I’m in the very early stages of a new project that could potentially be interesting – visualizing similarity of players through a multi-dimensional scaling. The quick version of the method is to take all the player stats I have, scale them down into two dimensions, and calculate the Euclidean distance between all of these points. Then I can plot those points in a typical Cartesian plane. Theoretically, players close to each other in the plot should have similar stats.
The proof-of-concept was doing it with all of the players in my dataset, and highlighting them by position. I see really good clustering with goalkeepers, good clustering with strikers and defenders, and middling clustering with midfielders.1 Here’s the plot:
The next plot is where I need help. The dimensions in this type of plot aren’t necessarily meaningful, but you can see in the full plot that lower right tends to be defenders, left center tends to be attackers, and midfielders are kinda sorta upper right. Here’s the zoomed in striker plot with selected players highlighted:
I labeled some major names, some names I find interesting, and then tried to highlight some names on the margins of the plot. When you do this type of plot, you don’t define the dimensions, but they potentially mean something. So I’d appreciate any thoughts anyone has on what we might be seeing here given the players I’ve labeled. Tweet them to me @Soccermetric and life will be good.
- This makes sense because wingers are basically attackers, and holding midfielders are similar to defenders. We’d expect to see a holding mid have more in common with a defender than a winger ↩