The vast majority of scientific research has indicated that people with growth mindsets tend to be higher-achieving than those with fixed mindsets. Growth-oriented people are the ones who are more interested in furthering their education, or picking up a new hobby, or getting out of their comfort zones to experience new things.
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.
Yesterday, the Ottawa Senators announced the hiring of Daniel Alfredsson as the team’s Senior Advisor to Hockey Operations. Alumni of the Hockey-Graphs blog Emmanuel Perry (who is a Senators fan) took advantage of the situation to come up with this (obvious hoax): https://twitter.com/MannyElk/status/644648872682242048
Now, the more I think about it, the more I believe that having someone like Daniel Alfredsson lead an NHL analytics group is actually a wonderful idea.
To quickly recap what I’ve covered in the first four parts of this series, I have updated the work that’s been done on Pythagorean Expectations in hockey, and am looking to find out whether teams that have the best lead-protecting players are able to outperform those expectations consistently.
The first step is to figure out how to assess a player’s ability to protect leads. To do this, for every season, I isolated every player’s Corsi Against/60, Scoring Chances Against/60, Expected Goals Against/60 (courtesy of War-On-Ice) and Goals Against/60 when up a goal at even strength. I then found a team’s lead protecting ability for the year in question by weighting those statistics for each player by the amount of ice time they winded up playing that year. For players that didn’t meet a certain threshold, I gave them what I felt was a decent approximation of replacement level ability. For example, here was the expected lead protecting performance of the 2014-2015 Anaheim Ducks in each of those categories.
Now let’s look a little closer at our Pythagorean Expectation — derived through PythagenPuck.
So now, four parts into this five part series, is probably a good time to discuss my original hypothesis and why I started this study.
As I mentioned in my previous post, baseball has already gone through its Microscope Phase of analytics, where every broadly accepted early claim was put to the test to see whether it held up to strict scrutiny, and whether there were ways of adding nuance and complexity to each theory for more practical purpose. One of the first discoveries of this period was that outperforming one’s Pythagorean expectation for teams could be a sustainable talent — to an extent. Some would still argue that the impact is minimal, but it’s difficult to argue that it’s not there.
What is this sustainable talent? Bullpens. Teams that have the best relievers, particularly closers, are more likely to win close games than those that don’t. One guess that I’ve heard put the impact somewhere around 1 win per season above expectations for teams with elite closers. That’s still not a lot, but it’s significant. My question would be, does such a thing exist in hockey?
In Part 1, I looked at some of the theory behind Pythagorean Expectations and their origin in baseball. You can find the original formula copied below.
WPct = W/(W+L) = Runs^2/(Runs^2 + Runs Against^2)
The idea behind the formula is that it is a skill to be able to score runs and to be able to prevent them. What isn’t a skill, however — according to the theory — is when one scores or allows those runs. Teams over the course of weeks or months may appear to be able to score runs when they’re most necessary, to squeak out one-run wins, but as much as it looks like a pattern, it is most often simple variance. If you don’t fully buy into that idea, or you don’t really understand what I mean by variance, read this and then come back. Everything should be a lot clearer.
When applying Pythagorean Expectations to hockey, there are a couple of factors that complicate the matter.
The 2015-2016 NHL season is almost here, and our sport has come upon a new phase — arguably the third — in its analytics progression.
Earlier this week, I read an interesting story by Frank Seravalli on TSN.ca on Mike Babcock, Phil Kessel and the Toronto Maple Leafs, which is a good read for anything interested in how coaches think. For me, it also illustrate another way analytics could be employed to make a coach’s life a lot easier and take out some of the guesswork inherent in the job.
It was not too surprising to hear that Babcock was already thinking about how to get the most out of his team this season while on vacation, but I am very curious about his thought process behind how best to replace Phil Kessel. But before we start thinking about how to replace Phil Kessel (or his production in aggregate), we need to start thinking about how we are to measure a Phil Kessel’s offensive contribution.
As I alluded to in my previous post, the choice of words is very important when selling ideas to a coaching staff. Semantics lets us see the same idea from different angles, and can be a very powerful way to alter our understanding of a subject matter.
Recently, I’ve began to refer to hockey analytics tools (possession metrics, Player Usage Charts, HERO Charts, dCorsi, etc.) as technology, which has allowed me to relate better with those less well-versed on the matter and have all sorts of interesting discussions with people who otherwise wouldn’t give advanced stats the time of day.