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 - Dun & Bradstreet
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.pdf
Alternative 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.com
Figure 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 of
good 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 stage
The 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 8
Table 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 effect
The 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 Cup
performance 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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