Exploring the Impact of New NHL Coaches and GMs

With Todd Richards being let go after a disastrous 0-7 start by the Blue Jackets, and Bruce Boudreau on the hot seat (heck, he might be out of a job by the time I finish writing this), coaching is once again in the spotlight. After Richards was fired, I went on a mini rant about how I believe having a good GM is more important than having a good coach, and while I still believe this is true, I wouldn’t be a data person unless I tried to prove it.

This project has many parts to it. The first, which I’ll be doing here, is just looking at the breakdown of Scoring Chances For% compared to Coaches and GMs in the early days of their tenure, i.e. right after being hired. Scoring Chances, to simplify things, are basically “more dangerous shots” (click here for a more rigorous definition).

To start, I needed data. I pulled all 30 teams from 2006/07 to 2015/16, and coded each season by what kind of organizational changes happened within. This gave me 331 data points, as there were often midseason coaching or GM hirings to account for.

The states broke down like this:

1) No Change – 64%
2) Hired a new Coach – 21.5%
3) Hired a new GM – 7.3%
4) Hired a new Coach & new GM – 7.3%

In looking at the data, some patterns quickly emerged. The first two years of tenure in either role were where the most change, for better or worse, happened.

Because I had so much data in the No Change category, I wanted to see if there was any sort of trend year over year for the control group.

No Changes - Coaches SCF Delta YoY

No Changes - GMs SCF Delta YoY

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Practical Concerns: A better way to evaluate defensemen

Credit: Michael Miller - Creative Commons

Credit: Michael Miller – Creative Commons

I was watching hockey a few nights when I heard NBC’s Pierre McGuire describe a rookie defenseman in glowing terms. It was the same kind of praise he used to shower upon Dion Phaneuf about 10 years ago, and this young player had very similar attributes to an early-20s Phaneuf: a huge frame, a huge slapshot, and a willingness to use both in equal measures.

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xSV% is a better predictor of goaltending performance than existing models

This piece is co-authored between DTMAboutHeart and asmean.

Analysis of goaltending performance in hockey has traditionally relied on save percentage (Sv%). Recent efforts have improved on this statistic, such as adjusting for shot location and accounting for goals saved above average (GSAA). The common denominator of all these recent developments has been the use of completed shots on goal to analyze and predict goaltender performance.

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Revisiting Imbalanced Drafting Strategies

Photo by user

Photo by user “Tsyp9”, via Wikimedia Commons.

At Hockey-Graphs, we like to provide data-based answers to questions. It’s what we do. But it’s also good to recognize issues in the analytics world that haven’t yet been addressed. Sometimes that’s the case because we don’t have the data we need available, and sometimes it’s because the question has yet to be properly framed. It’s important to know what we don’t know, and to talk about it regardless.

There has been some great draft work done at our site and elsewhere in the last few years, and one of the findings has been the volatility of drafting defensemen relative to forwards. Couple that with claims that forwards have more of an impact on shot rates than defensemen, and one would be tempted to claim that avoiding defensemen altogether would be a solid draft strategy (though I’ll note that most analysts think this is taking the conclusion too far).

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Blue Jackets Coach Todd Richards’ Firing, & Why the History Doesn’t Agree With Mike Harrington

Photo by user

Photo by user “Arnold C,” via Wikimedia Commons; altered by author

The Columbus Blue Jackets made a bold move today, firing their coach of 3 1/2 seasons Todd Richards in favor of noted firebrand and Brandon Dubinsky fan John Tortorella. The move, riding the coattails of a 0-7 start for the Jackets, was done unusually early in the season, so unusually I decided to spill a little ink on it.

Around the same time I was rounding up the data, the esteemed (Buffalo Baseball Hall of Fame!) Sabres writer and analytics pot-shotter Mike Harrington decided now was the time to defend a decision that made little sense, about a team he doesn’t write about. It started with a reasonable tweet from Friend of the Blog Micah Blake McCurdy:

At which point Harrington followed:

Alright, Mike, let’s take a look at the “numbers that count,” according to you. There’s a fun history here.

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Some Things to Know about One-Goal Games

Photo credit: Flickr user pointnshoot. Use of this image does not imply endorsement.

One of these teams will probably win by one goal.

Last season, as hockey analysts struggled to explain how a possession-dominant Kings team failed to make the playoffs while Anaheim and Vancouver topped 100 points, there was a lot of discussion surrounding the role of one-goal games in the standings. LA’s disappointing season was largely dismissed as bad luck, with an argument that went something like this: the outcome of a one-goal game is effectively random, and the Kings’ 13-9-15 record in these games (against the 33-1-7 and 22-4-5 performances of the Ducks and Canucks, respectively) was the difference in keeping them out of the postseason. I wasn’t entirely convinced by this, but it got me thinking about the randomness of close contests. How random are one-goal games, and how significant a problem is this for people trying to use numbers to understand why some teams win and others don’t?

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NCAA Defencemen Graduation Rates and NHL Success

Northeastern UMass Hockey 8657.jpg
Northeastern UMass Hockey 8657” by SignalPADFlickr. Licensed under CC BY-SA 2.0 via Commons.

I’ve been playing with some NCAA prospect numbers lately and I had a hypothesis.

To set the stage, under the current CBA NHL teams have up to 30 days after a prospect leaves school to sign their drafted prospects to an NHL Entry Level Contract (ELC), or by August 15th after they’ve graduated.

What this means is that teams have an incentive to encourage players they think will become NHLers to sign as soon as possible. The trade-off with signing an NCAA player is the player loses their amateur eligibility and automatically has to move to another league. NCAA prospects typically move on to the AHL or NHL but it is not unheard of to see prospects take a side-step to the CHL in the odd circumstance.

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Hockey-Graphs to take part of Vancouver Hockey Analytics Conference 2016


Image courtesy of picswallpaper.com

Two of Hockey Graphs contributors, Josh Weissbock and MoneyPuck, along with two Simon Fraser University professors, Tim Schwartz and Oliver Schulte, are organizing the first ever Vancouver Hockey Analytics Conference (#VanHAC).

This is not the first hockey analytics conference, as there have been a few popping up in recent years,  including those in AlbertaOttawa, Pittsburgh, Washington DC and Rochester.

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The relationship between competition and observed results is real and it’s spectacular

Raw Comp Impact


There has been much work over the years looking at the impact of competition on player performance in the NHL. Prompted by Garret Hohl’s recent look at the topic, I wanted to look at little deeper at the obvious linear relationship between Quality of Competition and observed performance.

The results are a mathematical relationship between competition and observed, which could provide insight into player performance over short time frames. In the long run, the conclusions drawn by Eric Tulsky still hold. The impacts of facing normally distributed Quality of Competition (QoC) will wash out the effects over time. But this should not preclude consideration and even adjustments for QoC when looking at smaller sample sizes.

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