I’ve had a couple of people ask about how to use the new interactive visualizations we offer at Hockey Graphs, so I thought I’d take the time to provide a tutorial with some visual demonstrations.
Shooting Percentage
Spread of NHL Team Shooting Performances, Year-to-Year 1952-53 through 2013-14
Sort of a mid-week quick graph…I’ve been compiling data for a different project and curiosity got the best of me to see what the spread in team shooting percentages was in NHL history. We all know that shooting percentage in the NHL went up substantially during the 1980s, but what you’re seeing above is one of the reasons why we theorize that shot quality and team shooting talent might have figured more greatly in outcomes in the 1980s than it does today. With some exceptions, the standard deviation seems to have settled from about 1996-97 to the present at just under 1%, which suggests our expectations from one year to the next should only allow a team that much of a bump above or below league-average. It’s worth noting that sample will affect this measure, hence why our line is so spiky during the Original Six era, and why 1994-95 and 2012-13 might have not been as characteristic of a trend. Incidentally, this is shooting percentage for all situations.
Note: As mentioned by a reader, increased scoring is going to work together with this standard deviation to accentuate the differences between teams. League-wide, the shooting percentage and standard deviation move well enough together to cause this effect, usually portrayed by coefficient of variance, to regress heavily from 1965 to the present. The exceptions, though muted, would be the early 1980s and the more recent years of Dead Puck, so the standard deviation fairly accurately represents our variance above. CoV data:
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Trading Off: How Much Possession Can My Team Surrender and Still Win?
Photo by Michael Miller, via Wikimedia Commons; altered by author
Within the continuing discussions over the value of possession metrics, and the veracity of shot quality or shooting talent measures, there’s a point that seems to have slipped through the cracks. While there’s a spectrum of attitudes about possession and shot quality/talent, neither entirely refutes the importance of the other – and with that thinking, it’s worth considering how much you can sacrifice in one and still maintain success by the other. Put more simply, how little can a team possess the puck and still expect to shoot their way to success?
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Gordie Howe vs. Bobby Orr vs. Wayne Gretzky vs. Sidney Crosby: Not Your Typical WOWY
Photo by “Djcz”, via Wikimedia Commons
With or Without You analysis, often referred to as WOWY, frequently involves either comparing the performance of a team or particular players when a single player is and isn’t playing. While the approach is a risky one (sample size is a pretty big issue), it can actually be quite telling when you collect enough data.
The value of modern WOWY is that you can definitely get data from precisely the seconds a player played apart from the seconds they weren’t on the ice. Historical WOWY, on the other hand, cannot do much better than taking data from games a player played versus games they didn’t. To this end, then, I wanted to see if historical WOWY can tell us much of anything, and the best way to do that is to focus on players that are undisputed in their value. In this case, I went for WOWYs of the big guns, four of the best players across the eras of NHL history: Gordie Howe, Bobby Orr, Wayne Gretzky, Sidney Crosby.
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NHL Defensemen and Shooting Contributions back to 1967-68
Photo by Dave Stanley via Wikimedia Commons
I have kicked around this data in the past, most prominently in my theoretical post on offensive systems, but I really wanted to get further into the intricacies of defensemen and their historical place in team shooting (among other offensive contributions). By looking at how much a defenseman contributes to a team’s shot generation (expressed as a percentage of team shots in the games a player played, or %TSh), we can draw some interesting comparisons across NHL eras, but I haven’t yet explored how the role of the defenseman has (or hasn’t) evolved from the Expansion Era to the present, nor have I taken a look at some of the more exceptional defense shooting teams. Let me correct that now.
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:
Outperforming PDO: Mirages and Oases in the NHL
Above is the progressive stabilization (game-by-game, cumulatively) of all-situations PDO over time for the 30 NHL teams. It’s a demonstration of the pull of PDO towards the average (1000, or the addition of team SV% and shooting percentage with decimals removed), and it gives you a sense of the end game: an actual spread of PDO, from roughly 975 to roughly 1025. In other words, if you were just to use this data, you could probably conclude that it’s not outside expectations for a team to outperform 1000 by about 25 (or 2.5%) on either side.
That’s all well and good, but PDO is a breakdown of two very different things, a team’s shooting and goaltending, two variables that understandably have very little to do with each other (they are slightly related because rink counting bias usually affects both). Shooting percentage can hinge on a number of contextual variables, though its reliance on a team’s player population usually can bring it a bit in-line with league averages. Save percentage, on the other hand, hinges on one player, and what’s more past performances suggest that a single goaltender can quite significantly outperform expectations. In this piece, I want to jump into the sliding variables of PDO, and what we can expect from teams, but first I want to begin with why I’m working with all-situations PDO.
Hockey Graphs 