Who You Calling Weak? Draft Class Variance

This year’s NHL draft class is weak. I don’t follow junior prospects closely, but that’s what I’ve heard from more knowledgeable sources. It’s a fair claim; Nolan Patrick and Nico Hischier seem talented but not among the game-changing talents that have recently been drafted first overall.

However, it’s harder to judge the draft class past the very top. Scouting is hard, especially for hundreds of prospects across the world. It’s possible that while there is no clear star in the draft class, the rest of the draft is as strong as ever.

That would have big implications for draft strategy. The conventional wisdom is that teams may trade more picks this year because they believe the weak draft class makes the picks less valuable. But if the draft is typical after the first few picks, that would be a poor use of assets.

We don’t yet know how well this year’s draft class will do in the NHL. But, we can use historical data to ask questions that establish expectations: how well does each draft class typically perform, and how much does this vary by year?

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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

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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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