NHL Topical Analysis

NHL Topical Analysis pieces are team- or player-specific analyses intended to tell us about league trends.

Friday Quick Graph: Season Stories Using % of Team Shots, Gretzky, Lemieux, Sheppard, and Simpson in 1987-88

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

This takes the progressive, cumulative percentage of team shots from the graphs below and compares them to one another (to view the original charts: Simpson, Sheppard, Lemieux, Gretzky). It really establishes how greatly Lemieux mattered to the Penguins…Gretzky had plenty of teammates taking over the shots, especially as he was dinged up during the season and players like Messier and Kurri were helping carry the load (not to mention Simpson and his 43 goals in 59 games). Any surprise Lemieux was one season away from 85 goals and nearly 200 points? Any surprise Simpson was already coming down from what would prove to be a career year? Any surprise that Sheppard was moving towards a quality career? These %TSh charts can really lend to interesting seasonal and career narratives.

Part of the reason I like doing graph work is because a good graph (with a little bit of contextual knowledge) can tell a really interesting story. In the past, I’ve been a proponent of digging deeper into the historical data, and noted that even though we have less data of the pre-BTN era it doesn’t mean we can’t make some intriguing graphs. %TSh, or % of team shots (in the games a player participated), provides a great opportunity to do just that, not just in a player’s career (as I’ve done before) but also over the course of a season. In the graph above, I took two well-known players, Mario Lemieux and Wayne Gretzky, and matched them to two (to the younger readers) lesser-known players from 1987-88, Ray Sheppard and Craig Simpson; I expressed their %TSh cumulatively, game-by-game. Craig Simpson, at the tender age of 20, was having the best year of his career (56 goals on an incredible 31.6% shooting percentage), but a trade to the Oilers mid-season would alter his offensive role for that season and into the future. Ray Sheppard, like Simpson very young (21), over the course of the season earned Ted Sator’s trust and responded with a 38-goal rookie season. Sheppard would go on to be a very good offensive player for about a decade.

Yet their lines relative to Gretzky and Lemieux also remind us that, for as good as they were, neither were driving the boat to the level of those legends (and probably wouldn’t). So you do get some perspective on what some of the best-of-the-best were doing. Lemieux, who was entering his prime, was literally carrying a middling Penguins team on his shoulders, and his ability to do that would bring him, in 1988-89, to convince people that Dan Quinn and Rob Brown were really good.

For frame of reference, in the BTN Era (2007-08 to present) only Ovechkin has been able to come close to the kind of shot volume Lemieux was demonstrating in 1987-88.

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

Benjamin Wendorf Friday Quick Graphs, NHL Topical Analysis, Theory Tags: , , , , , , , , , 1 Comment
Hextall OnIce.jpg

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%)

Should the Winnipeg Jets Hold On to Paul Maurice?

Benjamin Wendorf NHL Topical Analysis Tags: , , , , , , , , , , , 2 Comments

Photo by “Krazytea” via Wikimedia Commons

Mark Chipman, Kevin Cheveldayoff, & Co. took a huge step yesterday, firing their first choice in the new Winnipeg Jets coaching history, Claude Noel. Noel has the unfortunate (no, scratch that, earned) legacy of mediocre results, questionable lineup decisions, and the uncanny ability to look like nothing’s going on while standing in a tire fire. Whatever the case, the Jets decided to turn away from the new-coach idea towards a very-seasoned veteran in Paul Maurice. With 1,137 NHL games of coaching experience, and one trip to the Cup Finals (with Carolna in 2002), Maurice is definitely a smart choice if a team’s trying to find itself and build up from the relocation identity.

It’s also significant that Maurice has already endured the relocation process. First breaking into the league at the helm of the Hartford Whalers, he helped that team build up from a series of dismal years and a move from Hartford to North Carolina. Though he’d be fired before he could enjoy the ultimate prize of those efforts (the ‘Canes would win the Cup the year after he left), there is little doubt he has the experience for those that prize that sort of thing.

But that leaves a few hanging questions: is he a good coach? Can he make this a better team? Is there any way we can find answers to those questions?

We can, and we will.

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A Tale of Two Riverboat Gamblers: Analytically Comparing Jack Johnson and Dustin Byfuglien

Garret Hohl NHL Topical Analysis Tags: , , , , , , , 1 Comment

Source: Harry How/Getty Images North America

There are probably enough fan bias tendencies in sports to fuel psychology graduate theses for years to come. Sometimes these biases even creep into the minds of hockey’s brain-trusts, including GMs, coaches, and national team selection committees.

One such bias is the propensity against players who are strong offensively but can be a risk defensively. Whether these offensive players are a net-positive to the team depends on whether their offensive output outweighs their defensive lapses. Period. You win the game by out-scoring, not by just increasing your own scoring or limiting your opponents. However, if you were to survey most fanbases, you would probably find very few defensive risk-type defenders that are considered a net-positive.

When it comes to the traditional plus/minus statistic, there are great intentions of evaluating a player’s net contribution, but the statistic ultimately fails at achieving this. There are a few issues with plus/minus, one of them being sample size; another fault to the statistic is its low repeatability, which is its ultimate failure. This unreliability in plus/minus relative to most other statistics can be seen here:

Using analytics, we can demonstrate how numbers help differentiate two gambling defensemen who have been the butt-end of scrutiny from their fanbase.

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Journalism in the Prairie Provinces: Gary Lawless Goes for Dustin Byfuglien’s Jugular

Benjamin Wendorf NHL Topical Analysis Tags: , , , , 7 Comments

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Photo by John Slipec, via Wikimedia Commons

In case you missed it at 1 am this morning, Gary Lawless of the Winnipeg Free Press decided to add to a chapter in his future collection, Gary Lawless Gets Tough – Online Version (CD of Lawless Gets Tough – Radio Version coming soon!), by declaring Claude Noel needs to reduce Dustin Byfuglien’s minutes. The chapter, titled “Black Players,” is the longest of the book, filled with relentless reminders of how the players in-question aren’t anything like Gary Lawless.

The spark for the uproar, uproar being a requisite thing in the sports talk world where blowhards and mittenstringers are made to look hard-hitting and important, was an admittedly bad weekend for Byfuglien, who made a few costly errors that contributed to Jets losses. I get that “admission” from Byfuglien himself, as he’s quoted in the Lawless column: “Not playing my top. Something I have to figure out myself. Slow down and play the game I should be. Keep it simple. I might be playing a little too fast for myself right now. Tighten it up.”

That explanation, for Lawless, is “a refusal to be responsible with the puck.” But that’s just the beginning.

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Gauging the Relevance of Quality of Competition on a Player’s Stats – Toronto Maple Leafs’ D Edition

garik16 NHL Topical Analysis, Theory 12 Comments

In my last post, I detailed how there is a trend of people placing too much importance on the context behind a player’s statistics, even when such contextual numbers do not at all explain the poor (or great) performance.  Part of this is because old research indicated that quality of competition and zone starts have a much greater impact than more recent research has found to be the case.

In particular, the impact of quality of competition is often dramatically overstated.  This is a little understandable – after all, it should matter a lot who is on the ice against a player at a given time.  And in fact it does:

Graph by Eric Tulsky at http://nhlnumbers.com/2012/7/23/the-importance-of-quality-of-competition – a great post that everyone should reread.

But here’s the key thing:  While it matters if a player is facing Sidney Crosby instead of John Scott at any given moment, the range of competition that a player faces over the course of a season is EXTREMELY SMALL.  The gap between the players facing the hardest competition and those facing the weakest competition is the same as facing an average player at most like 4 shot attempts per 60.  In other words, the guy with the toughest competition in the league will face an average opponent who is +2 corsi/60, while the guy facing the weakest will face an average opponent who is -2 corsi/60.  And nearly all players won’t be in these extremes – most will be within -1 corsi/60 and +1 corsi/60.  And as you might expect the gap between opponents who are +1 shot attempts per 60 and those -1 is practically nothing.

Yet you’ll hear people talk about how one player plays “really weak” competition or another player’s bad #s are because he takes “the toughs” – this doesn’t really mean anything.

This can perhaps be illustrated best by looking at Dion Phaneuf and the Maple Leafs’ D Corp:
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Replacing Steven Stamkos: How the Tampa Bay Lightning Weathered the Storm

Benjamin Wendorf NHL Topical Analysis, TOI Chart Tags: , , , , , , , , , , Leave a comment

File:Steven Stamkos.JPG

Photo by “Resolute”, via Wikimedia Commons

One of the more remarkable and underreported stories of this season has been Tampa Bay’s continued competitiveness despite the loss of the NHL’s most dangerous sniper. You could hear the wind whoosh out of Lightning fans’ sails when Stamkos went down in November, and for good reason. Martin St. Louis’s Art Ross Trophy aside, Stamkos was the driving force behind the Tampa Bay attack.

Yet, at the time of this post, the Lightning are 3rd in the Eastern Conference, and 7-2-1 in their last 10 games. What changed when Stamkos went down? How has Tampa Bay managed to continue competing at such a high level? The short answer: they transformed from a star-driven team to a top-to-bottom threat. It was extraordinary, it was a model of what good management can accomplish, and it can be a lesson to teams in the future.

After the jump, I’ll break down how it happened.

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

Benjamin Wendorf NHL League-Wide Analysis, NHL Topical Analysis Tags: , , , , , , , , , 3 Comments

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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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