Using Goal Differential to Predict the Future

Scott Reynolds
November 15 2012 04:26PM


Photo by Jeff Nelson via Wikimedia Commons

 At the start of each season, all teams are equal in the standings. A team that was among the league's worst a season ago is tied for first at the beginning of October December, and hope springs eternal for all but the most browbeaten fanbases. Of course, those particularly unfortunate fans remember something important that many fans like to forget: every team has a past, and that past matters. Sure, teams change in the off-season but there's a lot of continuity too, which means that, even when we confine ourselves to a simple thing like the previous season's goal differential, we can get a pretty decent idea of what can be reasonably expected in the year to come.

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Hogging the Puck: A Follow-Up (All That You Wanted & More)

Ben Wendorf
November 15 2012 07:45AM


After initially publishing the piece on players who "hog" the puck (players who take a high percentage of the Fenwick attempts-for {shots + missed shots} when they're on the ice), I received a lot of helpful feedback and queries about the metric, which I called "percentage of attempted shots," or %AttSh. Some of the questions revolved around, "What if the player is playing with someone with a high %AttSh, like Rick Nash or Jeff Carter?" I had another question wondering if the %AttSh had a normal distribution like all of us stats folks love. And our own Eric T. wondered aloud what a chart of the player's shooting percentage minus his linemates' shooting percentage (x) would look compared to the player's %AttSh (y). Some of these questions I'd been wondering about myself, but some were angles I hadn't considered, so I figured I ought to put together a follow-up post to tie up some of those loose ends. Enter if you dare...

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Reference library: Variance and regression

Eric T.
November 14 2012 04:50PM

This article is part of the NHL Numbers reference library, which seeks to collect articles from around the web that have contributed to our understanding of the game.

This page is devoted to articles that look at the role of variance and regression in hockey. This will include looking at how much variance a given statistic has and whether it reflects a true talent, how actual performances compare to random chance models, etc.

We need your help to keep the library complete and up to date -- contact me on Twitter (@BSH_EricT) or via email (bsh.erict -at- gmail) with suggestions of articles you think we should consider adding.

Return to the library main index.

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The One-Percenters

Scott Reynolds
November 14 2012 09:04AM


Photo by Mike Durkin via Wikimedia Commons

Before the start of the 2011-12 season, I talked about a group of players that I called "the one-percenters", a group of players whose cap numbers were less than one percent of the salary cap. In order for a player like this to cover his bet, all he needs to do is competently fill a spot in the lineup on a regular basis. But some of these players can do more than that, which makes them extremely valuable. Before the season, I mentioned a few candidates that I thought might provide that kind of value: Niclas Bergfors, Bryan Bickell, Evgeni Nabokov, and Frans Nielsen. A couple of those players did, a couple of others didn't, and a few more bargain surprises emerged. So which one-percenter helped his team most in 2011-12?

Let's take a look at my top five candidates:

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Older players and the grind of a long season

Eric T.
November 13 2012 02:31PM


I've often heard it suggested that players wear down over the course of a season.

When I wrote about whether players elevate their game in the playoffs, multiple people in the comments argued that players can and should conserve energy during the regular season. It's a particularly common suggestion for older players, who are presumed to be more prone to fatigue. Guys like Jaromir Jagr and Teemu Selanne hate taking days off, but players and coaches are so convinced that they will wear out that they insist on it.

I'm not a physiology expert by any means, so I'm not qualified to make direct assessments of whether older players will suffer more cumulative fatigue than younger players would. However, I am capable of looking at whether such fatigue is born out in the stats.

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