The map shows the distribution of players across the whole world. Design 1 was plotted using p5.js, to have an idea of the distribution of three different statistics, which are the distribution of left or right-footed footballers (slider at left position), the distribution of players who own a passport that corresponds to the national league in which they are active in (middle position of the slider), and the distribution of players of different positions/roles on the field (red are goalkeepers, green are defenders, blue are midfielders and forwards are white. The plot was allowed to 'dance' around because some of the points which are stacked can also have an opportunity to expose themselves. These players were from five European leagues mentioned in earlier posts.
In total, 3254 football players were under study. Figure 1 shows the distribution of left to right-footed players when the slider is placed at the far left. The majority of the players are right-footed (green dots), while a handful of them are left-footed. Players from countries like Canada, New Zealand, Australia are shown to be right-footed, and most players from European, South American, and African countries. There does not seem to be a specific region where left-footed players occur more frequently.
When the slider is in the middle position, the player's passport is compared to the league in which they are active. This statistic serves to locate those born in one country but practice football as a profession in another country. Pink means players play in their home country. White means the players hold of that country left to play abroad. A clear and interesting finding is that the African continent has most players leaving their country to play abroad compared to other places in the world. European countries seem to have national football competitions in which the majority of the players also originate from the country in which the competition is held. A lot of hypotheses can be found for this. Socio-economic aspects but also the popularity of football in a certain region may have an influence.
The slider in the outmost right position shows the four different roles of the players on the field (goalkeepers, defenders, midfielders, attackers). This representation can capture the big trends. For example, an interesting point to see is that sub-Saharan African countries barely don't have any red dots (goalkeepers) on a professional level.
Design 1
Design 2 is another design that is implemented at this stage. It is the combination of a line plot with a bar chart on a time axis that indicates the game's duration, as was discussed in earlier posts. The smart passes in all premier league games are compared with the shots produced in all premier league games over the duration of a soccer match. A smart pass is defined as 'a creative and penetrative pass that attempts to break the opposition's defensive lines to gain a significant advantage in attack', according to the Wyscout glossary.
Design 2 shows that, at least to some extent, the number of smart passes in a game does seem to go hand in hand with the number of shots that can be produced in the period in which the smart pass is given.
An extra feature added to this visualization is to include not only smart passes but also the 'trivial passes' and to see whether the smart passes really do result in more chances, hence shots created, than trivial passes. Or can teams also get more shots towards the goal while they simply manage to give trivial passes in a moment during the game? Based on this view, the shot pattern is more similar to the smart pass pattern to the trivial pass pattern green). Hence, a smart pass is more likely to result in a shot than a trivial pass.
Design 2
