Redefining the Meaning of Winning a Draw

With the new interest in delving deeper into statistics, the one most frequently brought up on broadcasts is faceoff win percentage. While it is important to win draws, the reasoning behind it is often explained incorrectly.

Many times, the broadcast discusses face offs as a win/loss scenario, with no context as to what happens after the draw. I cannot tell you how many times “This goal happened because they won/lost the draw,” is said on a broadcast when there were 10+ passes made and 30 seconds of game time played after the puck dropped.

So, let’s get to the actual purpose of a face off: win possession of the puck. As we know from other analysis, the team that possesses the puck generally dictates the game. Continue reading

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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