Between some of the games I had a go at replicating a plot from liberation.fr on the connections between Euro 2016 players and the country of birth using the
circlize package in R. As with the previous post, the colours are based on the home shirt of each team and data scraped from Wikipedia. The values in the parenthesis represent the total number of players born in the respective country, which dictates their ordering around the circle. It is interesting to see just how many players represent countries that they were not born in. Only Romania has a 23 man squad completely full of players born in the country and no other Romanian born players representing other nations.
I also modified some the my
ggplot2 mapping code in my previous post to plot the links between players place of birth and the capital city of their national team: Click on the images if you want to explore. Here is an expanded view to illustrate all the longer distance relationships: All the code is up on my Github.
This weekend I was having fun in France watching some Euro 2016 matches, visiting friends and avoiding Russian hooligans. Before my flight over I scraped some tables on the tournaments Wikipedia page with my newly acquired
rvest skills, with the idea to build up a bilateral database of Euro 2016 squads and their players clubs.
On the flight I managed to come up with some maps showing these connections. First off I used
ggplot2 to plot lines connecting the location of every players club teams to their national squads base in France. The path of the lines were calculated using the
gcIntermediate function in the
geosphere package. The lines colour is based on the national teams jersey, which I obtained via R using the amazing
extract_colours function in the
rPlotter package.I was not entirely convinced that this plot is too effective as the base camp of each team in France is not particularly common knowledge. This led me to create a new plot with a link from the players club teams to their nations capital city.
This shows some clear relationships, such as between the Iceland players and clubs in Scandinavia and clubs in northern England and Scotland with the Irish teams. Here is another plot to show some of the more distant relationships for players from non-European clubs
Finally, I had a go at trying to match my old World Cup circular plot using the
chordDiagram function in the
I ordered the countries according to their UEFA coefficient, which is based on the performance of club teams in European competitions. Most players playing abroad are based in teams in the top leagues of England, Germany, Italy or Spain. Players based in the English leagues make up most of the squads for teams from the British Isles. There are sizeable numbers of the Austrian and Swiss squads playing for clubs in the German leagues and Croatian players in Italy.
All the code for the scraping the data and producing the plots are on my Github. Check out my next post for more on the players at Euro 2016.