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

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.

# Why Possession and Zone Entries Matter: Two Quick Charts

As some of you know, the NHL tracked offensive zone time for two seasons, 2000-01 and 2001-02, then inexplicably stopped. As some of you also know, I have a lot of historical game data, and that includes all the zone time from these seasons. Taking those performances, and focusing on the first two periods to avoid any major score effects (or “protecting the lead“), I charted every single game alongside 2pS%, the historical possession metric.

It’s pretty clear that the spread in shots-for in these games was quite a bit greater than the spread in zone times. Curious, I decided to do a distribution plot, the one that you see leading this piece (2pS% and offensive zone time % in the x-axis, percentage of total performances in the y-axis). Zone time, or generally speaking the flow of the game, has a tighter, much more normal distribution that the distribution of shots. What does this mean? This means that things like how you enter the zone (zone entries), and how you control the puck in the zone (possession, or passing) can make a pretty big difference in how you generate scoring opportunities.

Note: The data I used for these quick graphs were from home team’s perspective, hence why our distribution was a bit north of 50. Keeping that in mind, the 60-40 Rule we established here a year ago looks pretty good for assessing game flow, but there are ways within that flow that can tip the scale.

# Increasingly in the NHL, the Best Defense is a Good Offense

Photo by Lisa Gansky, via Wikimedia Commons; altered by author

While preparing statistics for a few upcoming posts on on-ice contributions, I decided to do a quick study on the share of on-ice shot attempts taken by defensemen versus forwards. The metric I’m using, which is a spin-off of an old one whose name doesn’t quite capture it right, is what I’m calling on-ice shooting proportion, or OSP. The results were quite interesting, and I decided that I should test the data a little further and see what we could find.

# Another Shot Quality Quandary: League Variance, Evolution, Error

Young Hockey Players” by Piotr Alberti, via Wikimedia Commons

Hockey statistical analysis isn’t really capturing all of hockey, or seeking to package it; it’s about getting as close we can to the essence of the thing. All the ideas, conclusions, best practices that we’ve cobbled together over the years give us an approximation of the actions a team, a player, or a fan could make going forward to better grasp the game.

Within this fact lies the greatest bone of contention for the hockey stats crowd, and the frequent refrain of critics who can only chirp from the sidelines. “Have you considered measuring this? Have you considered measuring that? Have you removed the games when the Rangers lacked sufficient compete level? Have you adjusted for Hamburglar’s pre- and post-lifetime gift certificate to McDonald’s?” While some of these adjustments may be worthy, and others utterly ridiculous, “shot quality” has been a persistent critique of the use of all shot attempts.

Admittedly, there are some interesting developments in Ryan Stimson’s work on puck movement, which might shed some light on an area yet explored. Though it’s not necessarily his focus, I think his data can give us an idea of how possession is maintained effectively. The remainder of shot quality, or at least the way it’s being conceptualized, lies in these remaining areas: type of shot, where shot is located on net, screened/tipped/direct/clear-look shot data, shooting talent, and where on the ice the shot is taken from. The former two, according to Gabe Desjardins, didn’t really demonstrate themselves when he came across the data (nor when I asked him a month ago). Shot location has already died a partial death by Desjardins, who found it seems to have minimal impact on save percentage, though he also found a team talent component, to the tune of differences ranging up to 0.7 feet.

Let me put the location stuff to bed the rest of the way.

# A Tank Battle in Pictures: Toronto Maple Leafs, Edmonton Oilers, Arizona Coyotes, & Buffalo Sabres in 2014-15

Having just added the 2014-15 season to our historical comparison charts, now was a good time to revisit (as I promised in my posts here and here on Pittsburgh’s 1983-84 tank battle) this season’s battle between Arizona, Toronto, Edmonton, and Buffalo. To do this, I tracked the progression of each teams shots-for percentage across two periods (or 2pS%), a possession proxy I developed for historical data that can help us compare teams back to 1952. As you can see above, the perception of the tank battle among these four teams wasn’t quite accurate to their results; Edmonton and Buffalo did not seem to have a marked drop-off in the final quarter-season.

Arizona and Toronto, on the other hand, did noticeably drop, and in Arizona’s case to a level below the hapless Sabres. Ultimately, the fight was more to maintain their improved odds, because Buffalo managed to hold at rock bottom. As I asked when I wrote about the topic with Pittsburgh in mind, it still gives rise to an interesting question: is it more wrong to tank than to maintain a low level all year? In some cases, a team that’s already laid low doesn’t need to tank deliberately…but on the flip side, I suppose that team also assumes risk in losing support and fans by not appearing competitive all season.

How did the Coyotes and Maple Leafs compare to what I’ve christened the “gold standard” for tanks, the 1983-84 Pittsburgh Penguins’ tank for Mario Lemieux? Well, the nice thing is that the plus- and minus-one standard deviations in 2pS% were virtually identical in 1983-84 and 2014-15, so I didn’t have to tinker with them:

While Pittsburgh had probably the starkest, earliest drop-off, both Arizona and Toronto were able to reach the same kinds of lows by the end of the season. To their credit, while the Coyotes and Leafs were, at best, in the lower half of the league in possession, they certainly did their best in the race to the bottom. You could question the wisdom of this kind of thing, since Pittsburgh was guaranteed the top pick if they reached the cellar, while this year’s tanks were struggling for a higher probability.

# HG’s Interactive NHL Graphs: A Tutorial

I’ve had a couple of people ask about how to use the new interactive visualizations we offer at Hockey Graphs, so I thought I’d take the time to provide a tutorial with some visual demonstrations.

# Why The Los Angeles Kings Missed the Playoffs: An Open Email

I’ve been asked by a couple of people how a team with a normal PDO and strong metrics could have missed the playoffs entirely. It’s an important question to address, particularly because the playoffs are so much more important than worrying about whether you’re lucky enough to win the Stanley Cup. I composed an email response, and felt good enough about it to open it up. While this doesn’t comprise the whole of the explanation (certainly, there’s some “blame” that goes to Calgary & Winnipeg), they’re points that I’m not seeing made elsewhere.

Hi XXXXX,

A couple of things really hurt the Kings. One is a cruel fact of a low-scoring league: if more games are going to be decided by one or two goals, it increases the likelihood that a fluky goal can impact a team in the standings. The Kings had the most overtime losses in the Western Conference; last year they were tied for the second least in the West. The second thing is the tank battle…the West had two teams with historically bad records – add in games against Buffalo, and we have three teams that will end the season with point totals that were typically reserved for the sole worst team in the league in other seasons. On the flip side, that creates a rising tide for all the other ships in the league, and raises the bar for getting into the playoffs. I mean, needing to get nearly 100 points to get in? Last year, the bottom team in the West, Dallas, had 91 points. A nearly identical record to this year got Los Angeles into the playoffs in the 8th seed in 2011-12.

Maybe the closest comparable circumstance was 2010-11, when the West again had two sad-sack teams (Colorado, Edmonton), and the East was noticeably weaker than the West. It took Chicago 97 points to get in. Also, look at 2006-07…Colorado didn’t make it with 95 points, having gone 44-31-7 during the season. If the West is considerably stronger than the East, as it was back then, you could also end up with a tougher path to making the playoffs. In ’06-07, every team in the Western Conference, save the 8th seed (Calgary, with 96 points), had 104 points or more!

Anyway, this year’s league created a scenario where a good team, by any measure, might not get in. The Kings went 39-27-15, outscored their opponents by 12 goals (in fact, they tied for 2nd in the league in goal differential at even strength), and could get 95 points and not make the playoffs. In the loser point era, there were only two seasons that was even possible, and both occurred in the stronger Western Conference. It’s a successful season by anything except the fluid marker of the playoffs, which unfortunately for them is all-important to reach.

Hope this helps,

Best,

Ben

Note: One critique I’d like to address – yes, all teams in the league are theoretically dealing with the tank battle, but tanking doesn’t occur across the entire season, which means that teams that have already played most or all of their games against tanking teams earlier in the year won’t have the benefit. Additionally, those same teams might have the resulting, added pressure of a more-difficult set of opponents through the latter portion of the season. If the difference between making the playoffs versus not is a matter of a few points, the difference in scheduling can become all the difference in the world.

# The Greatest Tank Battle: Penguins vs. Devils, 1983-84

Mario Lemieux with Laval of the QMJHL in 1984; photo by http://www.lhjmq.qc.ca/ via Wikimedia Commons

What do you do when a 6’4″ QMJHL forward who scored 184 points in 66 games in his last underage season scores at a 282-point pace in his draft year? You tank — you tank as hard as you can. In the latter half of the 1983-84 season, the Pittsburgh Penguins and New Jersey Devils were in an unspoken, pitched battle for the bottom of the league and everybody knew it. While the Penguins would ultimately win out, sputtering to a 16-58-6 record (“good” for 38 points in the standings) to New Jersey’s 17-56-7 (41 points), the two teams were coming from distinctly different franchise backgrounds.

Using information from our new interactive charts, we can see what set these teams apart, and led them to take different paths in what turned out to be a pretty wild race to the cellar of the NHL.

# The Art of Tanking: The Pittsburgh Penguins in 1983-84

While tanking is a hot topic in this year’s NHL, the act of tanking is as old as the idea of granting the worst teams a shot at the #1 pick in the draft. Case in-point: the 1983-84 Pittsburgh Penguins, routinely considered the most overt of tankers in NHL history. The graph above is just one example of their tank, and man is that bad. The yellow and grey lines indicate one standard deviation above and below league-average historical possession (using 2-Period Shot Percentage, or 2pS%, explained here). The blue line is a 20-game moving average (the orange is cumulative), and you’re seeing that right; a team close to the middle of the pack dropped nearly two standard deviations, or from near the top to near the bottom of the league. That graph, and all the ones below, are just some examples of the kind of tinkering you can do with our new interactive graphs, which I highly recommend you check out.

# Friday Quick Graphs: Are the 2014-15 Buffalo Sabres the Worst Team of All-Time?

This is part-opportunity to finally explore this question, and part-opportunity to tout some existing and upcoming data visualizations for HG. Travis Yost has been following the absolutely terrible Sabres season all year, and has raised some questions about whether it’s an all-time worst team. He’s only been able to reach back to the admittedly bad early 2000s Atlanta Thrashers, but the historically bad team by which all others need to be measured is the 1974-75 Washington Capitals squad. Using an historical metric like 2pS%, or a team’s share of all on-ice shots-for in the first 2 periods (expressed as a percentage), we can bring the 2014-15 Sabres together with the 74-75 Caps to see where both teams stand. Note: I used the cumulative version of the measure below, and added lines for one standard deviation below league-average in both seasons.

For as bad as Buffalo has been, they haven’t quite matched the futility of the 74-75 Capitals…nor should they. The Capitals were an expansion team that year, and unlike in other years the NHL did not really reach out to ensure the expansion teams in 1974-75 were given a good base to build from. These were also the peak years of the World Hockey Association, which made professional level talent even more diffuse than normal. The other expansion team in 74-75, the Kansas City Scouts, lasted two years before moving to Colorado to become the Rockies (the team subsequently moved to New Jersey in 1982-83 and changed their name to the Devils).

I included the standard deviations for the leagues in 1974-75 and 2013-14 (I haven’t compiled the data for 2014-15 yet, but this should be close enough), and even by those markers the Capitals compared markedly worse to their league than did the Sabres. But once again, the Capitals had a reasonable excuse, while the Sabres have walked into this situation with eyes wide open.

For those interested, I also put together 2-period shots-for and shot-against rates (and stretched them out to per 60 minutes) to get a rough sense of offense-versus-defense for both teams.

I added a couple extra filters to the charts, league-averages and standard deviations as well as 20-game moving averages in all the measures I used, which you can select by clicking on the grey “Team” bars and clicking on “Filter.”