To answer this question, we look at the so-called GINI coefficient.
This is primarily used in economics to reflect equality or inequality.
Often the coefficient is applied to the distribution of income to check how equal or unequal assets are distributed in a society.
If the GINI coefficient is 0 all persons would have exactly the same, but if the value is 1, one person would have everything and the others nothing.
We will now apply this indicator to the squad values to check how the distribution has developed.
In Figure 6 we are able to see the result:Figure 6: Development of the GINI-Coefficient from 2009 to 2018Inequality has increased in all five leagues over the last ten years.
The strongest growth was in France, where the GINI coefficient increased from 0.
29 to 0.
The greatest degree of inequality is found in Spain.
Here the coefficient has risen from 0.
46 to 0.
The relatively lowest inequality exists in Germany, but here, too, inequality has increased.
In Italy, there was a sharp decline of 0.
06 to 0.
The lowest increase was in England, where the coefficient only increased by 0.
01 to 0.
The dashed black line represents the average over all five leagues.
In 2009, the GINI coefficient was at 0.
41 and in 2018 at 0.
It is noticeable that there was a continuous increase until 2014 to 0.
Since then, the value has largely stagnated.
One reason for this could be the introduction of UEFA’s financial fair play, which meant that the top clubs could no longer expand their investment volume .
The statistical relationship between the average squad values and the sporting successWe have already noticed that the financially stronger leagues dominate the European scene and at national level the clubs with more valuable squads are also better off.
Now we check the statistical correlation between the value of the squad and the sporting success in terms of points achieved in a season:Figure 7: Correlation oefficients points and squad valueIn Figure 7 we see that Pearson’s correlation coefficient is about 0.
69 across all leagues.
This is a strong positive relationship.
The correlation coefficient first indicates that there is a relationship, but does not yet say anything about causality.
This correlation exists in all leagues and is strongest in Spain, while the correlation is relatively low in France.
Nevertheless, there is a strong positive correlation between the number of points scored and the squad value in all leagues.
Figure 8: Relationship between squad value and number of points per seasonIn Figure 8, this relationship is visualized.
It can be clearly seen that with an increasing squad value on average, the number of points scored also increases.
It can be assumed that there is a partial correlation here.
If a team like Leicester City unexpectedly competes for the championship, the market values of the players will rise without the team has invested in new players.
In this example, the good performance would be responsible for an increase of the squad value.
Generally, clubs make transfers to expand their sporting potential and we assume that a higher squad value leads to greater sporting success.
We can check this assumption with a regression:Figure 9: Regression with Points as dependent and squad value as independent variableFigure 9 shows the result of the regression.
The points are the dependent and the squad value the independent variable.
The adjusted R-squared is 0.
This means that approximately 66% of the variance of the dependent variable, that is, the points, can be explained by our independent variable.
Furthermore, we can see from the P-value that the result is statistically significant.
ConclusionIn the introduction, we have seen that the top five European leagues dominate financially as well as athletically.
In addition, this cohort continues to grow dynamically, while the remaining leagues show a slight downward trend.
Therefore, it can be assumed that the sporting dominance continues to manifest itself.
In a comparison of England, France, Germany, Italy and Spain it is noticeable that England has by far the highest squad values and that dominance continues for the last ten years.
Due to the current development, it is unlikely that another league will take first place in the near future.
In the national leagues we have found that the distribution of squad values is becoming increasingly unequal.
The greatest increase in inequality occurred in France, while the absolute level is highest in Spain.
In the last part, we statistically checked the correlation between squad value and athletic success.
There is a strong positive correlation, which allows two conclusions.
The sporting dominance of the five European top leagues will increase rather than decrease in the near future due to the financial potential.
Furthermore, due to the increasing inequality in the national leagues, the dominance of top teams will continue to exist.
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