To produce data for the empirical analysis, the decisions made in section 1 were followed up, and one vector of numbers containing the chosen countries FIFA rank  was constructed. In addition, the UoO-measure (named ρ_L according to the notation in [6]) was calculated.

Fortunately, an alternative project [7] had performed the necessary calculations, and these numbers  were used. The numerical information is so limited in size that all numbers are given in table 1

As table 1 indicates, all chosen countries were European. Surely, this may be seen as a choice of convenience, but European football is still (obviously) the most economically significant globally, and as such, relevant.

Furthermore, different chosen countries are tried to cover both small and large countries, as well as good and bad (football-wise) – trying to get a reasonable spread in the observations.

As can be observed, the best team GERMANY (no. 1 on the FIFA rank1 as well as number 99 on the same rank are included. Finally, a reasonable spread in the UoO-values is also observed; ranging from Croatia (16,94% – low UoO) to AUSTRIA (37,31% – high UoO).

Hence, it is assumed that the sample, at least to a certain degree can be considered representative.
Based on the data in table 1, a simple linear regression model with UoO as the independent- and FIFA-rank as the dependent variable is formulated.

Parameters β0, β1 in this simple linear regression model – with standard assumptions on error terms ε_i:

ρ_L,i = β₀ + β₁ ∗ FIFA – rank_i + ε_i       (1)

are estimated in equation (1). The results are shown in figure 1.

Figure 1: A simple linear regression; ρ_L on x-axis, FIFA-rank on the y-axis.