Econometrics and the World Cup - When the sacred and the profane go hand in hand - Dun & Bradstreet
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Econometrics and the World Cup When the sacred and the profane go hand in hand
Overview
In 2010, Price Waterhouse Coopers (PWC) conducted research1 on the 2000, 2004 and 2008
Olympics, in order to establish any possible indicators that could help predict national
success. The PWC team’s investigation established four indicators that were statistically
significant in creating a predictive equation. These were:
Population – This gives the country in question a larger sample of potential talent to
choose from;
Average income level (GDP per capita) – Richer countries have access to more
resources, which enable more investment in coaching, training facilities and sports
technology;
Political background – Centrally-planned economies were highly likely to invest
more in Olympic-related sports as a means of boosting national prestige;
Host country – There was an inference that host countries would out-perform their
previous Olympic performances.
Taking population, average income level and host country as explanatory variables (there is
no evidence of a centrally-planned economy effect in soccer), and following PWC’s
approach, the Country Insight Solutions team at Dun & Bradstreet have undertaken their own
investigation to help predict the likely winner of the 2014 World Cup incorporating historic
total World Cup points by country and their FIFA world rankings.
Our study adds to PWC’s analysis, in that:
It includes the World Cup 2010 results;
It uses the current (May 2014) FIFA/Coca-Cola World Rankings;
It uses a vector autoregression (VAR) to investigate if some variables granger-cause
other variables (and if so, which ones);
When the dependent variable is total World Cup points by country, we used the
current FIFA/Coca-Cola World Rankings as an additional explanatory variable. This
potentially serves as a proxy for those ‘psychological conditionings’ which may
unconsciously prompt referees to favour teams from countries with a more solid
soccer tradition! If the FIFA ranking has a positive and statistically significant impact
on the number of points scored, it could be that, for example, a goal which is offside
is more likely to be validated when it is scored by FIFA high-ranking countries than
by low-ranking countries. At the same time it may simply reflect the fact that higher-
ranking teams score more goals because they are stronger than low-ranking teams.
1PWC, 2010 What can econometrics tell us about World Cup performance?
http://www.pwc.es/es_ES/es/sala-prensa/notas-prensa/assets/informe-favoritas-mundial-070610.pdfAlternative measures of national performance in soccer
Since there have only been 19 World Cups since the competition began in 1930, the sample is
too small for a sound statistical analysis exercise. In order to get round this issue, PWC
considered two broader measures of national soccer performance:
The total number of points scored by each country in all World Cups since 1930,
using the current scoring system of 3 points for a win and 1 point for a draw (data
were sourced from www.planetworldcup.com); and
The current FIFA/Coca-Cola World Rankings as at 19 May 2014 (using the point
scores for each country rather than just the ordinal rankings).
As PWC illustrated in its analysis, these measures show a reasonable (although far from
perfect) correlation (see Figure 1). The dots above the exponential-trend line are the countries
whose total historic World Cup point scores are high relative to their current FIFA ranking,
and include Brazil, Germany, Argentina, Italy, Russia, Poland and Portugal. The dots below
the line indicate countries whose historic World Cup performance has been lower than their
current FIFA ranking would suggest. As in 2010 PWC’s study, Spain remains the most
notable case, ranking 1st on the current FIFA rankings but only 6th in terms of historic
performance (in 2010, Spain ranked 2nd and 7th, respectively).
Figure 1: Historic World Cup performance vs current FIFA rankings
250
Total World Cup points (1930-2010)
200
150
100
50
0
0 200 400 600 800 1000 1200 1400 1600
FIFA world ranking points (May 2014)The table below shows the full top 30 rankings on these two alternative measures.
Table 1: Top 30 nations by historic World Cup performance and current FIFA rankings
Total World Cup points scored (1930-2010)2 Current FIFA world ranking (May 2014)3
Rank Country Points Rank Country Points
1 Brazil 216 1 Spain 1460
2 Germany 199 2 Germany 1340
3 Italy 153 3 Portugal 1245
4 Argentina 124 4 Brazil 1210
5 England 97 5 Colombia 1186
6 Spain 96 6 Uruguay 1181
7 France 86 7 Argentina 1178
8 Netherlands 76 8 Switzerland 1161
9 Uruguay 66 9 Italy 1115
10 Sweden 61 10 Greece 1082
11 Serbia 56 11 England 1043
12 Russia 57 12 Belgium 1039
13 Poland 50 13 Chile 1037
14 Mexico 49 14 USA 1015
15 Hungary 48 15 Netherlands 967
16 Czech Republic 41 16 France 935
17 Austria 40 17 Ukraine 913
18 Portugal 39 18 Russia 903
19 Belgium 39 19 Mexico 877
20 Chile 33 20 Croatia 871
21 Switzerland 33 21 Cote d'Ivoire 830
22 Paraguay 31 22 Scotland 825
23 Romania 29 23 Denmark 819
24 Denmark 26 24 Egypt 798
25 USA 26 25 Bosnia and Herzegovina 795
26 South Korea 23 26 Sweden 795
27 Croatia 20 27 Algeria 795
28 Scotland 19 28 Ecuador 794
29 Cameroon 19 29 Slovenia 787
30 Bulgaria 17 30 Serbia 759
Relationship of national soccer performance to population
A simple regression of total World Cup points against population suggests that a larger
population improves soccer performance: a larger talent pool makes countries more
competitive. However, this relationship is far from consistent, as shown in Figure 2 below:
2
Source: www.planetworldcup.com
3
Source: FIFA/Coca-Cola rankings from www.fifa.comFigure 2: Total World Cup points vs population
250
Brazil
Total World Cup points (3 = win)
200
y = 0.2519x + 50.506
R² = 0.1139
150 t-stat = 1.9
100
50 US
0
0 50 100 150 200 250 300 350
Population (m)
The relationship is positive and statistically significant at the 90% confidence level. However,
there are clearly many exceptions where countries lie well above or below the trend line. As
in 2010 PWC’s study, the US, for example, scores well below expectations based on its
population size, while Brazil is well above expectations.
More generally, population explains only around 11% of the variation in total World Cup
points (as indicated by the R-squared statistic of 0.1139 in Figure 2) and 2% of the variation
in FIFA world rankings (although, in this case, the relationship, despite being still positive, is
not statistically different from 0). Clearly, population size is not a key variable in explaining
differences in soccer performance: the notion is that if a country possesses strong soccer
traditions, then locating and selecting 11 good players will not prove to be a difficult task
from a small population.
Relationship of national soccer performance with average income levels
Whilst PWC’s analysis found a statistically significant positive relationship between number
of medals and average incomes, as measured by GDP per capita, there is no a similar
significant relationship in the case of soccer (Figure 3).
Although there is a slight upward slope to the trend line, this is not statistically different from
zero (t-stat = 1.5), and GDP per capita explains only around 8% of the variation in FIFA
rankings across countries. The absence of a statistically significant relationship between per
capita GDP and FIFA rankings suggests that richer countries do not necessarily do better than
poorer countries in soccer. This seems to support PWC’s conclusion that advanced soccer
skills can as easily be honed in the favelas of Rio De Janeiro as in expensive sports centres.
Furthermore, since soccer may represent one of the most appealing routes out of poverty for
people in lower income countries, low income could actually serve to increase the pool ofgood players and thus could directly have a positive impact on soccer performance (this
might be the case with Brazil).
Figure 3: FIFA world ranking vs GDP per capita
1600 y = 3.6635x + 900.96
FIFA world ranking points (2014)
Brazil R² = 0.0798
1400
t-stat = 1.55
1200
1000
Switzerland
800
600
400
200
0
0.0 10.0 20.0 30.0 40.0 50.0 60.0
GDP per capita at PPPs (2005) from World Bank
The importance of host country advantage
As shown in Table 2, being the host country has a significant impact on World Cup
performance.
Table 2: Host country performance at the World Cup
Year Host country Performance of host country
1930 Uruguay Won
1934 Italy Won
1938 France Lost in quarter final vs eventual winner (Italy)
1950 Brazil Lost in final vs Uruguay
1954 Switzerland Lost in quarter-final (5-7 to Austria)
1958 Sweden Lost in final vs Brazil
1962 Chile 3rd place
1966 England Won
1970 Mexico Lost in quarter final (vs Italy)
1974 West Germany Won
1978 Argentina Won
1982 Spain Lost in second group phase
1986 Mexico Lost in quarter-final (vs West Germany)
1990 Italy Lost in semi-final (vs Argentina)
1994 US Lost in last 16 to eventual winner (Brazil)
1998 France Won
2002 S. Korea/Japan S. Korea reached semi-finals; Japan lost in last
16
2006 Germany 3rd place (lost to Italy in semi-final)
2010 South Africa It did not make it out of the group stageThe host country has won 6 out of the 19 World Cups so far. Even when the host team did
not win, they generally performed better than expected. England and France won their only
World Cups when playing at home, while Switzerland, Sweden, Chile, US, South Korea and
Japan all performed above their normal level when playing at home.
There is also a ‘host region’ effect that could be a combination of strong home crowd support
and familiar climatic conditions. With the sole exception of 2010, when Spain won the
competition in South Africa, European countries have only won the World Cup when it has
been held in Europe, while Latin American teams have won all the World Cups held in the
Americas. Both the ‘host region’ effect (familiar climatic conditions) and ‘host country’
effect (strong home crowd support) could work strongly in Brazil’s favour in the 2014 World
Cup.
Econometric model results
A more formal econometric analysis was undertaken, in which total World Cup points were
regressed4 against population, average income levels and a ‘host country’ variable based on
the number of times a country has hosted the competition (with values 0, 1 or 2).
Table 3: Model results for explaining total World Cup points by country
DEPENDENT VARIABLE: TOTAL WORLD CUP POINTS BY COUNTRY
Explanatory Coefficient Standard error t-stat Comment
variables
Constant 44.3 19.1 2.3 Statistically
significant
Population 0.09 0.2 0.4 Positive but
insignificant
GDP per capita -0.39 0.76 -0.5 No significant
at PPPs effect
Host country 43.1 16.5 2.6 Significant
dummy positive effect
R-squared = 0.38, Prob(F-stat) = 0.00
From table 3, we can see that the host country variable is highly significant and positive.
Furthermore, when this variable is used as an explanatory variable, the (positive) population
effect, and the (negative) income effect are no longer statistically different from zero.
As suggested by PWC, there could be reverse causality here, so that we should use some care
when interpreting these results: countries more likely to perform well may be more willing to
host the World Cup. Furthermore, larger and richer countries may also be more able to host
big sport events in general. In order to provide further econometric evidence, a vector
autoregression was developed in order to run a Granger causality test. The results are
presented in Table 4.
4 Regression run with Eviews 8Table 4: Granger-causality test (dependent variable: ‘host country dummy’)
Dependent variable: HOST_COUNTRY
Excluded Chi-sq df Prob.
POINTS 13.76083 2 0.0010
PC_GDP_PPP 1.139227 2 0.5657
POPULATION 2.520700 2 0.2836
All 17.29536 6 0.0083
From Table 4, we can see that all the three independent variable together granger-cause the
host-country variable. This result seems to support the reverse causality assumption:
countries with a higher number of scored points, a higher level of GDP per capita and a larger
population are more likely to host the World Cup. At the same time, willingness and capacity
to host may simply be a proxy for the strength of a country’s soccer tradition, which could be
a missing variable in these kinds of regressions, rather than implying a reverse causality
effect per se.
For our investigation, we also wanted to investigate the possibility that teams from countries
with a stronger soccer tradition may benefit from referees’ ‘psychological conditionings’. In
order to do so, we ran a new regression using the current FIFA/Coca-Cola World Rankings as
an additional explanatory variable for testing. We used the current FIFA rankings as a proxy
for ‘psychological conditionings’ that might unconsciously prompt referees to favour
traditionally stronger teams. If the FIFA ranking has a positive and statistically significant
impact on the number of points scored, it’s possible that a controversial decision leading to a
goal is more likely to be validated when it is scored by a FIFA high-ranking countries than by
low-ranking countries. At the same time, though, it may simply reflect the fact the higher-
ranking teams score more goals simply because they are stronger than low-ranking teams.
Table 5: Model results for explaining total World Cup points by country + FIFA rankings
DEPENDENT VARIABLE: TOTAL WORLD CUP POINTS BY COUNTRY
Explanatory Coefficient Standard error t-stat Comment
variables
Constant -12.7 25.0 -0.5 Statistically
insignificant
Population 0.02 0.2 0.4 Positive but
insignificant
GDP per capita -0.74 0.70 -1.04 No significant
at PPPs effect
Host country 27.2 16.5 1.6 Statistically
dummy insignificant
Point_FIFA_RK 0.08 0.03 2.5 Significant
positive effectThe regression results (Table 5) seem to support the ‘psychological conditioning’ view: when
the FIFA ranking is added as explanatory variable, all the other variables become statistically
insignificant; even the host country dummy, which was highly significant in absence of the
FIFA rankings, becomes not statistically different from zero. In other words, FIFA ranking
takes precedence over all the other variables; the adjusted R-squared also improve
significantly, increasing from 0.31 to 0.43, and implying that the model does a better job in
explaining the variation in the dependent variable when the FIFA ranking is used as extra
explanatory variable (although three out of four variables are not statistically significant).
The positive effect of the host country dummy on performance is significant even when
implementing the regression exercise using the FIFA world rankings as dependent variable.
For the same sample of countries, the results are shown in the table below.
Table 6: Model results for explaining FIFA world ranking points
DEPENDENT VARIABLE: FIFA WORLD RANKING POINTS
Explanatory Coefficient Standard error t-stat Comment
variables
Constant 885.5 50.3 17.6 Statistically
significant
Population -0.03 0.36 0.94 No significant
effect
GDP per capita 1.7 1.99 0.85 No significant
at PPPs effect
Host country 122.7 54.8 2.23 Significant
dummy positive effect
R-squared = 0.25, Prob(F-stat) = 0.05
The host country dummy is still significant, although less significant than in Table 3. This
suggests that, besides picking up home advantage, the home country effect may also pick up
something about the strength of the soccer tradition in different countries. The other results
are similar to those in Table 3, with the population and the income effect still not significant.
That said, the three variables together seem to be Granger-causing the FIFA rankings (Table
7), implying that their past values jointly help better predict future FIFA rankings:
Table 7: Granger-causality test
Dependent variable: POINTS_FIFA_RK
Excluded Chi-sq df Prob.
HOST_COUNT
RY 4.417167 2 0.1099
PC_GDP_PPP 6.326608 2 0.0423
POPULATION 1.431441 2 0.4888
All 13.12025 6 0.0412
Another point in common between the two models, which emerges from both Table 3 and
Table 6, is the low explanatory power of the equations: 38% for historic World Cupperformance and just 25% for the FIFA rankings. This means that there is a lot of variation which our models are not able to capture with those standard indicators. However, as PWC put it, this should be reassuring to the fans: no-one wants sporting results to be too predictable (not even betting agencies, otherwise potential pay-outs would be unattractive even for the keenest bettor!). Conclusions and implications for the 2014 World Cup Our up-to-date analysis, based on a previous study by PWC, largely validates their findings that econometrics can offer partially useful insights into past and potential future World Cup performance. In particular, host country and host region effects seem to play an important role in explaining variation in World Cup performance. Based on the science, Brazil is the (strong) favourite for the 2014 World Cup, as it is not only supported by its own people (host country and regional advantage), but it also has a strong soccer tradition and a high FIFA ranking. Other Latin American teams such as Argentina and Uruguay also have the potential to do relatively well in 2014 compared to past World Cups. This study shows that their low average income levels are no barrier to World Cup success and nor is population size a critical factor after adjusting for host country effect (and it is even less critical after adjusting for the FIFA rankings). Among European teams, Spain is the favourite: it is the only European team ever to win the World Cup outside of its home region and it ranks high on both the FIFA ranking and the historic World Cup performance (as of May 2014). Germany and Italy are also strong contenders based on their historic World Cup performances (with Germany having a slightly better chance of winning than Italy). England and Portugal remain a good bet for reaching the quarter finals, or even the semi- finals, based on both their FIFA rankings and our research analysis, but probably not to get beyond that given that they have never achieved this (excepting when England hosted the tournament in 1966). Indeed, even to get to the quarter-finals would match the best World Cup result that those three teams have ever had outside Europe. But soccer is clearly “a funny old game”, and we are relieved that other factors (chance) still seem to play an important role!
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