What if statistics chose the All-Star lines?

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No, not roster. Lines. This won’t be a discussion of hits and misses for the rosters.

While usually Hockey-Graphs tends to stay in the more serious and analytical side of sports statistical writing, I thought “why not have a little fun” since that’s what the All-Star break is supposedly about.

How would one shape the line ups for tonight if the best (minus some missed calls and injured) in the business were designed by statistical analysis (with a pinch of old-school eye-test)? Continue reading

Trading Off: How Much Possession Can My Team Surrender and Still Win?

Photo by Michael Miller, via Wikimedia Commons; altered by author

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?
Continue reading

The Hockey Graphs Podcast: Episode 2

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Welcome to the second episode of the Hockey Graphs podcast, where Rhys Jessop (of Canucks Army and That’s Offside) and Garret Hohl continue talking about hockey while learning how to podcast. Join us as we discuss the CSS rankings, Vancouver Canucks, Winnipeg Jets, Toronto Maple Leafs, the NHL’s disciplinary practices, and the up coming All-Star game. Continue reading

The relationship between Corsi% and winning faceoffs.

Faceoffs have always been an interesting area of research. There have always been individuals in the media and public who extol faceoffs importance; I have even heard quotes like: puck possession is so important and you cannot win the puck possession battle if you are starting without the puck.

Not too long ago Gabriel Desjardins showed that the impact of a faceoff is real (as one would expect) but likely over glorified by some. One example from his study showed shot rates after an offensive zone faceoff:

From these numbers Desjardin estimated an impact of +2.45 goals for every 100 non-neutral zone faceoff wins over 50%, and +3.66 for every 100 for special teams. A real impact, but not overly huge impact. Neutral zone faceoffs carried even less of an impact with +0.90 goals for every 100 faceoffs over 50%.

But what about faceoffs overall relationship with possession? Continue reading

The Hockey Graphs Podcast: Episode 1

qe7ScKvj

Welcome to the inaugural episode of the Hockey Graphs podcast, where Rhys Jessop (of Canucks Army and That’s Offside) and Garret Hohl navigate the wonderful world of podcasting for the first time ever. Join us as we discuss Vancouver Canucks and Winnipeg Jets prospects, what the hell is up with the Anaheim Ducks, and, of course, a healthy dose of fancystats. Continue reading

One of the many issues with the Toronto Maple Leafs

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Rand Carlyle was recently fired from the Toronto Maple Leafs. This brought joy to many online Leaf fans as many -legitimately- believed Carlyle to be a source of the Maple Leafs consistently being out shot, out possessed, and out chanced.

Of course, Carlyle was not the difference between the Leafs spontaneously becoming a contender in the east. There are issues with the Maple Leafs that will take some time for Brendan Shanahan and company to fix. Continue reading

Back to Basics: Forward Univariate Analysis

Uni - CF%2League wide univariate analysis isn’t very sexy, which is why you rarely see it used in the hockey blogosphere. Still, the information is necessary in better understanding what we are describing and adding context. It is also useful for looking back at whenever a variable may not impact or work in a model as you initially hypothesize.

I gathered all player season data for each full (excluding lockout) season available in the “Behind the Net era”, filtering only forwards with 100 or more minutes. These seasons were combined into one massive sample of 2368 player seasons. Continue reading

Goaltender Performance vs Rest

Photo by Michael Miller, via Wikimedia Commons

Photo by Michael Miller, via Wikimedia Commons

I couldn’t find this data (if it’s out there, please point me to it), so I went back to 1987 and pulled goaltender performance vs games rest. We knew goalies did poorly in the second game of a back-to-back pair, but I’m surprised to see such a large gap for two and three games. (The overall dataset is roughly 40000 games.)

Days between Games % of Games Mins (G1) Mins (G2) Shots Vs (G1) Shots Vs (G2) Sv% (G1) Sv% (G2) W% (G1) W% (G2)
1 9.5 54.7 55.0 28.9 29.7 0.905 0.897 0.498 0.421
2 35.6 57.0 56.8 28.7 28.7 0.908 0.901 0.522 0.486
3 19.2 57.1 56.7 29.0 29.0 0.905 0.900 0.514 0.481
4 12.1 56.7 56.3 29.2 28.7 0.899 0.898 0.477 0.487
5 7.2 55.4 55.2 29.0 28.8 0.892 0.899 0.440 0.448

There are lots of systematic issues here (e.g. most back-to-back games are on the road) but simplistically, this would mean goalie rest obscures the bulk of a goaltender’s value. That seems implausible and worth looking at in more detail…

Schedule Adjustment for Counting Stats

Edit:There is another version of this article available in pdf which includes more explicit mathematical formulas and an example worked in gruesome detail.

Rationale

We all know that some games are easier to play than others, and we all make adjustments in our head and in our arguments that make reference to these ideas. Three points out of a possible six on that Californian road-trip are good, considering how good those teams are; putting up 51% possession numbers against Buffalo or Toronto or Ottawa or Colorado just isn’t that impressive considering how those teams normally drive play, or, err, don’t.

These conversations only intensify as the playoffs roll around — really, how good are the Penguins, who put up big numbers in the “obviously” weaker East, compared to Chicago, who are routinely near the top of the “much harder” western conference? How can we compare Pacific teams, of which all save Calgary have respectable possession numbers, with Atlantic teams, who play lots of games against the two weak Ontario teams and the extremely weak Sabres? Continue reading

The Defensive Shell is a good idea in theory. Unfortunately, it doesn’t work.

The results of score effects are pretty basic hockey analytics knowledge at this point.  Teams down in goals tend to take more shots, while teams up tend to take less, with the effect becoming larger as the game goes on.

We often explain this effect by saying teams go into a “defensive shell”, playing extremely conservative on offense to avoid easy opponent scoring opportunities, at the cost of more time in the team’s defensive zone.  It is of course, not a one team effect either – we often emphasize that the other team is taking greater risks as well to try and score, which is why the shots taken by the team with the lead go in at a higher rate than normal.    That said, it’s pretty much accepted that going into a shell would be a losing strategy for a team to attempt over a whole game, which is why teams don’t attempt this strategy for a full game. Continue reading