Using NHL Coaching Changes to Identify Historically Good and Bad Coaches

Iron Mike no like. - Photo by "Resolute", via Wikimedia Commons; altered by author

Photo by “Resolute”, via Wikimedia Commons; altered by author

Having now looked at the overall effect a coaching change might have on a team, and identified some outstanding examples where a coaching change had a drastic impact on a team, it’s now time to shift over to some juicier matters. For the most part, I don’t think one coaching change is necessarily sufficient to say a coach is good or bad; there is a possibility the previous coach was just that bad. But if the coach returns the same signal a couple of times or more, you are probably getting closer to a true reading on what they might bring to the table.

Across the 140 or so coaching changes these last 60 years where both coaches led the team 20+ games, there were 69 coaches who were a part of that change twice or more (which, to me, is quite a remarkable number). The full list, followed by an explanation of the measures:
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What to Expect When You’re Expecting: Does Switching NHL Head Coaches Make a Difference?

Bruce Boudreau

Photo by Matthew Miller, via Wikimedia Commons; altered by author

How good do you feel because your team has a new coach? I mean, really…it’s almost like a new-car smell. So many possibilities – This time, things will be different. With the exception of coaching changes due to disastrous, unexpected things, the typical hockey fan was ready for that moment, and were happy to see the coach go. But is that eagerness for change based on real results?

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Is it time to appoint a new jester?

Toronto -with its high profile in the media combined with some questionable management- has consistently been the brunt of jokes over blogs, message boards and twitter from other fanbases.

Recently the Toronto Maple Leafs has made a bunch of savvy, low-risk, high-potential steps both in management and player personnel to improve their team. While they are still a distance away from being a contending team, the steps taken are not those that the online hockey community has grown to love about Toronto.

With this knowledge and the offseason nearly in our rearview mirror, it is time for Hockey-Graphs to ask its analytically inclined following:

All teams in poll came from an unofficial nomination survey I conducted on twitter.

Revisiting the NHL Regression Predictions from January 1st

Photo by “User:Zucc63” via Wikimedia Commons, modified by author

If you’ll remember, one of the inaugural posts here was a regression prediction piece, using a combination of PDO and Fenwick Close to see who might improve or decline over the latter half of the season. I decided to put together a table of the teams I predicted would negatively or positively regress, just using the aforementioned data:

If you’ll remember, I pegged Anaheim, Colorado, Montreal, Phoenix, Toronto, and Washington for negative regression, and Florida and New Jersey for positive regression. So, even with really rudimentary predictors, this season I was able to be fairly successful building predictions from a half-season sample for the remaining season. In previous years, the fancy stats folks usually picked the much more obvious targets (Toronto being the big one this year), but it’s very possible to go further if you wanted.

NHL Defensemen and Shooting Contributions back to 1967-68

File:Defenseman Ray Bourque 1979.jpg

Photo by Dave Stanley via Wikimedia Commons

I have kicked around this data in the past, most prominently in my theoretical post on offensive systems, but I really wanted to get further into the intricacies of defensemen and their historical place in team shooting (among other offensive contributions). By looking at how much a defenseman contributes to a team’s shot generation (expressed as a percentage of team shots in the games a player played, or %TSh), we can draw some interesting comparisons across NHL eras, but I haven’t yet explored how the role of the defenseman has (or hasn’t) evolved from the Expansion Era to the present, nor have I taken a look at some of the more exceptional defense shooting teams. Let me correct that now.

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Friday Quick Graphs: Shooting and Playmaking Contributions, 1967-68 through 2012-13

I’ve just finished a pretty massive dataset, so I’m geeking out a bit over what I can do with it. Just the beginning, above…this is the distribution of %TSh (player shots divided by estimated team shots in games they played) and %TA (same equation, but with assists) season performances, 20+ GP, from 1967-68 through 2012-13. Per recent arguments about Ovechkin, I’ve added lines showing where his best season (2008-09) and most recent full season (2012-13) fall on the list; his current season would fall approximately in the same place as last season.

Those of you who’ve been following me on Twitter know that I’ve put together a pretty substantial dataset, and I’ve been working through the data with a metric I’ve used for a while. %TSh is a player’s shots divided by his team’s estimated shot total in games they played (Team Shots / Team GP, multiplied by player GP). The measure gives us an idea of the player’s shooting contribution to the team’s offense. It moves outside the pesky variance of shooting percentage and gets closer to a stable indicator of offensive role. I’ve done the same with %TA, which is the same equation for assists. The reason for estimated team totals is we don’t yet have good macro-data on specific games that players played before 1987-88, but the metric runs essentially in lock-step with the real thing and I want to provide a useful, historical point of comparison. Doing this allows us to look 20 years further back.

The distribution above includes over 23,000 player seasons over 20 GP; the orange distribution is %TA, and black is %TSh. I used the marks to connect back to the previous week’s bizarre flame war over Ovechkin’s value and approach to the game; the top one shows Ovechkin’s peak year, 2008-09 (20%), which also happens to be the highest %TSh of all-time. The bottom mark is Ovechkin’s 2012-13 (16.3%), which I’m using because his current season is just slightly higher – it would be good for 16th best in NHL history.

I also did a second graph, wanting to look at the relationship of %TSh to %TA, to see just how much they ran together:

Related to the previous post, I decided to see if the relationship between TSh% and %TA was too close to tell me anything. %TSh is on the x-axis, and %TA is on the y. As you can see, they do run together, which is okay, because rebounds can result in assists for the shooter, and players with a lot of shots will generally be engaged in the offense in all ways. That being said, it’s not so close that they aren’t distinctive. The plot above does look scattered enough for these two metrics to tell us something apart from one another.

In the graph above, the x-axis is %TSh, and the y-axis %TA. Intuitively, these run together a fair amount, as shots create rebounds that can be counted as assists, and a player that shoots a lot is likely to be more heavily involved in the entire offense. That said, they don’t run nearly so close together as to render either measure moot. I think %TA can be a valuable counter-weight for assessing defensemen. Anyway, this is the tip of an enormous iceberg of data, so don’t be surprised to see me refer to and use %TSh and %TA again.

Input versus Output: An Ongoing Battle that No One Knows About

XKCD comics is written by Randall Munroe, a physicist who probably doesn’t know what  hockey underlying numbers (ie: #fancystats or advance statistics) even are, let alone supports them… yet – for the most part – he gets it.

Mainstream sports commentary is full of poor analysis when it comes to using numbers appropriately. Most of this comes from a lack of understanding between the difference between inputs versus outputs and how much a player can control certain factors. (It should be noted that this is a broad generalization; not everyone falls into this category).

Benjamin Wendorf displayed a bit of these factoids in his recent article Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin, but Campbell still didn’t get it.

What happened:

For those that do not know, here is a quick summary of Campbell’s article:
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Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin

File:Defense.gov photo essay 080220-F-6684S-642.jpg

Photo by Adam M. Stump via Wikimedia Commons

You know, there was a time when I relished The Hockey News, and really any hockey writing I could get my hands on. I grew up in the sticks in Wisconsin, where you can’t find jack about hockey, and so to convince your parents to buy a THN magazine was a real treat. I’ve never forgotten that feeling, and I want those old reporting institutions to continue, but it isn’t going to happen with haphazard attempts at analysis like Ken Campbell’s piece on Ovechkin from today. In it, he tries to argue that Ovechkin is going to have the worst 50-goal season in NHL history because his plus-minus isn’t good. After the jump, let’s take a look at some of these gems.

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Crystal Blue Regression: Leafs, Avalanche, Ducks, Among the Most Likely to Regress in 2014

Image

Picture taken by Sarah Connors, posted to Flickr – via Wikimedia Commons

With the Winter Classic coming up, or should I say the Winter Classics since the NHL handles marketing success like the kid who found the cookie jar, we also ring in the rough middle of the season. It’s a time for reflection, maybe a chance to re-assess your decisions, lifestyles; and if you’re analyzing the NHL, it’s the perfect time to recognize trends that may or may not continue. Also known as “regression,” here I’m dealing with a concept everyone understands to a degree; you invoke it when you see a friend sink a half-court shot in basketball and say, “Yeah, bet you can’t do that again.” The trend, supported by a history of not making half-court shots, suggests that it is unlikely for your friend to sink the half-court shot, even if they recently made one. In the NHL, possession stats like Corsi are considered better predictors of future success than stats that can be influenced more greatly by luck, like goals (and, consequently, wins), shooting percentage, or save percentage. Much like your friend and their half-court shot, there are teams that are defying their odds (established by possession measures) to succeed, which can easily happen with less than a half-year of performance.

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