Some day we will reach the point where we can comprehensively analyze which power plays are the best, which players drive that success, and most elusively, what roles to place players in to maximize a unit’s output, but statistically, our special teams cupboard is pretty bare. This season, as many of you know, I took on the long and arduous task of hockey tracking in the interest of trying to get us even one step closer to our objective: how can we better evaluate and predict power play success? So let’s dive right in. Continue reading
A few weeks ago, I was playing in a weekly beer league hockey game with some McGill University staff members. At one point, I came down the left wing with the puck, looked off a defender and whipped a wrist shot high, far side.
However, instead of the puck going bar-down as I had (ambitiously) hoped, it caromed off the glass and went all the way around the rink for an odd-man rush against. When I got back to the bench, someone said something to the effect of: “Stop missing high and wide. You’re just helping the other team break out of their zone.”
It was a light-hearted chirp – we weren’t playing for the Stanley Cup, after all. But it got me thinking about coaches who yell up and down the hall when their teams don’t “put the puck on net.” Is it really something that some teams do better than others?
A few days ago, our friend Micah Blake McCurdy did some work in an effort to answer that question. He took a look at the proportion of goals/shots on net/missed shots/blocked shots for each NHL in the past two seasons. Here is what he found: Continue reading
I don’t know if we’ll ever see a power play quite like that of this decade’s Washington Capitals. We can’t attach a firm date to it because it could extend as far as the end of Alex Ovechkin’s career at this rate, but we know that its peak of power began with the hiring of Adam Oates as Caps head coach back in 2012. Oates had run a successful 1-3-1 power play for the New Jersey Devils with Ilya Kovalchuk as his trigger-man, but nothing even close to the heights he managed to achieve with the man advantage in his two seasons in DC. Barry Trotz, to his credit, has kept the same formation — what’s that old adage about things that ain’t broke? — with only minor tweaks, and last year the power play continued to succeed.
Now there’s a lot to discuss about the formation and its success — I like to think of the Caps’ PP as a work of art more than anything else — but for the sake of this post I’m going to focus in on Alex Ovechkin. Never has there been a more criticized future first-ballot Hall of Famer, nor arguably a more controversial elite goal scorer. It should already be a given that Ovechkin is the best power play goal scorer of all time — he sits fifth overall in PPG/g despite playing in a significantly lower scoring era than his contemporaries like Mike Bossy and Mario Lemieux — but I would argue by the time he retires, he will also likely be the greatest goal scorer of all time period. It’s the man advantage recently, in the latter stages of Ovechkin’s goal scoring peak, that has been the sniper’s bread and butter. Since Oates brought the 1-3-1 to town, Ovi has scored 48% of his goals on the power play, compared to 33% prior to that. He scored 25 power play goals last year, six ahead of the next highest total in Joe Pavelski’s 19. You have to go back another five to reach the player who is in third — Claude Giroux with 14 — indicating how great of a season the Sharks’ center/winger had, but that’s a story for another day.
In February of the 2009-10 season, John Buccigross of ESPN was spurred by a mailbag question to do a quick thought experiment: does he think Ovechkin could set the all-time goals mark? Gabe Desjardins voiced skepticism of Bucci’s optimistic projection but didn’t offer a counter-projection, presumably because, as he wrote:
Basically, careers are incredibly unpredictable – nobody plays 82 games a year from age 20 to age 40. And players who play at a very high level at a young age tend to not sustain that level of play until they’re 40…So, to answer the reader’s question: I believe that there is presently no significant likelihood that Alex Ovechkin finishes his career with 894 goals. He needs to display an uncommon level of durability for the next decade, and not just lead the league in goal-scoring, but do so by such a wide margin that he scores as much as Gretzky, Hull or Lemieux did in an era with vastly higher offensive levels.
That said, I thought it would be fun, with five full years gone, to see how Bucci did, and try to build a prediction model with the same data he had available. Continue reading
Systems are without question the most elusive, yet most important, part of our understanding of hockey and the application of analytics. What works and what doesn’t? To what degree can a coach or team apply a strategy?
This led me to think about where we might most convincingly see evidence of a system at work. In the past, we here at HG have had a lot of skepticism about a number of elements of a “system.” For example, Garik’s pieces on competition-matching lines (here and here) and the use of the “defensive shell” to protect a lead, neither of which presented themselves as particularly effective ways of looking at or implementing systems. I have shown in the past that attempts to use extreme deployment in terms of zone starts doesn’t move the needle beyond a 60-40 range of possession, the range of shooting shares for forwards and defensemen haven’t seemed to change much over the last 20-25 years, and a plotting of even-strength shots-for with top and bottom possession teams do not suggest a major difference in shot location.
So where to go from there? Eventually, I decided that we need to get to an extreme enough situation, with robust enough data, where a team might have the best opportunity to dictate a system — in other words, we need to look at the powerplay. The most ideal opportunity for comparison, given the workable data for me, comes from the coaching careers since 2008-09 of Bruce Boudreau, Lindy Ruff, and Alain Vigneault. They all provide at least a couple of seasons with different teams, in addition to a robust set of coaching data from 2008 to the present. Let’s see what we can see…
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.
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:
Photo by “Djcz” via Wikimedia Commons
I don’t think we engage the idea of the place in history that many of today’s best players hold, and I partly attribute that to the difficulty of finding points of comparison across generations. Simply using raw scoring data doesn’t do the best job because a.) everyone knows Gretzky wins, and b.) we know that scoring fluctuated drastically in the 1980s, and it wasn’t because all the best shooters and passers were playing then. With that in mind, I’ve stewed over ways to bring these different generations together, in such a way that we can be comfortable comparing them. It’s led me to build a couple of metrics that move a little bit away from the counting statistics (G, A, PTS) and towards some metrics that demonstrate a player’s share of their team’s results.
The two metrics I’m focusing on for these young guns both relate to offensive measures, but I think that generally they also allude to a player’s importance to play overall. I tend to agree with Vic Ferrari’s assertion (see his third comment here) that forwards and only a select number of defensemen play much of a role in driving offense, and recalling some of the player types implicated in Steve Burtch’s work over at Pension Plan Puppets on Shut-Down Index, I’d propose that players that drive possession (forwards and defense) more generally will return some signals in regards to shooting or playmaking. Whether that simply means, in the future, we’ll get more from simply looking at passes and shots (or robots will do the whole darn thing and save me the trouble), I can’t say. For now, though, I created %TSh, or percentage of team shots, which expresses the proportion of team shooting a player does (in games they played), and %TA, which does the same exercise with team assists. While the issue of whether this expresses positive possession players is ripe for debate, it’s indisputable that players strong in these metrics will be drivers of offense for their teams.
In that spirit, I wanted to delve into some nifty historical data; I’ve been able to go all the way back to 1967-68 with data on %TSh and %TA, and it returns some fascinating studies on NHL legends vis-à-vis today’s stars. For this piece, I’m focusing on the players that get everyone excited, so-called “young guns,” or players under 25 that have already demonstrated their ability at the top level. How do contemporary young guns measure up all-time?
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:
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.
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.
For those that do not know, here is a quick summary of Campbell’s article: