Month: February 2014

Friday Quick Graph: NHL 5v5 TOI Peak at 24, 25 Years Old

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: , , , , , , , Leave a comment

This is the distribution of the skater performances w/200+ 5v5 TOI from the seasons 2007-08 through 2011-12 (n = 3,334). Use as reference for the below two charts. Notice that our line gets a little wacky as our n drops near the tails.

Some of you already know this, but I enjoy distributions, and I think they get sorely under-used in analysis (although, in the end, they are the basis of predictive work). This piece is a bit old (the data is across all skaters, 2007-08 through 2011-12, n = 3,334), but it shows the number of skaters with 200+ minutes of 5v5 time at each age grouping. The peak is clearly at 24 or 25 among this group, but we should be clear with what “peak” means. Although even-strength time can be a pretty good indicator of overall player talent, it’s still a shaky signal (c’mon, we know not all coaches put the “right” guys out there sometimes). Further, powerplay time can sometimes be a drag on better players’ energy for even-strength time, which can also compromise this signal. Nevertheless, if you were to sort all players into even-strength time groupings (say, forwards in 4 groups by ESTOI, and defensemen in 3 groups by ESTOI) you’d see that the top would generally perform better possession and offense-wise than the second, and so on down.

With that in mind, “peak” is also about health. Though we’ve not had much research into it (hint, hint), we have reason to suspect that injuries might drag on possession measures a bit. That said, 24-25 can also be a performance peak for the reason that players are less likely to have major injuries until that age or later.

I plan on digging into this data again (now that I have my ES data back to 1997-98) and splitting into forward and defense groups, but this is a good start.

The Day David Staples Killed Corsi Because…Taylor Hall

Benjamin Wendorf NHL Topical Analysis, Theory Tags: , , , , , , 1 Comment

File:Taylor Hall.JPG

Photo by Alexiaxx, via Wikimedia Commons

I’ve been following the story of Taylor Hall as the season progresses, particularly through Tyler Dellow’s attempts to un-vex the vexing year Hall is having (Parts I – II – III – IV). In Tyler’s second part, he notes three differences between this year and last year: fewer zone entries with a carry, poorer retrieval of dump-ins, and a lower shots-per-carry total. The latter, Tyler notes, is likely symptomatic of a larger emphasis on dumping-in, wherein a player carries to just inside the blue line before dumping. He quotes Dallas Eakins as suggesting that Hall, in-particular, seems to take this dumping-in approach to heart. I’d add that there’s a possibility that this is abbreviating potential offensive zone possession time, as overall Hall and the other Edmonton Oilers have dropped from nearly 50 seconds per shift to 47 seconds. Further to that point, Tyler noticed in the fourth part that the Oilers have seemed to adopt a tip-in dump-in, wherein the player in the neutral zone either redirects or chips, while standing in place, the puck into the offensive zone. Just based on the video evidence Tyler provided, this looks like an extraordinarily passive approach to the dump, equivalent to dumping and getting off the ice. In that latter scenario, you are unequivocally giving up possession. In the tip-in approach, you take your active close player and leave them in-place, in favor of a later-to-the-game forechecker. It would seem to me that you’d benefit from an active dump-and-chase forechecker.

There are a couple of others irons you can put in the fire, including variance of CF% (a 5% swing is not unheard-of, particularly moving from a 48 to a 56-game sample), potential fatigue from increased playing time (he’s taken on some penalty kill minutes and more even-strength minutes this year), and the swapping out of Ales Hemsky as a linemate (for Sam Gagner). The tougher competition, for me, is essentially washed out by a bump up in offensive zone starts. I don’t see evidence of recording bias, either. I suspect a couple potential, additional things: 1) the drop-off is right there with the Ovechkin-Dale Hunter drop-off, so there might be some player vs. system aggravation, and 2) some fatigue issues related to the early-season knee injury. Injuries aren’t just about pain, they can also compromise strength and endurance. A guy like him, who has had injury issues in the past, does not want the “soft” label (you’ve seen what that’s done to Hemsky’s time in Edmonton), and might not want to admit it to the media or himself.

Up to this point, you’ve seen Dellow’s and my own introspection into what appears to be a poor possession season from Taylor Hall. Enter David Staples.

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Friday Quick Graph: How the Possession Battle Stabilizes

Benjamin Wendorf Friday Quick Graphs, NHL Topical Analysis, Theory Tags: , , , , , , Leave a comment

Surely you’ve been exhausted with graphs from this December 30th, 1981 Oilers-Flyers game, but allow me one more. I wanted to demonstrate both how many possessions it took for the possession battle to grant us a clear picture, and also further speak to the value of 2pS%. The chart above demonstrate what happens when I establish a rolling possession-for % (as indicated by the y-axis, possession-for % is done from the perspective of Edmonton) using the last 10 possessions, then the last 20 possessions, and so on to 60 possessions. I stop there because we then arrive at a point where we are primarily measuring (in 60-120 on the x-axis) the 1st and 2nd period in-tandem. What we see is that, by that point, our possession battle has calmed down much closer to something that resembles the final battle (a 52% to 48% victory for Philadelphia). The y-axis shows how far above or below .500 (or 50% possession) the battle went; once again, this was measured from Edmonton’s perspective, so below the line is Philadelphia winning the battle, above is Edmonton (hence the color-coding). We also see, then, that the battle doesn’t calm down to a spread below the 60-40 possession benchmark until 40 possessions…which means it doesn’t really reach the likelihood of truly reflecting demonstrated possession talent until that point. For this reason, I think we can derive confidence in the signal that two-periods provide us with regards to possession battles. Additionally, it speaks to the potential problem with focusing on single periods of data.

NHL Defensemen and Shooting Contributions back to 1967-68

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , , , , , Leave a comment

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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Wayne Gretzky vs. Bobby Clarke, December 1981: A Micro-Analysis

Benjamin Wendorf NHL Topical Analysis, Theory Tags: , , , , , , , , , 8 Comments

Image

Left image by “Centpacrr“, Right image by “Hakandahlstrom” via Wikimedia Commons, both altered by author

On December 30th, 1981, Wayne Gretzky’s Edmonton Oilers and Bobby Clarke’s Philadelphia Flyers met in a Wednesday night tilt rich with symbolism. Clarke, 32, was a couple of years away from retirement; two of his three remaining teammates from the Cup years, Reggie Leach and Bill Barber (defenseman Jimmy Watson was the third), were themselves out of the league in two years (Leach due to talent drop-off, Barber due to injury). Ironically, there was little indication in 1981 that this was going to happen – all were around 30, all were near point-per-game scorers playing all minutes. Whatever the case, they were the last of the Broad Street Bullies, and were now mentoring a new generation of “Bullies” like Ken Linseman, Tim Kerr, and Brian Propp, who seemed at times more annoying than dangerous. Though in transition, Philadelphia was still a great possession team (4th in the league in 2pS%, an historical possession metric), but fought the percentages all year to squeak into the playoffs. Edmonton, on the other hand, was romping through the league at record pace, and by December 30th held a comfortable lead over 2nd place Minnesota in the old Campbell Conference. Gretzky, of course, was at the heart of this surge, and by game 39 he had 45 goals.

The 1980s Oilers were the next step in NHL offense, really a Canadian version of the 1970s Soviet style of hockey. They didn’t need to bully their way to victories – they let the other team take the penalties, and skated all over them. I should say, that’s what Edmonton would eventually do; on this night they lined Gretzky up with Dave Lumley and Dave Semenko, as they had done most of the year. More on that later.

As I said before, though, the Flyers were a great possession team, as they always had been when Clarke and Barber were in their prime (they averaged, averaged, 55% 2pS% in the years 1973-74 through 1981-82, placing them consistently among the top 5 in the NHL). They were fast and calculating with their puck movement; the grit was just extra work – and who knows, maybe it contributed to Clarke, Barber, and Leach’s early retirement. The Bully when met with the Oilers, though, learned that the box was the bigger enemy.

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Friday Quick Graphs: When did “Score Effects” Emerge in NHL History?

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: , , , , , , 6 Comments

Back in 2009, Tyler Dellow first elaborated on the idea of what we now call “score effects,” or how teams with a lead will go into a “defensive shell” and purposely withdraw from the possession battle to preserve their score. Score effects are the primary reason the go-to possession stat is “Fenwick Close” today – the “close” implies the importance of looking at possession measures when teams still have a reason to engage. The limits of historical shot recording, and the possibility of score effects, are precisely why I’ve advocated the use of 2pS% (shot-differential percentage from the first two periods) as an historical possession measure.

The one thing I never completely took for granted was that score effects had always existed in the NHL. To test this, I broke down each game into individual period shot battles, and looked separately at the correlation* of 1st, 2nd, or 3rd period shots-for percentages to final goals-for percentages. The result above clearly shows that the 3rd period SF% begins to drop away drastically after 1977 or so, after a quarter-century of running pretty close to the others. It does seem possible, then, that the re-introduction of overtime in 1983-84 (gone since 1943-44) had an impact on the growth of score effects (although I’m not sure how); on the other hand, the introduction of the “loser point” in 1999-2000 doesn’t seem to have had any effect. We can also do a similar graph of correlations to goals-for percentage to validate the use of 2pS%:

As you can see, score effects have essentially become the norm, much to the detriment of overall shot differential. At any rate, whomever put two-and-two together back in the 1970s probably had the right idea; I’d forward the hypothesis that the 1970s NHL was ripe for change and innovation (a lot of competition; growth of league = increase in decision-makers and opportunities to exploit market inefficiencies). In that kind of environment, protecting the lead quickly became a best practice, and it steadily grew to a league-wide practice by the mid-1990s or so.

* Or a -1.0 to +1.0 relationship of the variance in one variable to the variance in another; positive means as one goes up, the other tends to go up, suggesting a positive relationship or correlation. A negative correlation suggests that, as one goes up, the other tends to go down. The closer to 0.0, the less likely the variables have any relationship at all.

Consistency in the NHL: How often do teams tend to play “their game”

Garret Hohl NHL League-Wide Analysis 4 Comments

Source: Bruce Bennett/Getty Images North America

INTRODUCTION:

Our very first published article used shot attempt differentials to see if certain teams were more consistent than others in their performance. We observed that teams differed greatly in how they performed on average, but not so much in their levels of consistency, as in the spread of their performances.

One of the commentators of the article, under name of “Anthony Delage” wondered if team’s differed much in playing “their game”, or in other words: how often low-event team’s play low-event games vs high event teams play high-event games.

See more after the jump.

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Why NHL Stats and Scouting Must Work Together

Benjamin Wendorf Theory Tags: , , , , , , , Leave a comment

File:2006nhldraft-stage.jpg

Photo by Arnold C, via Wikimedia Commons

I think it’s fair to say that people familiar with hockey scouting and stats analysis know that there is a bit of a rift between the two (not unlike what exists in baseball). The former, as in baseball, has a long history as the standard in hockey analysis, being at-or-near the forefront of drafting, trading, and free agency decisions for teams. The latter is expanding its reach exponentially into league offices, and has many a pro-stats person questioning the abilities of scouts to analyze players (and vice versa). There are at least preliminary attempts to reach out, on the part of Corey Pronman at Hockey Prospectus (and ESPN), but scouting and stats analysis both have a lexicon, methods, and best practices, and devotees of one probably don’t have much time to develop proficiency in the other.

Yet, therein lies a problem and a solution. There is a common thread between these two groups, the desire to usefully analyze hockey players. They each have their own approach, but neither necessarily contain such complicated concepts that they cannot be read by a conscientious analyst. But most importantly, they have something to offer one another that could improve both areas of analysis.
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An Introduction to Analyzing Neutral Zone Data (with Graphs)

garik16 Neutral Zone Analysis 3 Comments

Goons like Eric Boulton tend to break Graphs featuring Neutral Zone Data in very……..not-good ways.

A little over 2 years ago, Eric T. introduced us to the idea of tracking play in the neutral zone, in the form of tracking “Zone Entries.”   Zone Entry Tracking is now being done by trackers for a few different teams (Isles, Canes, Ducks, Kings, Sharks, Flyers, Caps, and more here and there) although not most teams’ data has not been collected yet such that people can compare players across teams.

That’s something I’d like to change, so I will be using this space to acquire data from various teams and compare teams and players.  That said, I’d like to explain how we analyze neutral zone data in the first place.

Most Neutral Zone trackers track using a spreadsheet created by Eric, which collects the time, player, and type of each entry.  That sheet compiles the 5 on 5 and 5 on 5 close individual #s of each player (How many entries, What % of entries were via carry, how many shots per type of entry, etc.) and the team.

Using a tool created by Red Line Station (@Muneebalummcu) and with some help from Eric T, we can then use this data to get on-ice data for every player.  This is to me, the real gold mine of neutral zone data – we can see not just how often a player carries in, but how often the opponents do so against him, and whether opponents are carrying it in or instead dumping.   We can also use this data to determine which players aren’t getting it done once the puck is in the offensive or defensive zones, although how repeatable that data is is still in question (More on this in a bit)

There are a few neutral zone stats I think are most worth highlighting: Continue reading →

The Top “Young Guns” in NHL History

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , 1 Comment

File:Orr Trip.jpg

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?

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