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Vol.:(0123456789)
De Economist
https://doi.org/10.1007/s10645-020-09379-6
1 3
Why are We SoGood At Football, andThey SoBad?
Institutions andNational Footballing Performance
MeshaelBatar1· JamesReade1
Accepted: 9 November 2020
© The Author(s) 2020
Abstract
The basic production technology in football is identical for each team that com-
petes. All around the world, a field, goalposts and a ball is all that is required, in
addition to players. It’s hard to imagine the quality of informal football in public
parks, streets and alleys the world over differs much. Yet at each country’s high-
est level,there exists vast quality differences in the national football teams across
countries. This paper sketches out broad patterns in this variation in performance,
and seeks to understand why some countries are very good, whilst others perform
poorly. We investigate a range of macroeconomic, demographic and political expla-
nations, alongside more conventional sporting metrics.We alsoconsider the extent
to which they explain the observed variation in footballing performance historically.
We find that higher level of GDP per capita helps nations to win more often, but that
population hinders this. A more developed domestic footballing structure appears to
be helpful too.
Keywords Development· Contests· Sport
JEL Classification O1· C20· L83
1 Introduction
Outcomes matter. Economists are interested in the distribution of outcomes, espe-
cially if many salient features that matter for outcomes remain fixed. Around the
world, people play football on streets, often in any kind of space that’s available.
The authors would like to thank Mark Casson and Minyan Zhu for comments on this research as
it has developed as part of Meshael’s PhD, and two anonymous reviewers for their comments. All
remaining errors are our own.
* James Reade
j.j.reade@reading.ac.uk
1 Department ofEconomics, University ofReading, Reading, UK
M.Batarfi, J.Reade
1 3
Even without grass and goalposts, or even a football, substitutes can be used—
“jumpers for goalposts.”
Almost every country in the world has a national football team that represents
it.National teams play against each other in international competition and the pinna-
cle of the game is the showpiece event: the World Cup. This takes place every four
years.
Every country’s youngsters kick balls around, and there is no reason to believe
kick-abouts in England or Germany differ much in quality from kick-abouts in Can-
ada or China. Yet,in the 90-year history of the World Cup, only eight countries have
won the event.
Indeed, the variation can be stark; in the calendar year of 2011, Bolivia played
16 football matches and won none, while in 1984 France played 12 football matches
and won all of them.
As with general economic activity then, performance in football varies across
countries, and it is interesting and important to ask why some countries perform
better than others. In the spirit of Landes (1990) we ask why are certain countries so
good at football and others so bad?
In this paper, we answer this question by using a range of explanatory variables
that range from the sporting through to the macro-economic. Although production
technology is identical at grassroots informal level, between there and a country’s
national team is tremendous scope for variation. Footballing infrastructure can vary
greatly, from a single amateur league through to numerous professional leagues,
youth structures to select and develop talent can vary dramatically, while opportu-
nity costs will differ depending on the level of economic opportunity in a country.
Countries may have preferences for a much wider range of sports than just football,
and political structures may exert an influence through, say, compulsory military
service.
The remainder of this paper is structured as follows: Sect.2 will expand on our
motivation and discuss the relevant literature, Sect. 3 outlines the methodology,
Sect.4 presents the data, Sect.5 sets out the results, Sect.6 provides a discussion of
our results, and Sect.7 concludes.
2 Motivation andLiterature Review
Neale (1964) described the output of a football match as the “product joint”: two
teams using identical production technology to produce a single output. The more
productive is a team, ceteris paribus, the more likely it wins. The more recent eco-
nomics literature casts outcomes in terms of Tullock (1980) contest functions. That
is, the outcome of a match between team 1 and team 2 is some function of the effort
expended by each team,
e1
and
e2
, such as:
(1)
p1(e1,e2)=
er
1
er
1
+er
2
,
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Why are We SoGood At Football, andThey SoBad? Institutions…
where
p1
is the probability team 1 wins, and
0≤r≤1
. The cost of effort, however,
for teams will differ, and for national teams will be a function of various national
characteristics.
While the production technology for a basic football match is identical across
countries, much of the associated infrastructure varies a great deal. Football
clubs, at all levels, organise football, and select players. Football clubs then play
within leagues, in competition with each other. Neale notes that the economic unit
of interest is the sports league, rather than the sports club—at this level externalities
relating to the quality of teams are internalised. As such, sports leagues matter, as do
the bodies that govern them.
Sports leagues and governing bodies attempt to ensure sufficient competition
throughout their structures such that interest remains; the uncertainty of outcome
hypothesis proposed by Rottenberg (1956) surely applies to participants as well as
spectators. More interesting league structures attract more attention; more attention
would be expected to correlate with higher willingness to pay to participate or spec-
tate. Such greater willingness to pay implies greater resources for sports leagues, in
principle reducing the cost of effort in (1) for the national team of that country.
Equally, though, sport is, ostensibly, a leisure pursuit. As such, it may be pro-
posed that as any economy develops, and productivity increases, the opportunity
cost of leisure rises. Hence conversely, it may be the cost of effort increases with
greater income levels in a country.
Moreover, productivity growth is rather limited; teams can only win matches.
Teams like France in 1984 may win all of their matches, and from this point further
productivity growth cannot occur.1 Because of productivity gains in other sectors
in an economy, the cost of producing a football match has increased over the years,
a manifestation of the Baumol effect (Baumol and Bowen 1966). This will be par-
ticularly strong in countries where incomes are higher for other types of production
where productivity gains occur.
It is thus interesting to explore the relationship between sporting performance and
measurable factors both from within sport, and from the broader economies in which
football exists. Koning and McHale (2012) incorporate population and income per
capita to a simulation model forecasting the winners of the Fifa World Cup. They
find both to be positive and significant.
Several studies have examined the economic determinants of national sporting
performance and country wealth and development level. Most of the existing litera-
ture has found that countries with better life conditions and more resources have a
higher ability to win at sport when compared to developing countries with poor life
conditions and less available resources (Andreff 2006).
Peeters etal. (2019) consider the role that immigration plays in the development
of knowledge capital in countries, via coaches of national teams. They find a posi-
tive effect of GDP and population.
1 This is, of course, somewhat debatable. In 1984 France won all matches and won the European Cham-
pionships, a continental tournament. In 1998 they didn’t win all matches, yet won the World Cup, argu-
ably a greater achievement marking greater productivity.
M.Batarfi, J.Reade
1 3
Eber (2003) shows that sport practice and economic wealth go hand in hand.
Improvement in sport performance in developing countries are related to both
technology transfer and local information spillover. Some developing countries
have shown the ability to catch up with developed countries in many different dis-
ciplines including sport (Yamamura 2009).
A country’s FIFA ranking may be considered as an indicator of the country’s
development (Gásquez and Royuela 2014). Szymanski (2016) noted that the FIFA
ranking of national teams depends negatively on population size and income.
Economic prosperity is positively associated with football resource acquisition,
which helps to boost football performance (Omondi-Ochieng 2015). Country
wealth and population size may contribute to international success for both male
and female soccer teams (Bredtmann etal. 2014). In this context, other research-
ers have found similar results showing income and population having a positive
diminishing effect on football success (Leeds and Leeds 2009). Moreover, popu-
lation size has been found to be strongly correlated with football success, espe-
cially in Latin American countries (Macmillan and Smith 2007).
Similar results have been found for women’s football. Higher income leads
to better availability of resources, such as strong infrastructure and more leisure
time, which enables potential athletes to practice more (Hoffmann etal. 2006).
African countries with a higher level of GDP and population have also shown
better sport performance(Luiz and Fadal 2011). More broadly, Krause and Szy-
manski (2019) investigate the classic economic development hypothesis of con-
vergence by using international football match outcomes, noting that while pro-
duction technology differs considerably across industries, and thus countries, the
production technology for a football match is the same regardless of the country
the match is played in. Most studies have confirmed the significant impact of eco-
nomic development on sport performance (Andreff 2006). Moreover, GDP was
significantly related to the success of football clubs in some European countries
(Klobučník etal. 2019).
Other research has suggested that the more elderly members in a community, the
more likely that policy makers will pay attention to non-sport sectors instead, such
as health (Barros 2006). Wealthy countries with better nutritional levels, more suf-
ficient sport facilities and more qualified coaches have a higher chance of winning
more medals in the Olympic games (Barros 2006).
Countries with a large population have more opportunities to win more medals
in the Olympics Games, as it is more likely that skilled and talented players will be
drawn from a larger population (Andreff 2006). Bernard and Busse (2004) consider
the impact of population and income per capita on Olympic games success, finding
both have a positive impact.
Most governments profess to be favourably inclined towards sport, and for many
reasons this ought to have a positive impact on health, social control and cohesion,
diplomacy and economic development (Houlihan and Green 2006). Countries like
the UK have created their own charter aiming to obtain excellent sport results at
the world class level, whereas other countries, such as France, have modified their
regime according to their level of performance in the Olympic games (Nys 2006).
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Why are We SoGood At Football, andThey SoBad? Institutions…
3 Methodology
We carry out an empirical analysis, informed by the above literature and reason-
ing, to investigate the institutional quality of footballing infrastructure in different
countries.
The value of football match outcomes is the detail with which they have been
recorded over a very long time period—the first international football match took
place in 1872, and over 30,000 have occurred since then. This can be used to
evaluate productivity, and sporting output of nations.
We explain variation in win proportions by national football teams using
explanatory variables that relate to footballing and sporting aspects of a country,
as well as more broader economic and political variables.
We consider how often a country’s national team wins in international compe-
tition to be our measure of footballing development; the more developed a coun-
try, the more often it will win. We scale the number of wins a country has in a
season (where we take the conventional northern hemisphere season, which spans
two calendar years running from the Autumn of one year through to the Spring of
the next year) by the total number of matches that country’s team plays.
Teams will win more often, however, the weaker is their opposition, and hence
an initial step is to control for opposition strength. We do this by calculating Elo
ratings for each team, and we include the average Elo strength of a country’s
opponents in a season.
We propose a number of hypotheses:
Hypothesis 1 A national team will perform better with more economic resources,
since more resource can be devoted to sporting development. That is, the income
effect will dominate the Baumol effect.
Hypothesis 2 A national team will perform better with a larger population, since
if we assume talented individuals exist with some small probability, then a larger
population increases the expected number of talented footballers.
Hypothesis 3 A national team will perform better the higher is its unemployment
rate. Football is a leisure pursuit fundamentally, and hence more wealthy nations
may have less leisure time as the opportunity cost of leisure is higher. Hence if
unemployment is higher, there are more individuals free to play sport and develop,
and the lack of job opportunities may encourage young people to pursue a career in
sport.
Hypothesis 4 A national team will perform better if that country has a greater mili-
tary spend. A country whose population must conduct military service will be bet-
ter trained in team work and discipline, arguably important attributes of successful
teams. We measure this by the proportion of GDP spent on military activity.
M.Batarfi, J.Reade
1 3
Hypothesis 5 A country whose national sport is (not) football will perform better
(worse), ceteris paribus. We measure this by using information on country perfor-
mance at the Summer Olympic games, in terms of the number of sports participated
in. A country with more medals and participants arguably is a country with more
diverse sporting interests.
Hypothesis 6 A country with a more developed domestic football scene will per-
form better. Countries with more leagues, more clubs and more players, hence a
more developed local footballing infrastructure, will perform better. We measure
this using information on the number of clubs, and competitions in a country in a
season. The more of either, the more developed is the domestic football scene.
We run a linear regression model on an unbalanced panel data of country win
proportions over calendar years:
Here,
wit
is the win proportion of country i in year t, and
Xit
is a set of explanatory
variables including those representing each of the above hypotheses. The error term
is assumed to have mean zero, constant variance.
The win proportion is the ratio of the number of wins a country team has in a
calendar year to the number of matches played. We focus on wins and ignore draws,
even though a draw can often represent an achievement for a team.2 It remains, how-
ever, that the win is the most salient aspect of a football match’s outcome. We con-
sider all matches a national team plays in a year, hence friendlies as well as competi-
tive matches. This is because while friendlies may be argued to be less informative
since they do not count towards any tournament outcome, they still provide informa-
tion about a national team’s performance. Furthermore, a greater proportion of a
national team’s matches are friendlies than for domestic clubs. We control for the
strength of opposition a country faces, to guard against the possibility that national
teams only pick friendly matches against easier teams.
Because of the range of variables we include, we are unable to exploit the full
richness of the history of international football; our effective sample starts in 1950
when our macroeconomic data becomes available, and ends in 2017.
We investigate the impact of a range of variables on the win proportion for a
country’s national team. In Fig.1 we plot the win proportion against our measure of
average wealth in a country’s population: the log of real GDP per capita. The plot
reveals that the simple relationship between these variables, whilst positive (the red
least squares regression line), is very noisy. Hence we add in a range of additional
explanatory variables in
Xit
.
We add in variables to measure the six hypotheses listed above, and we include
the lagged win proportion; in addition to country fixed effects, this enables us to
(2)
wit =Xit𝛽+eit
,
eit ∼N(
0,
𝜎2).
2 Indeed, Reade etal. (2020) argue that the draw is what makes forecasting football match outcomes so
strange, and difficult.
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Why are We SoGood At Football, andThey SoBad? Institutions…
capture the persistence in the performance of national football teams; as playing
careers at the pinnacle of the game are relatively short, and cohorts overlap, good
teams will last for a number of years before gradually being broken up. Equally,
bad teams will remain bad for some time even as better players begin to enter the
national team.
It is plausible that some of the variables to measure the six hypotheses are related
to each other. For example, Olympic success is known to be correlated with the
income levels in a country. However, the level of correlation is not sufficiently high
between any of our explanatory variables as to warrant concern; by including these
distinct variables in the same multi-variate regression model we are able to evaluate
the contribution each makes, once the other is conditioned upon.
4 Data
Data on sport is plentiful, as over 30,000 international football matches have taken
place since 1872, the year when England and Scotland played the first recognized
football match (Wikipedia 2020a). On May 21 1904, after 154 international matches
had taken place between nine different countries, The Fédération Internationale de
Football Association, or FIFA, was formed as the world’s football governing body.3
FIFA now has more than 200 members (Wikipedia 2020b)
We collect data on football match outcomes back to 1872 from Kaggl e.com, and
domestic league structures of 113 countries from world footb all.net. Supplementary
3 These nations were All Ireland, Austria, Belgium, Bohemia, England, France, Hungary, Scotland and
Wales.
Fig. 1 Annual income per capita and annual win proportion
M.Batarfi, J.Reade
1 3
information on domestic league structures was collected from The Rec.Sport.Soccer
Statistics Foundation (RSSSF, rsssf .com) and Wikipedia.
Data on GDP per capita, employment and population were collected from Penn
World Tables, version 9.1; see rug.nl/ggdc/produ ctivi ty/pwt/. Data on military
spending was collected from the Stockholm International Peace Research Institute
(SIPRI, sipri .org).
As many national teams have only relatively recently started playing football, the
data constitute an unbalanced panel. Figure 2 provides a graphical representation
of the years in which countries played their first international football match. After
football’s early spread at the start of the twentieth century, the next burst of inter-
national starts came after the First World War, with a second burst after the Second
World War (Fig.2).
Data from the Summer Olympics games was collected from the website www.
sport s-refer ence.com. The Summer Olympic Games are arguably a better indica-
tor of how ‘sporty’ a nation is, as the Summer Games have more than 42 sports,
whereas the Winter Olympic Games only has 15 sports (Wikipedia 2020c). Further-
more, in general summer sports are more accessible to a wider range of nations due
to both the required climactic conditions, and the necessary cost to participate in
such sports.
Descriptive statistics are presented in Table1. From here we see that there is sig-
nificant variation in the number of observations available from different sources.
While the win proportion for countries goes back to their first recorded match, the
economic variables are limited to the period between 1950 and 2017 (around 7500
observations). Military spending, as a proportion of GDP, is available for yet fewer
observations (6202), and the domestic league arrangements for slightly fewer than
that (though these samples do not necessarily overlap).
Fig. 2 Year of first international match by countries
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Why are We SoGood At Football, andThey SoBad? Institutions…
It is, nonetheless, the domestic league data that yields novel insights relative to
previous studies. We use the total number of clubs in elite competitions in a country
per year, and the total number of elite competitions.4 It points to an internal institu-
tional structure that may provide the basis for international development policy in
the area of sport.
In Table2 the correlation matrix between our explanatory variables, and also the
dependent variable, is presented. The largest correlations, of above 0.8, are for the
two measures of Olympic participation, and the two measures of the domestic foot-
balling scene. We thus interpret these coefficients with some degree of caution in
our results.
5 Results
We present our results in Table3. We present a number of regression models as we
add variables in. Estimation is by ordinary least squares as we have both large N and
large T in our panel dataset. The time period for the regression model in the first
column, with the most observations, begins in 1950, the first years of observations
for our income and population data. With subsequent additions of explanatory vari-
ables, some observations are lost as the coverage of these variables is less complete.
It is important to check the quality of a regression model; we assume that the
error term is mean zero, constant variance. In Fig. 3 we present analysis of the
quality of the model. From the left plot, which is a histogram of residuals from our
Table 1 Descriptive statistics
Statistic N Mean SD Min Pctl(25) Pctl(75) Max
Win proportion 12,065 0.354 0.271 0 0.1 0.5 1
Log real GDP per capita 7847 −4.987 1.195 −8.408 −5.935 −4.046 −1.452
Log population 7847 15.811 1.935 8.384 14.870 17.156 21.067
Employment rate 7422 0.395 0.093 0.117 0.333 0.461 0.898
Military spending (% of GDP) 202 0.028 0.033 0.000 0.013 0.034 1.173
Summer olympics: no. of partici-
pants
8012 43.623 83.453 0.000 3.000 40.000 648.000
Summer olympics: total medals 8012 3.659 11.272 0.000 0.000 1.000 174.000
Total number of matches played 12,065 188.982 191.487 1 40 282 1018
First international match 12,065 1,938.250 29.277 1872 1921 1959 2019
Mean opposition elo rating 12,065 1,043.742 106.738 511 981.0 1,113.7 1453
Total clubs in domestic competi-
tion
4713 22.643 23.963 0.000 12.000 20.000 292.000
Total number of domestic com-
petitions
4713 2.290 2.943 1.000 1.000 3.000 53.000
4 We define elite to be those for which data exists, either on world footb all.net or rsssf .com.
M.Batarfi, J.Reade
1 3
Table 2 Descriptive statistics: correlation matrix
Win GDP Pop Emp Mil Olym prt Olym tot Tot match 1st match Opp elo Total clubs Total comps
Win proportion 1 0.100 0.240 0.070 −0.020 0.170 0.120 0.190 −0.170 −0.010 0.190 0.110
Log real GDP per capita 1 −0.130 0.430 0.020 0.280 0.240 0.440 −0.330 0.300 0.320 0.330
Log population 1 0.070 0.020 0.360 0.300 0.390 −0.380 0.280 0.220 0.130
Employment rate 1 −0.150 0.210 0.210 0.320 −0.260 0.150 0.150 0.170
Military spending (% of GDP) 1 0.020 0.030 −0.150 0.060 −0.020 −0.070 −0.110
Summer olympics: no. of participants 1 0.860 0.290 −0.420 0.270 0.270 0.230
Summer olympics: total medals 1 0.220 −0.330 0.200 0.220 0.200
Total number of matches played 1 −0.360 0.470 0.460 0.430
First international match 1 −0.330 −0.290 −0.190
Mean opposition elo rating 1 0.250 0.270
Total clubs in domestic competition 1 0.800
Total number of domestic competitions 1
1 3
Why are We SoGood At Football, andThey SoBad? Institutions…
preferred, full model, these are symmetric around zero, with no evidence of outliers.
From the right plot, residuals against time, no clear patterns stand out, suggesting
that most systematic variation in our variable of interest has been included in the
model.
In the first column we present a regression model of win proportion on per capita
GDP, population and the employment rate. GDP per capita has a positive and sig-
nificant effect on win proportion, as does the population—richer and more populous
countries win more. The employment rate is insignificant. The coefficient of varia-
tion,
R2
, is 0.071, suggesting that GDP per capita, population and the employment
rate are able to explain about 7% of the variation in win ratios recorded by countries.
In the second column, we add fixed effects. With country and year fixed effects,
the population variable becomes negative, while the GDP per capita effect is barely
affected. The employment rate remains insignificant. In this column,
R2
increases to
0.237, suggesting that the fixed effects alone, included to capture unobserved hetero-
geneity, account for about 23% of variation in win ratios. While it is commonplace
to estimate with fixed effects, it is worth considering their role here explicitly. They
enable differences in means over given years and countries, for unobserved effects,
to be accounted for. It is likely that adding fixed effects will reduce the explanatory
power of other variables, but this should not be the primary concern, provided, as is
the case here, the overall explanatory power of the model increases. By including
more variables, more discernment is afforded in terms of which variables are caus-
ing which variation in the dependent variable.5
5 A related concern is that as win proportions must sum to 1, that the interpretation of fixed effects is
confusing. In principle, however, all series could be scaled such that they must sum to a particular num-
ber. Fixed effects enables unobserved factors that cause differences between countries in their win pro-
portions to be represented in our model.
Fig. 3 Residual analysis. Left panel is the histogram of residuals from the full model as reported in
Table3, and the right panel is the residuals plotted against the year of observation
M.Batarfi, J.Reade
1 3
Table 3 Regression results
Dependent variable:
Win proportion
(1) (2) (3) (4) (5) (6)
Constant −0.023 0.673
∗∗∗
0.843
∗∗∗
0.910
∗∗∗
3.065
∗∗∗
30.957
∗∗∗
(0.029) (0.182) (0.216) (0.222) (1.001) (9.363)
Log real GDP per
capita
0.029
∗∗∗
0.028
∗∗∗
0.043
∗∗∗
0.051
∗∗∗
0.034
∗∗∗
0.031
∗∗
(0.003) (0.009) (0.011) (0.011) (0.010) (0.015)
Log population 0.034
∗∗∗
−0.023
∗
−0.037
∗∗
−0.038
∗∗
−0.072
∗∗∗
−0.013
(0.002) (0.013) (0.015) (0.016) (0.015) (0.019)
Employment rate −0.033 0.077 0.163
∗∗
0.157
∗
0.307
∗∗∗
0.344
∗∗∗
(0.032) (0.068) (0.080) (0.081) (0.077) (0.107)
Military spending (%
of GDP)
−0.077 −0.028 0.038 −0.079
(0.122) (0.123) (0.117) (0.130)
Summer olympics: no.
of participants
0.0001
∗
0.0002
∗∗∗
0.0001
(0.0001) (0.0001) (0.0001)
Summer olympics:
total medals
−0.001 −0.001
∗
−0.001
(0.001) (0.001) (0.001)
Lagged win propor-
tion
0.061
∗∗∗
0.069
∗∗∗
(0.013) (0.016)
Total number of
matches played
0.0003
∗∗∗
0.0003
∗∗∗
(0.0001) (0.0001)
First international
match
−0.0004 −0.015
∗∗∗
(0.001) (0.005)
Mean opposition elo
rating
−0.001
∗∗∗
−0.001
∗∗∗
(0.00004) (0.00005)
1 3
Why are We SoGood At Football, andThey SoBad? Institutions…
∗
p < 0.1;
∗∗
p < 0.05;
∗∗∗
p < 0.01
Table 3 (continued)
Dependent variable:
Win proportion
(1) (2) (3) (4) (5) (6)
Total clubs in domes-
tic competition
0.001
∗∗
(0.0003)
Total number of
domestic competi-
tions
−0.0003
(0.002)
Observations 7422 7422 5876 5735 5732 3436
Country fixed effects N Y Y Y Y Y
Year fixed effects N Y Y Y Y Y
R
2
0.071 0.237 0.271 0.276 0.347 0.368
Adjusted R
2
0.071 0.211 0.243 0.248 0.322 0.334
Residual SE 0.233 (df = 7418) 0.215 (df = 7180) 0.202 (df = 5662) 0.200 (df = 5520) 0.190 (df = 5513) 0.173 (df = 3263)
F statistic 189.773
∗∗∗
(df = 3;
7418)
9.244
∗∗∗
(df = 241;
7180)
9.875
∗∗∗
(df = 213;
5662)
9.850
∗∗∗
(df = 214;
5520)
13.462
∗∗∗
(df = 218;
5513)
11.032
∗∗∗
(df = 172;
3263)
M.Batarfi, J.Reade
1 3
In the third column we add in military spending. This has a negative effect, but is
insignificant, but it reduces the available sample by almost 2,000 observations. The
effect of this is that the employment rate becomes positive and significant, and the
population rate becomes significant (and negative). The effect of per capita GDP
also increases in magnitude.
In the fourth column we add in Olympic performance. The number of partici-
pants a country sends to a Summer Olympics is significant and positive, but the total
number of medals is not, although it is negative. Hence the sportiness measured by
the number of participants, rather than the success measure by medals, matters.
In the fifth column, football specific variables are added: lagged win percentage,
the number of matches played by a country, the year of its first match, and the mean
strength of the opposition (as measured by mean Elo (1978) ratings). The lagged
win proportion is significant, which establishes that persistence exists in football-
ing performance. This is inherently sensible since a team can be thought of as a
stock concept with flows of younger players into the team, and flows of older players
out of it. A country may have a ‘golden generation’ of players who lead to better
performance, but once they begin to retire, performance levels fall. Country experi-
ence is positively significant, suggesting that with each extra match of experience, a
team’s win proportion will increase by 0.0003. The average strength of opposition
in a given year also matters for win proportions; there will be natural variation in
the quality of opposition a team can face; World Cups and regional tournaments like
UEFA’s European Championships, only occur every four years, and so outside of
these years a team will likely face a lower quality of opposition.
Finally, in column six we add in domestic league structures. The total number of
elite clubs in existence has a positive effect, while the total number of competitions
is negative but insignificant. Also of note is that while almost all coefficients remain
very similar between columns (5) and (6), there are some notable changes. This is
likely more the effect of losing around 2,000 observations in adding the domestic
league structure variables into the equation, than of the adding of the variables. For
example, the population effect becomes insignificant, though still negative. The
impact of military spending also turns negative, albeit still insignificant. The impact
of summer Olympic participation becomes insignificant, although still of the same
sign, while finally, the impact of a country’s year of first match (see Fig.2) jumps in
size and becomes significant. It is more likely that this reflects the characteristics of
the countries that remain in the sample, but nonetheless it is plausible that a country
that began playing football earlier would have more accumulated knowledge capital,
and hence win more matches.
The adjusted
R2
for this final equation is 33.7%, suggesting that our model
accounts for about a third of the observed variation in win proportions. The same
model without fixed effects has an adjusted
R2
of 0.2, implying that our additional
variables on top of those in column (1), do account for some of the unobserved het-
erogeneity, as the fixed effects only account for 13% of unobserved variation in our
final model.
Throughout the GDP per capita effect is positive and significant, while the popu-
lation effect becomes insignificant, and the employment rate effect becomes positive
and significant.
1 3
Why are We SoGood At Football, andThey SoBad? Institutions…
6 Discussion
In this Section we provide some discussion of our results, and return to our original
hypotheses from Sect.3 to frame them.
The results presented in Table3 suggest that for a one log-point increase in GDP
per capita, win proportions increase by three to four percentage points. The finding
that average wealth does increase the success of national football teams need not
necessarily be surprising, and indeed confirms our Hypothesis 1.
The finding of a negative or insignificant population effect is intriguing, particu-
larly given that (Peeters et al. 2019) find a significant and positive impact. Their
dependent variable is Elo ranking, however, rather than a country’s win proportion
in a year. As such, our Hypothesis 2 is not upheld.
Hypothesis 3 is only indirectly measured, since unemployment data is not avail-
able across the same wide sample as the employment rate. Our finding of a positive
and significant effect of the employment rate is contrary to Hypothesis 3, since it
suggests that a country does better at football the more of its population is employed.
Military spending was insignificant in all of our regression models, suggesting
that Hypothesis 4 is not upheld, namely that a higher military spend fosters more
discipline and fitness and hence better sporting performance.
Our Hypothesis 5 suggests that the larger the number of participants at the Sum-
mer Olympics, the more diversified is sporting preferences in a country, and the less
likely is that country to be successful at football. However, the effect of the num-
ber of participants is positive on the footballing win proportion, albeit insignificant,
counter to this.6 This may be a feature of the relatively high correlation between
these two variables; however, while they are of opposite signs, they are not of offset-
ting magnitude. However, the level of success, measured via medals, is negative if
insignificant, suggesting that greater success at a range of sports means footballing
success is lower.
A strongly significant, and positive, impact of the total number of clubs sug-
gests that the richness of the domestic football scene matters greatly, supporting
our Hypothesis 6. It is total clubs, rather than competitions that matter—indeed,
the number of competitions is negative, if insignificant. Again, this opposite sign
between these variables may reflect the relatively high correlation between them.
However, a reasonable explanation can be provided; it suggests that an increasing
number of competitions likely distracts teams by adding more games to the calendar,
tiring players who then play for the national team. A common thread of discussion,
particularly in England, revolves around the number of matches being played due to
the number of competitions.
As a final part of our analysis, we present two sets of residuals from the model.
We present the residuals for two countries: England and France.7 Residuals dis-
play the variation left unaccounted for in national team performance after all of our
explanatory variables have been taking into account. As such, the residuals should
6 It is significant in column (5), before the domestic football scene variables are added.
7 Further residual plots for countries are available on request.
M.Batarfi, J.Reade
1 3
display little or no systematic pattern. In the Figs.4 and 5 we plot in black the resid-
uals from the baseline model (column 1), as well as the full model (column 6). The
full model residuals in both cases are mean zero, and constant variance, from a cas-
ual glance.
Fig. 4 Residuals from England’s performance
Fig. 5 Residuals from France’s performance
1 3
Why are We SoGood At Football, andThey SoBad? Institutions…
Another aspect of residual series is that while none should be too large, the larger
residuals still do reveal the more unexpectedly strong performances in a given year.
In England’s case in Fig.4, the largest residual is in the year 1966, which corre-
sponds with the single year in which England won the World Cup. In France’s per-
formance in Fig.5, its largest positive residual is 1984, the year it won every single
match, including the UEFA European Championships, France’s first international
tournament.
7 Conclusion
In this paper, we investigate variation in observed outcomes for countries playing
football, in order to try and explain why such great variation exists at national levels
between countries, yet does not at the very grassroots level of the game. We find
that GDP per capita has a significant positive impact on wining proportion, though
population size might decrease the chances of winning, the more populous the coun-
try is, the less chances they have to win. There was no impact of military spend-
ing or country experience or summer total participation, but the total matches have
shown negative significant effect. The total number of football clubs in a country has
a positive impact on a national team’s performance, while the number of domestic
competitions has a negative, though insignificant, effect.
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