Why Possession is the Key to Zone Exits

If there’s anything you know from neutral zone analytics, it’s probably this: carry-in zone entries are better than dump-ins. In the linked piece, Eric Tulsky finds that “maintaining possession of the puck at the blue line (carrying or passing the puck across the line) means a team will generate more than twice as much offense as playing dump and chase”.

But what about zone exits? Is possession equally important there? Work by Jen Lute Costello suggests that it is, but her data was limited to one playoff series. Today, I’ll expand on her work to show that maintaining possession is crucial for successful zone exits, and breakouts should be structured with this in mind.

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Introduction to the Transition Project

This is one of my favorite plays:

Almost every team is coached to make their opponent fight for every inch. Skjei’s end-to-end rush cuts through those defenses and leaves his team in a much better position than when he started.

But just how much better off did he leave them? How does that compare to alternative outcomes? And which players are the best at making these plays? We have unanswered questions about transitional play. We’d like to study them in more detail, but the gif above doesn’t appear anywhere in the league’s play-by-play data to help conduct analysis.

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Identifying Playing Styles with Clustering

One of the aspects of player performance that is discussed ad nauseam is chemistry. How well do certain players elevate their performance with one player or another due to some inherent ability to find the other on the ice? To know what a teammate is going to do? However, very little has been done to analyze this phenomenon. In this piece, I posit that by identifying playing styles, something that’s been done in the NBA, we can quantify how well certain players will complement one another.

All data is from 5v5 situations from the 2015 – 2016 and current season, totaling almost 900 games from the Passing Project volunteers and Corey Sznajder. Special thanks to Asmae for her guidance throughout this piece.

I want to stress that this is a first foray into this type of analysis and simply because a player has a different style than what I’ve named (which are relatively arbitrary) it doesn’t mean they are necessarily better than another player. Players may have similar styles, but some will simply be more effective due to their ability. Finally, given that each day we accumulate more data, a player with a smaller sample size could find themselves in a different cluster in future analysis.

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Mikael Granlund, Playing Behind the Net, & Predicting Goals

Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoring rates. I’m going to do this by quantifying the pre-shot movement that occurs when a player is on the ice. Finally, I’ll spend some time discussing certain forwards/teams that caught my eye. All data is from 5v5 situations and special thanks to Dr. McCurdy for pulling the on-ice player data for me. All non-passing project data is from Corsica.

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Coaching Analysis Part 2: Metropolitan Division

Note: This is Part 2 of the series on coaching analysis. Part 1 is here.

In this post, I’ll do a brief review of each team’s coach history from the current Metropolitan Division. These graphs only show a team’s performance in 5v5 situations from 2005 to 2016. The vertical lines indicate when a season begins. The horizontal line shows the 50% mark, where a team would be if it had as many shots for as shots against. The bold line is a smoothed representation of the team’s shot percentage. The faded bands around the bold line indicate 95% confidence intervals. These intervals show the uncertainty around the smoothed estimation of the data.

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