Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin

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Photo by Adam M. Stump via Wikimedia Commons

You know, there was a time when I relished The Hockey News, and really any hockey writing I could get my hands on. I grew up in the sticks in Wisconsin, where you can’t find jack about hockey, and so to convince your parents to buy a THN magazine was a real treat. I’ve never forgotten that feeling, and I want those old reporting institutions to continue, but it isn’t going to happen with haphazard attempts at analysis like Ken Campbell’s piece on Ovechkin from today. In it, he tries to argue that Ovechkin is going to have the worst 50-goal season in NHL history because his plus-minus isn’t good. After the jump, let’s take a look at some of these gems.

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Friday Quick Graph: Possessing the Puck in 1969, 1981, and 2013

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Photo by Jim Tyron, via Wikimedia Commons

Just finished tracking possession times in a November 15th, 1969 game between the Flyers and the Leafs. This game, when compared to the games from this post, fits virtually in-between them, which is interesting because, unlike with the other two games, the Flyers and Leafs were two teams on the lower end of the spectrum in the league (8th and 9th in 2pS% in a 12-team NHL). Maybe that also contributes to their average possession time of 6.08 sec (n=349) compared to the 1981 game’s 6.15 (n=364) and 2013 game’s 6.17 (n=360). Another observation among these games: the standard deviation for the 1969 and 1981 games is right around 4 seconds, where it’s right at 5 seconds for the 2013 game. I’ll save any deeper ruminations until I have a larger sample, but it’s food for thought.

Not too long ago, I decided I wanted to try out tracking time of possession in historical games, with the hope of eventually having enough data to look into things. I realized it’s going to be a little difficult to get large enough samples of singular teams, but I also realized that we could potentially compare the game as a whole in different eras. I’ve always been of the mind that the game has evolved somewhat, but at its core there are a number of best practices that have kept it pretty much the same game from around the time that the red line was introduced in 1943. I wanted to test that as far back as I could go, though, so with this possession tracking I actually tracked each individual possession rather than just a total time of possession. For this chart, I displayed all those individual possessions as a distribution, longest possessions to the shortest. These three games, the Philadelphia Flyers vs. Toronto Maple Leafs in 1969 (Toronto won 4-2), Edmonton Oilers vs. Philadelphia Flyers in 1981 (Edmonton won 7-5), and Los Angeles Kings vs. St. Louis Blues (St. Louis won 4-2), had some surprising results when compared. As you can see above, the distribution is actually quite close, with the 1981 game seeming to have shorter possessions but then moving above the others in the middle of the line. The 1969 game actually seems like a trendline of the 2013 and 1981 games. The average possession time? 1969: 6.08 seconds, 1981: 6.15 seconds, and 2013: 6.17 seconds. Obviously, I need (and want) more data, but it is a really intriguing start.

The “possession battle” results?

All Situations Possession

  • PHI (47.1%) vs. TOR (52.9%), 1969
  • EDM (53.4%) vs. PHI (46.6%), 1981
  • LAK (51.7%) vs. STL (48.3%), 2013

Possession, Score Close

  • PHI (41.3%) vs. TOR (58.7%)
  • EDM (48.7%) vs. PHI (51.3%)
  • LAK (51.2%) vs. STL (48.8%)

A Rule of 60-40: Thoughts on Individual Player Possession Metrics

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The image above is the distribution of individual offensive zone start percentage (or the percentage of times a player started their shift in the offensive zone) and the distribution of individual Fenwick percentages (shots-for and shots-missed for that player’s team divided by all shots-for and shots-missed, both teams, all tabulated when that player is on the ice). I specifically targeted player season performances wherein the player participated in at least 20 or more games, as that’s roughly around the number of games it takes before these measures start to settle down.

These distributions tell us a few important things for understanding possession, deployment, and how we might analyze the game. Most importantly, after the jump I have a modest proposal, a 60-40 Rule, that might help us in the chase for those elusive, all-encompassing player value metrics.

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A Nice Tool to Have: BehindtheNet.ca Player Name Converter, plus Age and Position, 07-08 through 13-14

Those of you who have worked with Behind the Net data would be the first to say it’s a great, important site. I feel the same way, but I also know that anybody that’s worked with it close enough knows that there is a bit of a pain-in-the-ass there, with the different name spellings. Also, there are some position discrepancies and, for those that like to look into that sort of thing, player ages aren’t on there. Well, because I just brought the data together for something else I’m working on, I decided to share what I had for those problems. This link is to a Google doc that has the Season, regular Player Name, their age and position that season, and their BTN name for that season.

The players include all players that played a season from 2007-08 up to last week Thursday, 2013-14. Let me know if the link below doesn’t work:

BehindtheNet.ca Player Name Converter, plus Age and Position, 2007-08 through 2013-14

Hope this helps, happy researching!

Crystal Blue Regression: Leafs, Avalanche, Ducks, Among the Most Likely to Regress in 2014

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Picture taken by Sarah Connors, posted to Flickr – via Wikimedia Commons

With the Winter Classic coming up, or should I say the Winter Classics since the NHL handles marketing success like the kid who found the cookie jar, we also ring in the rough middle of the season. It’s a time for reflection, maybe a chance to re-assess your decisions, lifestyles; and if you’re analyzing the NHL, it’s the perfect time to recognize trends that may or may not continue. Also known as “regression,” here I’m dealing with a concept everyone understands to a degree; you invoke it when you see a friend sink a half-court shot in basketball and say, “Yeah, bet you can’t do that again.” The trend, supported by a history of not making half-court shots, suggests that it is unlikely for your friend to sink the half-court shot, even if they recently made one. In the NHL, possession stats like Corsi are considered better predictors of future success than stats that can be influenced more greatly by luck, like goals (and, consequently, wins), shooting percentage, or save percentage. Much like your friend and their half-court shot, there are teams that are defying their odds (established by possession measures) to succeed, which can easily happen with less than a half-year of performance.

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