If you are joining this series for the first time, be sure to check out Part 1 and Part 2 in this series on machine learning in Major League Soccer. To recap, using a k-means clustering technique (a machine learning technique to look for similarities within data and group things based on closeness), we grouped MLS players into 27 unique player-roles.
A Brief Intro...or Recap?
You might be asking yourself the question of “why are you doing this?” First and foremost, I believed that the methods used currently to group players together were too broad and not refined enough to describe and distinguish between players like Zlatan Ibrahimovic and Adi.