In anticipation of the NWHL All-Star Game (Feb 11-12), I wanted to look at which NWHL players contribute the highest % of their team’s shots on goal. This is simply the number of shots the player has taken, divided by the number of shots the player’s team has taken.

As the graph shows, Brianna Decker and Shiann Darkangelo lead the league in % of team shots at 19% each. Haley Skarupa, a rookie, leads the Conneticut Whale at 15% of the team’s shots. This is doubly impressive, as the Whale also lead the league in shots. Madison Packer leads the New York Riveters at 12%.

The code for this graph can be found on my Github page.

# 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.

# Digging Into Coaching Numbers

There are four takeaways I hope to show in this post:

1. The amount of games a coach has in any given tenure with a team appears to depend more strongly on goal statistics than shot statistics
2. Coaches are strongly incentived to coach their team to score goals
3. Coaches whose teams give up many goals do not last
4. These are all fairly obvious conclusions, but are worth proving

# Examining League-wide Offense and Defense

Over the holidays, I created some charts that show the distribution and density of Shots and Scoring Chances for Per 60 for each team. There’s a lot of information in these charts, so I chose a few to inspect further.

First, we’ll look at the league-wide chart for Shots and Scoring Chances For. Keep in mind that each individual dot is a game, and the contour lines show the density of the dots (i.e. how close they are to each other).

# Jakub Voracek’s Goal Drought

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Jacob Voracek is having an especially poor season.  After averaging above 1.6 Primary Points per 60 at 5v5 the last few seasons, he’s down to .6 in 2015-16.  Voracek’s assist rate is down, and most prominently, he has only scored one goal at 5v5.

What is causing this run of poor form? The most direct route of analysis is to examine his shot metrics. How does Voracek’s 2015-16 season stack up against his previous seasons?

# Using Cluster Analysis To Identify Player Position

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What did you think the first time you watched hockey? Did you know the difference between a forward and a defensive skater? Could you tell the difference just by watching? It’s likely that some outside factor (a friend, the play by play announcer, a graphic on the broadcast) alerted you to the fact that NHL teams use more than one type of skater.

But, say that outside variable never intervened, and you were left to your own devices. How long would it take for you to develop the idea of “forwards” and “defensive skaters”? Would you come up with your own classifications? Would you differentiate them at all?

# Distribution of Quality of Competition and Teammates Metrics

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The analysis community has studied these metrics in various ways. The purpose of this post is to lay out the way I understand the metrics, and identify areas of additional research.

The effects of competition and teammates on players are not new concepts in hockey.  We hear about it all the time in analysis and conversation: “Jonathan Toews is deployed by his coach to specifically shut down the top players of the opposition”,  “4th liners play against the opposing 4th line”, “Sidney Crosby makes his teammates better”, etc. etc.

Having analyzed the metrics used to quantify quality of competition and teammate, I came to two conclusions.

# Rate Metrics Matter

The other day, @Moneypuck_ and @SteveBurtch had a conversation about the Prospect Cohort Success Model:

While the PCS model is interesting in its own right, I found the discussion about the methods we use to analyze players to be interesting as well.