Young Player Growth, MLS Team Growth, and Other Machine Learning Nuggets

Steve is proud of his first project.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. Both…