Career Chart

Career Charts look at the specific tracking and graphing of player performances over their career, in comparison to self over time or to others.

Friday Quick Graph: Player Career Charting by Percentage of Team Shots, 1967-68 to 2012-13

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

Embedding interactive graphs into blog posts, especially blogs with a narrow runner like ours, is frequently an awkward process. Just about the time things look good, you tinker with it and it looks bad. Nevertheless, I had a bunch of old data I put together, once upon a time, and I wanted to get it out there in a form that you could tinker with. Basically, in the past I have used the percentage of team shots in the games a player participated (%TSh; explanation here) as a way to capture a player’s contribution to the shot load; I also think it strongly implies a player’s involvement and contribution to team offense overall.

In the case of today’s graph, I took %TSh and looked at aging curves with a multitude of players from 1967-68 through 2012-13 (like I said, the data is a little old). I prepared this with a selected group of players available for the filter, the majority of whom are stronger, more familiar players of the years covered. I also included some players that struggled by the metric, for the sake of comparison. To filter, click on the “Name” bar, click on “Filter,” and let your imaginations run wild. Feel free to download if you wish.

Note: I believe I set the cut-off at 20 GP before I would record the point of data. It’s old. I’m old. We’re all getting older.

How Will Joe Thornton Perform Through his Contract?

Jared Career Chart Tags: , 1 Comment

photocred Wikimedia Commons

Joe Thornton has been in the news a lot recently, with talk that the Sharks are looking to move him as part of what they are labeling a rebuild.

Thornton is elite. In the past five seasons, he’s tied with Corey Perry as the sixth best point accumulator in the league. The Sharks signed Thornton, who turns 35 within two weeks, to a three year contract with a cap-hit of $6.75 million back in January.

I’m sure any potential acquiring team is intensely interested in how Thornton may depreciate over the life of his contract.

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How well do goalies age? A look at a goalie aging curve.

garik16 Career Chart, Theory Tags: , , , , 11 Comments

This guy may be lying flat on his face like this more and more often as he’s reaching the big 35.

 

A few weeks back, I unveiled Hockey Marcels: an extremely simplistic system for projecting goalies performance going forward, utilizing just the last four years of a goalies’ play to do so. Building off of work by the great Eric T., I weighted more recent years more heavily than older ones, to try and give a better estimation to what we should expect from goalies going forward.  In addition, I added a regression factor to Eric’s work, such that we could deal with varying sample sizes and the extreme variability of NHL goaltending.

But the one thing I didn’t include was an aging adjustment.  This is an integral part of any serious projection system for the obvious reason:  Using past years to project future data is sound, but players will be OLDER in the future and increased age generally results in worse performance (except for the really young).  This is especially the case with hockey, where peak performance has been found to be at ages 24-25.   If we really want to project goalie performance going forward, we need to find out how well goalies age.

A few people have looked at this before (both Eric and Steve Burtch have written about goalie aging in previous posts), but I wanted to actually get #s rather than just a graph on how aging affects goalies of all ages.  So I used hockey reference to get the seasonal data of all goalies from 1996-1997 to the present season who had played 20 years, and tried to take a look.

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Forecasting Future Goalie Performance with Four Year Hockey Marcels:

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Evaluating goalies is hard.  Goalie performance varies more than anything else in hockey and today’s terrible goalie can randomly turn into an elite goalie next season….and then turn back into a terrible goalie.  The best measure we have for evaluating goalies is Save Percentage and so we often tend to use a player’s career SV% as a way of forecasting what to expect from a goalie in the future.

However, it would make more sense not just to take a goalie’s career average SV% when forecasting future performance, but rather to take a weighted average in which we place greater importance on more recent data.  Eric Tulsky recently did this at his must-read blog, Outnumbered, and looked at what weight he should give each recent year’s data to forecast the next three years of a goalie’s performance:

So in my base case, I’m using years 1-4 to try to predict years 5-7. The best predictions came from weighting things like this:

  • Each shot faced in year 3 counts 60 percent as much as shots in year 4
  • Each shot faced in year 2 counts 50 percent as much as shots in year 4
  • Each shot faced in year 1 counts 30 percent as much as shots in year 4

This is particularly similar to the baseball forecasting system invented by Tom Tango, known as the Marcel Forecasting System.  Marcel, named after the monkey, is one of the most basic projection systems possible – it simply weights each of the last three years with weights of 5/4/3, adds a very basic regression to the mean, then adds a very basic aging projection.  Marcel is very basic on purpose – it’s still pretty damn accurate, and if a more complicated forecasting system can’t beat Marcel in baseball, it’s useless.  Surprisingly, most forecasting systems don’t improve upon Marcel by very much.
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NHL Career Charting: The Pre-BTN Era and What We Can Still Do With Historical Data

Benjamin Wendorf Career Chart, NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , 4 Comments

File:BrendanShanahan.jpg

Photo by “IrisKawling”, via Wikimedia Commons

Hockey statistics have always been fairly historically limited; most of the so-called “fancy stats” have only been tracked (and easily track-able league-wide) back through the 2007-08 season. The prior years have a veil of fog over them, though there is fairly decent shot data going all the way back to the 1952-53 season (thanks to the Hockey Summary Project; I’ve been able to bring the data together), good game-by-game individual player data going back to 1987-88 (thanks to Hockey Reference via Dan Diamond & Associates), and gradually-improving TOI data going back to 1997-98 (thanks to NHL.com and Hockey Reference). Unfortunately, this has lead to a relative dearth of research into the years of the “Pre-BTN” Era, so-called because 2007-08 was the first year we received in-depth, league-wide data from Gabe Desjardins’ Behind the Net stats site and Vic Ferrari’s timeonice.com.

Having a background in history, and also having grown up as a fan of the league in this grey statistical era, I have spent the last couple years trying to compile and present statistics from the Pre-BTN Era in ways that can help provide a window into those years (and possibly inform our understanding of the present-day game). I’m somewhat indebted to Iain Fyffe, a guy who’s been doing similar yeoman’s work much longer than myself at Hockey Prospectus, though more recently he’s been sharing his work at his own site, Hockey Historysis.

The fact of the matter is that there is actually an enormous amount of information out there, and more importantly with graph work we can really do some interesting things. First case in-point is what I call “career charting;” essentially, charting a player’s shots in a game relative to their team’s shots in those same games. Using the metric %TSh, or percentage of team shots, this provides an interesting glimpse into player contributions, workload, and development in the Pre-BTN Era. Adding some artistic (and informational flourish), I present to you Pierre Turgeon:

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