In the salary cap era in the NHL, the entry draft has become a top priority for general managers. Acquiring players like Connor McDavid, Auston Matthews, Johnny Gaudreau, or Aaron Ekblad is most easily done at the draft table. More and more, GMs are recognizing that players peak at a young age, making long-term deals for early-20-somethings a wise investment, even if valuations are fueled by projection.
Here’s a sample of contracts for under-25-year-olds:
Note: This is Part 2 of the series on coaching analysis. Part 1 is here.
In this post, I’ll do a brief review of each team’s coach history from the current Metropolitan Division. These graphs only show a team’s performance in 5v5 situations from 2005 to 2016. The vertical lines indicate when a season begins. The horizontal line shows the 50% mark, where a team would be if it had as many shots for as shots against. The bold line is a smoothed representation of the team’s shot percentage. The faded bands around the bold line indicate 95% confidence intervals. These intervals show the uncertainty around the smoothed estimation of the data.
Columbus has been surprisingly good this year. As of this writing, the Blue Jackets are first in the league in points and goal differential with games in hand. Remember: Columbus, in terms of preseason predictions, was pegged as more like a 5-8 finisher in the Metropolitan division (e.g. see here, here, here, here, and here).
That said, it’s still early. If it might take 70 games for skill to overtake randomness in terms of contribution to the standings, and if teams like the 2013-14 Avalanche and 2013 Maple Leafs (to name two prominent examples) can fool us for so many games, it doesn’t seem so unbelievable that a team could do it over just 32. (And the Blue Jackets aren’t the only example this year, either–Minnesota is under 48% possession and has a 103+ PDO right now.)
The holiday season is here and as my gift to you, I am offering a series of graphs that show how all 30 teams around the NHL are performing. I know this might not be a particularly good gift. But it’s the best I can do.
The NHL season is a long and grueling affair and most teams will experience some ups and downs over the course of 82 games. Even a team that had a 67% chance of winning every game it played, would still have a 20% probability of putting up a five-game losing streak. And this is just straight probability theory with fixed probabilities. What happens when you consider all of the factors that go into determining the probability of winning an individual game, let alone predicting performance over an entire season?
Well, I’m not here to answer that question.
What I am here to do is to try to apply an analytical technique that was developed in the 1950s for the purposes of quality control in industrial and manufacturing processes to the game of hockey. Continue reading
Happy Max Corsi Productivity Day! We’ve reached the point in the season where Corsi best predicts future winning percentage. There’s plenty of more advanced ways to better predict how the rest of the season will go, but Corsi offers a simple baseline in a way that helps explain why it is so important. I’ll first explain what that means and why it matters, then take a look at how we can use it to predict basic shifts in the standings for the rest of the NHL season.
Every year right around December 1, NHL analysts and fans alike start thinking a little more seriously about playoff chances. Although in many ways this is an arbitrary point in time, particularly when linking it to US Thanksgiving, there’s also some logic to it as well.
By the end of November, we are two months in and most teams have played 20-25 games, or a quarter of the season. And from an analytics perspective, that’s about the point when you can start making use of most of the early season data.
So while most of the traditional playoff discussion at this time of the year tends to focus on points or even where teams are in the standings, let’s take a look at how early-season shot-based metrics relate to whether a team ultimately makes the playoffs.
So on Thursday, I went on a bit of a rant about the role of defensemen in the NHL. Well, rant is probably the wrong word, as it wasn’t particularly emotional, so let’s call it instead a “tweetstorm”. This piece will expound upon those tweets in greater depth, as I think it’s a topic that deserves a little more than what 140 characters and snark can provide.
Though I can’t promise this won’t also include some snark.
The entire discussion was kick started by this tweet:
Now, you’re probably thinking “oh man, a Shea Weber analytics take. Let me go get my popcorn for the inevitable scathing fallout.”
Hockey Graphs 