Econometrics and the World Cup. When the sacred and the profane go hand in hand

Transcription

1 Econometrics and the World Cup When the sacred and the profane go hand in hand

2 Overview In 2010, Price Waterhouse Coopers (PWC) conducted research 1 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 higherranking teams score more goals because they are stronger than low-ranking teams. 1 PWC, 2010 What can econometrics tell us about World Cup performance?

3 Total World Cup points ( ) 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 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 1 st on the current FIFA rankings but only 6 th in terms of historic performance (in 2010, Spain ranked 2 nd and 7 th, respectively). 250 Figure 1: Historic World Cup performance vs current FIFA rankings FIFA world ranking points (May 2014)

4 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 ( ) 2 Current FIFA world ranking (May 2014) 3 Rank Country Points Rank Country Points 1 Brazil Spain Germany Germany Italy Portugal Argentina Brazil England 97 5 Colombia Spain 96 6 Uruguay France 86 7 Argentina Netherlands 76 8 Switzerland Uruguay 66 9 Italy Sweden Greece Serbia England Russia Belgium Poland Chile Mexico USA Hungary Netherlands Czech Republic France Austria Ukraine Portugal Russia Belgium Mexico Chile Croatia Switzerland Cote d'ivoire Paraguay Scotland Romania Denmark Denmark Egypt USA Bosnia and Herzegovina South Korea Sweden Croatia Algeria Scotland Ecuador Cameroon Slovenia Bulgaria 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: 3 Source: FIFA/Coca-Cola rankings from

5 Total World Cup points (3 = win) Figure 2: Total World Cup points vs population 250 Brazil y = x R² = t-stat = US 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 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

6 FIFA world ranking points (2014) 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 Brazil y = x R² = t-stat = 1.55 Switzerland 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 Germany 3rd place (lost to Italy in semi-final) 2010 South Africa It did not make it out of the group stage

7 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 regressed 4 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 Statistically significant Population Positive but insignificant GDP per capita at PPPs No significant effect Host country dummy Significant 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

8 Table 4: Granger-causality test (dependent variable: host country dummy ) Dependent variable: HOST_COUNTRY Excluded Chi-sq df Prob. POINTS PC_GDP_PPP POPULATION All 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 higherranking 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 Statistically insignificant Population Positive but insignificant GDP per capita at PPPs No significant effect Host country dummy Statistically insignificant Point_FIFA_RK Significant positive effect

9 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 Statistically significant Population No significant effect GDP per capita at PPPs No significant effect Host country dummy Significant 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 PC_GDP_PPP POPULATION All 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

10 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 semifinals, 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!