NHL Team History, Possession, and Winning the Stanley Cup

Photo by “JulieAndSteve”, via Wikimedia Commons

Gabe Desjardins dropped a comment over at my Tumblr awhile ago, asking me if I could put together a graph expanding on a metric I came up with, 2-Period Shot Percentage (or 2pS%). 2pS% is an historical possession metric that takes shots-for and shots-against in just the first two periods of a game and expresses it as a percentage for the team being analyzed. The idea was that I was trying to get a rough possession measure from the period that would avoid score effects, or the tendency for teams with a lead to sit on the lead and thus give up shots late in the game. Having recently completed a database of period-by-period shot data going back to 1952-53, I have been able to test this metric a bit and the results were good for 2pS% as a possession measure. Returning to Gabe’s request, he wanted to know if I could chart the 2pS% data from year-to-year, with one line following the league leader in the metric and the other line following the Stanley Cup winner. I’d been curious about this myself; certainly there are a number of different ways to express the value of the metric, but this particular one could be interesting because it toes the line between what the Old and New Guard feel is important in this kind of analysis.

Well, I was right that it would be interesting:
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Should the Winnipeg Jets Hold On to Paul Maurice?

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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Friday Quick Graph: The Evolution of an NHL Forward’s Time On-Ice

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

I did a similar GIF one week ago, using defensemen, in an effort to understand how a player’s playing time evolves over their career. Taking NHL player data from 2007-08 through 2011-12 and identifying year-t0-year change, I’m able to create a hypothetical forward that plays from age 18 to age 40, and how that player’s ice time would change.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

There are some similarities to the defensemen GIF, primarily that player’s are given powerplay minutes early, but grow into penalty kill minutes. Unlike defensemen, though, forward TOI decreases uniformly at all strengths, whereas defensemen tend to retain some of their penalty kill time.

As with the previous post, it’s worth pointing out that a player playing from age 18 to age 40 would be a pretty unique, talented player, so this model is really just to demonstrate change.

NHL Career Charting: The Pre-BTN Era and What We Can Still Do With Historical Data

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

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

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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

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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Friday Quick Graph: The Evolution of an NHL Defenseman’s Time On-Ice

Age progression TPCs for a hypothetical defenseman who has played from age 18 through 40. The progression is built on year-to-year age trends across the entire NHL defenseman population from 2007-08 through 2011-12.

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

What you see above is a “Total Player Chart,” or TPC, a chart I developed about a year ago to visualize a player’s time on-ice (TOI) deployment. Using that chart, I took the NHL player population from 2007-08 through 2011-12 and recorded the year-to-year change in player’s TOI relative to their age and age +1 seasons. I took those trends and placed them upon an average 18-year old defenseman’s ice time, and tracked how that hypothetical player’s TOI would evolve if they played to the age of 40. The result is the GIF above.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

As you can see, the trend is that young player’s tend to receive 5v4 minutes, and as they age they become more trusted with 4v5; as they get older, the 4v5 minutes stick around, but the 5v4 minutes fade.

It’s worth pointing out that this hypothetical defenseman, overall, is likely to be a decent player, by virtue of the fact that they would be getting NHL minutes at age 18 in the first place (and playing until 40).

Replacing Steven Stamkos: How the Tampa Bay Lightning Weathered the Storm

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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

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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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