Why NHL Stats and Scouting Must Work Together

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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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Input versus Output: An Ongoing Battle that No One Knows About

XKCD comics is written by Randall Munroe, a physicist who probably doesn’t know what  hockey underlying numbers (ie: #fancystats or advance statistics) even are, let alone supports them… yet – for the most part – he gets it.

Mainstream sports commentary is full of poor analysis when it comes to using numbers appropriately. Most of this comes from a lack of understanding between the difference between inputs versus outputs and how much a player can control certain factors. (It should be noted that this is a broad generalization; not everyone falls into this category).

Benjamin Wendorf displayed a bit of these factoids in his recent article Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin, but Campbell still didn’t get it.

What happened:

For those that do not know, here is a quick summary of Campbell’s article:
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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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NHL Systems, NHL History, and Forward vs. Defense Shooting

Photo by “ravenswing” via Wikimedia Commons

“It’s a matter of systems,” “They don’t have a good system,” “There is no system there”…we hear phrases much like this frequently, and I wonder just how much weight we give the word “system” in a game that flows and relies on instinct and reflex. Teams have some kind of system, no doubt, but it’s funny how the actions of any kind of system pale in comparison to the number of times we notice the classic breakout, setting up of the zone, or cycle. What I’m trying to say is, might we be putting too much emphasis on system, when the results are not clearly resulting in different shot quality? Might we be overstating the role of something practiced for a couple of months, maybe a year or two, versus 15-30 years of playing experience, and all the instincts, common tactics, and reflexes?

In my mind, systems are important in-and-of themselves, because their organization principles are intuitive. Cover the man or take away the passing lanes, apply forecheck pressure or trap in the neutral zone…these base ideas probably need to be there to keep things from devolving into pickup hockey. And you all know that game, where everyone’s a superstar forward and nobody backchecks. Seriously, no wonder you guys can’t ever find two goalies.

Anyway, with my current treasure trove of game-by-game, player-by-player data going back to 1987-88 (thanks to Hockey Reference’s excellent Play Index), I wanted to see just how much the game has evolved since the late 1980s, particularly in regards to defensemen involvement in the offense. We already know that the difference in shots-for per team, per game is 30.4 in 1987-88 to 29.1, so not a heck of a lot has changed in shot generation, and the goals/game per team has changed drastically, from 3.71 in ’87-’88 to 2.75 today. This information alone should suggest we probably haven’t improved too much in regards to what we might call offensive systems. Has defensemen involvement increased, and driven the scoring down? Have teams attempted more forward involvement to improve scoring? Will Guy Boucher ever convince us he has the key to better offense again?

I took data from about 30,000 individual player performances in 1987-88 and about 26,000 in 2012-13; I compared the player’s shot totals to their team totals in those games and derived my %TSh, or percentage of team shots metric, previously used in my piece on Career Charting.

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

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%)

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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On Guys Who Score But Don’t Drive Possession

This dude didn’t drive possession much, but MAN what a shot.

Consider four types of forwards:

1.  Forwards who don’t drive possession and don’t score points
2.  Forwards who drive possession forward and score points effectively
3.  Forwards who drive possession forward but don’t score points
4.  Forwards who don’t drive possession but score points.

The first two types of forwards are easy to think about: Type 1 forwards are bad players, not really giving value through their play and Type 2 forwards are the best type of players, those who provide value in both offense and defense and aren’t a liability if they ever go on a cold streak.

Type 3 forwards are a little trickier, but really aren’t that hard to think about – they’re your ideal 4th and maybe 3rd liner, the guy who might not score but keeps your team in the game while your better guys rest.

Then you have your type 4 forwards – the dudes who can score a bunch of points but really don’t keep the puck out of your own zone and in the opponent’s zone.  Perhaps these guys are really bad defensively, perhaps they’re completely inept in the neutral zone, or perhaps they’re guys who score mainly by being in front of the net at the right time, and thus aren’t really being helpful when the puck doesn’t come to them.

How do we value these guys?  Depending upon the point totals these guys can put up, we can value them pretty high actually.  Ilya Kovalchuk was a pretty damn good player who didn’t drive possession much, but his scoring almost certainly made up for what he cost the team otherwise.  Matt Moulson’s hands allowed John Tavares to rack up assists due to his amazing ability to be in the right position to put in goals.  Thomas Vanek likewise.

In a sense, these guys are basically role players.  Of course, that role isn’t being a grinder or a checker or some other name for defensive forward, it’s to be an offensive specialist, paying little attention to anything else.  You’d like to play these guys in positions that maximize that ability like any other role player – so high ozone starts, alongside guys who might complement those skills (playing them alongside guys who don’t have these weaknesses, and thus can make their line a plus possession line, is another typical way to handle these guys).  And scoring lots of points is a pretty important type of role for a player to have.

Most of the time, the best scorers don’t fall into this category – the skills involved with being a plus possession player are the same ones that lead to scoring goals – getting the puck into the zone by carry-in, spending more time in the O Zone, etc.  But a few guys will – think Ilya Kovalchuk or perhaps even the more recent version of Alex Ovechkin (though he used to be a clear driver of play).  These are guys you play as much as possible despite the possession problems simply because well – scoring is what wins games in the NHL.  These guys aren’t that common, so you’ll never see a low possession team dominate for multiple seasons like you did in the 80s (when three teams did accumulate such players).  But you play them anyhow and you try and surround them with a lot of plus possession talent to make up for their shortcomings.

Again, these players aren’t bad by any means – they can even be elite!  Of course, lacking possession driving skills means slumps by these guys will kill you, but for your Kovalchuk’s and Ovechkins, you’ll live with that.

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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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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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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