Fig 1.
Trend of spending in the global football player transfer market.
The trend of average annual expense, revenue and volume (sum of expense and revenue) of the elite clubs in the transfer market.
Fig 2.
Inequality of financial ability of professional clubs.
A: Distribution of annual club expenses in euros. B: Cumulative distribution of annual club expenses. Clubs are sorted in descending order by the transfer expense, and the percentage of total expense was plotted against the corresponding percentage of clubs. The dashed line denotes the situation where all clubs spend the same amount of money in the player transfer market. C: Gini coefficient of annual transfer expense, revenue and volume of the clubs.
Fig 3.
Degree distributions of the global football player transfer network.
A: The in-degree and out-degree distributions of the network. B: The in-degree/out-degree relation for each club in the transfer network. C: The distribution of excess degree kex of all nodes in the transfer network. The standard deviation of kex is 5.7.
Fig 4.
Rich-club coefficients in the player transfer network.
The network fails to show the rich-club phenomenon ϕnorm(k) > 1 for high degree nodes.
Fig 5.
Match performance of the elite clubs.
A: The distribution of average league game points. B: The distribution of 5 year aggregate IFFHS CWR points. C: Scatter plot of the two match performance measures.
Table 1.
Kendall’s Tau between match performance and profitability of clubs.
Table 2.
Kendall’s Tau between the money flow of clubs at the transfer market and their match performance.
Table 3.
Kendall’s Tau between network properties and club functionalities in the player transfer network.
Table 4.
Kendall’s Tau between network properties and club functionalities in international and domestic transfer networks.
Table 5.
Kendall’s Tau between network properties and club functionalities in loan and transfer networks.
Table 6.
Classification of the 24 leagues according to the relationship between match performance and transfer profitability of their teams.
Table 7.
Kendall’s Tau between network properties and club functionalities in different league categories.