of winning a medal in a new sport (model 3). Noticeably, the effect of
portfolio size decreases when proximity or relatedness density is added (model 2–3). This
signals that the specific selection of sports matters. The more that related sports are part of a
country’s current portfolio, the more likely it will become successful in the focal sport as well.
The magnitude of success in each of the related sports (proximity, model 2) seems to be less
important.
Regardless, by far the most important factor for success in new sports is the host
advantage. Compared to countries that are not hosting the Games (previous/next host excluded),
a host country has a five times higher chance of winning a medal in a sport in which it has not
been successful in the past (model 1–3). This is an expected result given the strong host effect
on overall medal counts (e.g. Bernard & Busse, 2004; Lui & Suen, 2008).
The stability of the models can be confirmed with three alternative specifications.
Excluding all countries that cannot have any relatedness to a potential new sport (those with no
medals in 𝑡 1 at all) changes the results only marginally (model 4). The second one (model 5),
includes only the Games from 2000 onward to eliminate any disturbances such as boycotts or
dissolutions of countries (GDR, Soviet Union). In addition, population and GDP per capita can
be considered. In this specification, the effects of relatedness density and competitive balance
become even more important in relation to the respective control variables medals per sport and
portfolio size.
12 𝑒0.1 = 1.202. Calculation of odds-ratio from the log-odds (coefficients).
Table 4. Entry model – effects of relatedness and competition on the diversification of countries into new sports.
Estimation: Binomial (Logit)
Dependent Variable: Entry = 1 (A country’s first medal in a particular sport)
Competition Proximity Rel. Density
Recent success 2000 - 2016 42 Sports
(1) (2) (3) (4) (5) (6) Proximity 0.015** (0.004) Rel. Density 0.040*** 0.037*** 0.067*** 0.028*** [Boschma et al.] (0.004) (0.004) (0.009) (0.004) Competitive balance 0.022*** 0.023*** 0.023*** 0.021*** 0.049*** 0.028*** (0.004) (0.004) (0.004) (0.005) (0.012) (0.004) Medals per sport 0.030*** 0.030*** 0.031*** 0.031*** 0.021** 0.018*** (0.004) (0.004) (0.004) (0.004) (0.007) (0.002) Portfolio size t-1 0.184*** 0.166*** 0.123*** 0.107*** 0.062*** 0.201*** (0.005) (0.008) (0.009) (0.009) (0.017) (0.014) Population t-1 0.128** (0.040) GDP/capita t-1 0.127** (0.044) Host (current) 1.599*** 1.603*** 1.645*** 1.619*** 0.860** 1.634*** (0.124) (0.124) (0.124) (0.124) (0.286) (0.158) Host (next) 0.552** 0.542** 0.583*** 0.433* 0.833** 0.833*** (0.166) (0.166) (0.166) (0.180) (0.306) (0.192) Host (previous) -0.592** -0.739** -0.508* -0.453* -0.950 -0.693* (0.230) (0.242) (0.231) (0.229) (0.541) (0.306) Year F.E. Yes Yes Yes Yes Yes Yes Constant -3.366*** -3.343*** -3.138*** -3.023*** -8.913*** -3.078*** (0.295) (0.294) (0.295) (0.298) (0.877) (0.353) adj. R² 0.06 0.06 0.06 0.07 0.04 0.07 Observations 61,768 61,768 61,768 38,354 28,311 39,097 Note: Own calculation. Data: The Guardian (2016), Olympic.org (2018) *p<0.05; **p<0.01; ***p<0.001
The socioeconomic controls have positive but rather small effects. Countries with one ‘unit’
increase in population or higher GDP/capita on the log-scale have approximately a 14% higher
likelihood to enter a sport. One unit corresponds, for example, to a difference between 10
million and 27 million people and between US$ 10,000 and 27,000 per capita, respectively.
Noticeably, a small share (2.5%) of all entries to new sports is due to the same athlete
being active in multiple sports, whereby the vast majority of these cases is observed in
swimming, fencing, and shooting. When the Olympic events are further aggregated to 42 sport
(model 6), only seven ‘entries’ by the same athlete in multiple sports remain. Most noteworthy,
Carl Albert Andersen (Norway) won a medal in pole vault in 1900 and then one in artistic
gymnastics in 1908. Overall, this is very rare and a phenomenon of the early Olympics, when
the competitive level was considerably lower. The results of model 6 indicate that the
aggregation results in portfolio size gain in relative importance to relatedness density. The
overlap in financial, physical, infrastructural, or cultural requisites appear to be less relevant
when sports are more different on average, as a result of the higher aggregation to 42 sports.
Discussion The empirical results provide robust evidence that relatedness impacts the types of sports
countries become successful in. However, only a small share of this process can be explained.
Nevertheless, it is possible to trace some national differences in diversification patterns. As
Weber et al. (2019a) point out, the internal capabilities of a country and the external market size
and competition of a sport are the two dimensions that impact prioritization of funding and
therefore shape success in elite sport. Relatedness density and competitive balance refer to these
two dimensions in the context of diversification. To showcase their relative influence, we
compare their effects in separate regressions for each country (Figure 3). The bars indicate the
lower and upper 95% confidence intervals, and statistically significant coefficients are indicated
with solid points. For seven countries, competitive balance has a significant effect on their
diversification scheme, and for six countries it is relatedness density, whereas there is no case
in which both variables significantly drive the diversification process13 . Besides this mixed
finding, the figure also reveals that many countries’ diversification pattern cannot be attributed
to either competitive balance or the relatedness density of the sports. Without additional
information about the countries’ actual approaches to success at the Olympic Games, it is
impossible to draw conclusions, yet it is nonetheless an interesting finding that motivates further
investigation.
One of the countries whose diversification seems to be shaped by relatedness density is
Great Britain (GBR, 32 entries). To translate the framework into a more practical tool, we
visualize relatedness density and the competitive balance of all sports that Great Britain could
have possibly entered in two-dimensional plots for each of the five most recent Olympics
(Figure 4). The black lines indicate the median of all considered sports, and the sports that Great
Britain entered are labeled. High scores on both dimensions indicate that the sports fit very well
to the country’s resources and have a high competitive balance. Hence, a competitive
positioning strategy based on this information would suggest prioritizing these sports in funding.
A comparison of the competitive position (Figure 4) with real funding data can show how
closely a country (intentionally or not) targeted sports according to this framework. With data
from the agency ‘UK sport’ it is possible to illustrate distinct funding figures for eight of the
sports in which Great Britain was unsuccessful until 2000 (Figure 5).
13 A few outliers with very large coefficients are excluded from Figure 3.
Figure 3. Country-specific effects of relatedness density and competitive balance on the entry to new sports. Source: own calculation and visualization, data: The Guardian (2016), Olympic.org (2018). Notes: The bars indicate the lower and upper 95% confidence intervals. Statistically significant coefficients are indicated with solid points.
Figure 4. Relatedness and competitive balance in the diversification of Great Britain at the Olympic Games (2000-2016). Source: own calculation and visualization, data: The Guardian (2016), Olympic.org (2018). Notes: All sports Great Britain could have possibly entered are plotted. The sports actually entered by Great Britain are indicated with bright, labeled points.
Of these eight sports, Great Britain entered the Olympic market in badminton (2000),
taekwondo (2008), and triathlon (2012). These three sports received the highest amount of
funding since the foundation of ‘UK sport’ in 1997. Furthermore, taekwondo was the most
balanced and triathlon the second most related sport since their first observation in 2004 until
their entry. However, a substantial share of the total funding could just be a result of the first
Olympic medals won during this time frame and explain why they stand out in comparison to
the other five sports. Moreover, triathlon and taekwondo were newly introduced to the Olympic
program in 2000. Accordingly, professional structures were less likely to be existence in all
countries at that time. Therefore, early and large investments into such ‘new’ markets could
easily pay off. Lastly, badminton is very popular in China, where the Games took place in 2008.
All these are reasons for the variations in funding and it is difficult to connect these to the
framework of relatedness and competition.
Nevertheless, the influence of relatedness on the diversification of Great Britain is also
visible in sports not covered by the funding data. Apart from synchronized diving (2004), the
sports entered were in the upper half of relatedness density. Dressage (2012) was even the most
related sport to the portfolio of Great Britain and kayak sprint (2000) scored high on both
dimensions. However, in some cases, relatedness and competitive balance clearly did not
materialize. Wrestling (Greco-roman), which has a consistently high competitive balance and
cross cycling, with the highest relatedness density in 2016, clearly stand out, However, Great
Britain did not win a medal in these. This underlines the probabilistic nature of the relatedness
framework.
Although the findings are not wholly conclusive, some recommendations for sport
policy makers can be made based on our analysis. Tracking past developments in elite sport,
and in particular the reasons for the emergence of success in specific sports using the framework
of competition, specialization, and relatedness, will reveal important information and help to
design more successful policies. An in-depth analysis is likely to reveal why certain sports have
benefited the most from certain sport policies, or why specific funding strategies have led to
rather disappointing outcomes. For example, according to Figure 4, table tennis was extremely
unlikely to yield success (consistently in the bottom-left quadrant). By contrast, funding for
table tennis was at its highest level for the Olympic Games 2008 in China, where the sport is
very popular. Therefore, it seems fruitful to investigate the link between relatedness,
competition, and targeting in more depth to further detect mechanisms through which countries
become successful in sports. To what degree do countries actively prioritize sports related to
their current strengths? Even more generally: is relatedness between sports a factor that national
sport associations consider in their strategic planning for a national elite sport system (as in De
Bosscher, Shibli, Westerbeek, & Van Bottenburg, 2015)?
Figure 5. Funds dedicated to Olympic sports by the British agency ‘UK sport’. Source: own visualization, data: UK Sport (2019). Notes: Only those sports in which Great Britain was unsuccessful before 2000 and to which funding could be directly assigned.
Conclusion The present study discusses the concept of sport relatedness. Based on the approach of Hidalgo
et al. (2007) for quantifying the degree of relatedness between export products, relatedness
between sports is calculated and visualized as a ‘sport space.’ This network represents related
groups of Olympic sports and indicates which ones could share features such as physical and
infrastructural requirements, or cultural heritage. The subsequent empirical analysis covering
the whole history of modern Olympics confirms that medals in related sports have a positive
effect on countries’ likelihood of winning their first Olympic medal in a specific sport, though
a balanced competition (lower entry barrier) (Truyens et al., 2016) shows similar positive
effects. The host advantage that was shown to affect national success at the Olympic Games
(e.g. Bernard & Busse, 2004; Lui & Suen, 2008), is also a strong driving force behind entry
into new fields of sports. Accordingly, relatedness is but one factor among others that shapes
countries’ diversification into new sports. However, the concept will be informative as a
supplement to the internal factors in a national competitive positioning strategy (Weber et al.,
2019a; Weber et al., 2019b).
However, a number of shortcomings of the empirical study must be pointed out. Firstly,
relatedness based on co-occurrence measures can be rather deterministic as it does not
necessarily reveal actual cross-fertilization and learning. It can be mainly driven by cultural and
historical events or capital intensity. This cannot be distinguished in the present approach.
Studies with more direct measures of relatedness, such as information on co-operations in
training, mutual drills, or the connectedness of athletes in social networks could validate our
findings.
Secondly, the results can be improved by considering alternative measures of sport
success as a basis for the co-occurrence calculations. For instance, participation and results of
world championships as well as world rankings will make the results more reliable and will
allow for a more time-variant measure of relatedness. Moreover, this would likely improve the
dependent variable of ‘entry into a sport.’ In our case, sport capabilities could have been present
before countries win their first Olympic medals, but we do not observe this empirically.
Thirdly, the study assigns a great weight to national borders, i.e., factors determining
countries’ success reside primarily within a country. However, coaches and athletes are mobile
(Jansen & Engbersen, 2017). For instance, nearly the whole silver medal-winning Qatari
handball team at the 2015 World Championships consisted of players born in Eastern Europe
(Hamann, 2015). Similarly, many East African runners emigrate and change nationality due to
high competitive pressure in their home countries (Chemi & Fahey, 2016). Some athletes form
international training groups and practice where the conditions are best. All these processes
imply that the factors impacting athletes’ success are not necessarily related to the countries
they win the medals for. Finally, the consideration of winter sports might reveal interesting
relationships to summer sports, as they are concentrated in a few countries and more expensive
overall.
Despite these shortcomings, the study marks an additional step in understanding the
influence of place-specific factors on sports and sport success. It presents a novel methodology
(at least in this context) for analyzing the relationship between sports. Moreover, it adds to the
recent literature in which the role of differentiation in sport policy is discussed (De Bosscher et
al., 2019; Truyens et al., 2016; Weber et al., 2019a; Weber et al., 2019b) and presents robust
empirical evidence that the relatedness between sports has an effect on the entry of countries to
new sport markets.
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1
Appendix 1. Different aggregations of sports.
Sport 42 Sports 61 Sports Original Event
Aquatics
Diving Diving
Synchronized Diving
Swimming
Backstroke all distances
Breaststroke “
Butterfly “
Freestyle “
Medley “
Synchronized Swimming
Water polo
Archery
Athletics (track & field)
Combined Combined Heptathlon and Decathlon
Endurance Endurance 3000m to Marathon incl. Steeplechase
Middle Distance 800m and 1500m
Jumps Jumps Long Jump, Triple Jump, High Jump
Pole vault
Race Walk all distances
Sprint Hurdles all distances
Sprint 60 to 400m
Throws
Discus
Hammer
Javelin
Shot put
Badminton
Baseball
Basketball
Boxing
Canoe / Kayak
Canoe Sprint
Kayak Sprint
Canoe/Kayak Slalom
Cycling
Road Cycling
Track Cycling all events on indoor race track
Cross Cycling bmx, mountain bike
Equestrian
Dressage
Eventing
Jumping
Fencing
Epéé
Foil
Sabre
Football
Gymnastics
Artistic Gymnastics
Rhythmic Gymnastics
Trampoline
2
Handball
Hockey
Judo
Modern Pentathlon
Rowing
Sailing
Shooting
Pistol pistol
Rifle rifle, running target
Shotgun trap, skeet, clay pigeons
Softball
Table tennis
Taekwondo
Tennis
Triathlon
Volleyball Beach Volleyball
Volleyball
Weightlifting
Wrestling Wrestling Freestyle
Wrestling Greco-Roman
Notes: The aggregation of sports shows which original events of the Olympic Summer Games
(last column) have been aggregated to 61 sports, 42 sports (for the robustness check), belonging
to the broader categories of sports (first column). Sports that were part of less than five Olympic
Games or only part of the earliest Olympics (e.g. Tug of War) have been excluded.
3
Appendix 2. Robustness check: Count data regression. Estimation: Quasi maximum-likelihood
Dependent Variable: Number of medals (of a country in a particular sport at one Olympic Games)
Success in the same
sport
Speciali-zation Proximity Without
1980 and 1984 2000 - 2016 42 Sports
(7) (8) (9) (10) (11) (12)
Proximity t-1 0.007*** 0.007*** 0.014*** 0.002*
(0.001) (0.001) (0.001) (0.001)
Number of medals t-1 1.303*** 1.183*** 1.122*** 1.131*** 1.186*** 1.099***
(0.014) (0.015) (0.016) (0.017) (0.029) (0.014)
Specialization t-1 0.028*** 0.028*** 0.029*** 0.036*** 0.028***
(0.001) (0.001) (0.001) (0.002) (0.001)
Host (current) 0.691*** 0.706*** 0.692*** 0.737*** 0.413*** 0.712***
(0.032) (0.033) (0.034) (0.037) (0.083) (0.036)
Host (next) 0.076 0.091* 0.083 0.083 0.178 0.102*
(0.041) (0.043) (0.044) (0.044) (0.092) (0.045)
Host (previous) -0.320*** -0.244*** -0.336*** -0.337*** -0.127 -0.266***
(0.042) (0.045) (0.047) (0.050) (0.076) (0.046)
Country, Sport, Year, Fixed Effects Yes Yes Yes Yes Yes Yes
Constant -4.434** -4.515*** -4.368*** -4.416*** -30.068*** -4.184***
(1.006) (1.022) (1.029) (1.071) (6.173) (1.068)
adj. R² 0.42 0.44 0.44 0.43 0.52 0.58
Observations 73,512 72,551 72,551 65,669 31,075 51,381 Note: Own calculation. Data: Olympic.org (2018) and The Guardian (2016). *p<0.05; **p<0.01; ***p<0.001
The count data regression (Poisson quasi-maximum-likelihood) with the number of medals of
a country in a sport at the Olympic Games substantiates the findings of the article. The number
of medals the country won in that sport at the previous Games (Number of medals t-1), meaning
ongoing success in the same sport, is by far the best predictor for the number of medals. The
degree of specialization in that sport (Specialization t-1) and the host advantage also positively
affect medal counts. Nevertheless, the proximity of a country to a sport (as calculated in the
main document) also has a significant positive effect on the number of medals a country wins
in a sport. Though, the additional variation explained by the variable is, again, very small. Two
subsets of the data (model 10 and 11) confirm the robustness of these findings. However, just
like in the main findings (model 6), reduced to 42 sports, the connection between relatedness
and sporting success declines (model 12). Again, the stronger aggregation and, therefore, lower
average relatedness between sport might explain the sharp decrease in effect size.
4 A
ppendix 3. Possible entries at the Olym
pic Gam
es in Tokyo 2021. C
ountry Sport
Probability
Country
Sport R
el. Density
RU
S Football
0.32
RU
S Football
67.75 JPN
Tram
poline 0.31
U
SA Tram
poline 61.65
USA
Badminton
0.24
GB
R
Cross C
ycling 57.64
GER
B
adminton
0.19
GER
B
adminton
56.28 G
BR
C
ross Cycling
0.19
FRA
R
ace Walk
45.25 FR
A
Race W
alk 0.15
JPN
Tram
poline 42.12
CH
N
Kayak Sprint
0.09
CH
N
Kayak Sprint
41.53 C
AN
C
anoe/Kayak Slalom
0.06
C
AN
C
anoe/Kayak Slalom
39.10
ITA
Table Tennis 0.04
ITA
Table Tennis
34.57 A
US
Dressage (Equestrian)
0.04
BR
A
Weightlifting
33.55 B
RA
W
eightlifting 0.03
A
US
Dressage (Equestrian)
32.88 ESP
Sprint (Athletics) 0.03
ESP
Trampoline
30.58 R
SA
Canoe/K
ayak Slalom
0.03
RSA
C
anoe/Kayak Slalom
30.58
SWE
Judo 0.03
D
EN
Race W
alk 30.12
DEN
R
ace Walk
0.03
NZL
Road C
ycling 28.66
NZL
Road C
ycling 0.03
C
ZE D
ressage (Equestrian) 27.86
AZE
Artistic G
ymnastics
0.03
UK
R
Javelin Throw
27.13 C
ZE Track C
ycling 0.03
K
OR
M
odern Pentathlon 25.91
UK
R
Javelin Throw
0.02
CR
O
Race W
alk 24.75
Source: Ow
n calculation. Data: O
lympic.org (2018) and The G
uardian (2016). Left: Predicted Probabilities (highest per country) based on Rel. D
ensity, Com
p. Balance, H
ost status and available m
edals (coefficients of model 3). R
ight: Highest value of relatedness density per country.
The table shows the highest probability of entry (left) and the highest values of R
elatedness Density (right) (from
high to low and only the highest per
country – otherwise Japan (as upcom
ing host) and the USA
would appear several tim
es). The rows highlighted in italics are the only ones in w
hich
the left and the right table differ. For example, Track C
ycling delivers more m
edal winning opportunities than D
ressage and has a higher competitive
balance. Therefore, the model predicts a higher probability of entry for the C
zech Republic than expected solely by the value of relatedness density.
The probabilities are relatively low for m
ost cases as the overall explanatory power of the m
odel is rather weak.
5
Appendix 4. The network of Olympic medal-winning countries (2016). Notes: Nodes represent countries. Nodes are positioned based on their non-random links (𝜑 > 1) (adjusted relatedness 2016). For better visibility, only the strongest 15% of links between countries are displayed. Node size is given by GDP per capita (2013-2016 average). Source: own calculation and visualization. Data: Olympic.org (2018) and The Guardian (2016).
The network of sports can be transposed to the network of countries (Appendix 4), based on the
similarity of their medal portfolios at the Olympic Games. It shows historical similarities
between countries through the sports in which they are successful (co-occurrences of countries’
athletes on the ‘same’ sports’ podium). We find a clustering of countries located on the same
continent. In addition, countries with a higher GDP per capita are grouped together in the central
part of the network. This reveals similarities in (sport) culture, as well as the influence of socio-
economic conditions on success in sports. There are some sub-continental clusters as well.
6
Though not showing strong direct links, Azerbaijan (AZE), Armenia (ARM), and Georgia
(GEO) are close to each other (upper part of the network). Moreover, former and current
communist countries China (CHN), North Korea (PRK), Cuba (CUB), GDR, the Soviet Union
(URS), and many of its successor states are relatively close to each other at the left of the center.
Some counterintuitive strong connections (e.g. Luxembourg and Jamaica) arise from occasional
co-occurrences. In this case, both countries won a medal in mid-distance running at the 1952
Olympics in Helsinki.
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1zeeZQzFoHE2j_ZrqDkVJK9eF7OH1yvg75c8SaBcxaU/edit#gid=322436777