In preparation for the best sports event, which will be held in Russia in a few months, I prepared some visualizations that shows the players of each of the countries that will be playing the FIFA World Cup Russia 2018.
The players are organized as if they form a constellation of stars, by using dimensionality reduction called Princial Component Analysis (PCA). With these technique I could represent more than 30 skills of the players (for example, acceleration, ball control, dribbling, speed, etc.) into a 2-D plane. The main goal of PCA is to reduce the dimensionality, while retaining the variation present in the dataset, up to the maximum extent.
Hence, having one player as a reference, for example Messi, we would say that players close to him are also good players, while players who are far from them may not be as good.
You can take a look at this link, which also contain more details about the methodology for each visualization: webdocs.cs.ualberta.ca/~serranos/fifa.html
Information about players is preprocessed with the python script players_pca.py, which generates the files used by the webpages and located in the data/ folder.