Improving Opposition Analysis by Examining Tactical Matchups

On Monday, I introduced some work on quantifying and identifying team playing styles, which built upon my earlier work on identifying individual playing styles. Today we’re going to discuss how to make this data actionable.

What are the quantifiable traits of successful teams? What plays are they executing that makes them successful? How can we use data to then build a style of play that is more successful than what we’re currently doing? The way we bridge the gap between front office and behind the bench is by providing data to improve their matchup preparation, lineup optimization, and enhance tactical decisions.

This is what I mean by actionable: applying data-driven analysis and decision-making inside the coach’s room and on the ice. All data is from 5v5 situations and is either from the Passing Project or from Corsica.

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Friday Quick Graphs: The Dangers of Binning Data

If you’ve ever read a little math, you likely know the dangers of binning continuous data when testing relationships between two variables. It is one of the easiest and most common mistakes that an amateur statistician might make, largely because, intuitively, it seems like it should make sense.

But it doesn’t, and here’s why.

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