Machine Learning the Crew

Machine Learning the Crew

Machine learning is so hot right now and if Skynet is going to destroy all humans, it should at least know a little bit about Major League Soccer’s Columbus Crew. To wit, I created a machine learning model to classify which position in a Gregg Berhalter 4-2-3-1 formation a player most likely played in during a single game.

I chose the Crew for a couple reasons. First, they are my favorite team. Second, they had consistent coaching for a long period of time with a defined style of play. The latter is very important, as the model has to be trained well in order for the results to make sense. Since the Crew almost always played a 4-2-3-1 that relied on ball possession to disorganize the defense and create goal opportunities (get used to that phrase USMNT fans) it was a perfect test of whether this kind of thing could be done.

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Roster Consistency in MLS: How much does it actually matter?

Roster Consistency in MLS: How much does it actually matter?

Recently, while trying to put a finger on what exactly happened with FC Dallas last season, “lack of squad consistency” was mentioned as a reason for their remarkable plummet down the table. This made me wonder both how much their roster actually changed from week-to-week and how much week-to-week rotation is the right amount. 

The following is my initial exploration into roster consistency.

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