2015 NHL Stanley Cup 2nd Round Predictions: As Good As SAP

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“85% accuracy, y’all.” Photo by Matthias S., via Wikimedia Commons.

The first round has come and gone, and as we expected before a game had been played, brackets were not going to be fun for everyone. Most people leaning on statistical models saw their brackets chewed up by the vagaries of the playoff sample; SAP, if you’ll remember, hailed their overfit model and its “prediction” of 85% of the past 15 years of playoff series — and proceeded to do no better than a coin flip (they missed all the Eastern teams, and got all the Western matchups). An exception to the #fancystats slaughter was Nicholas Emptage, who went 6-2, which is a good thing if your site is called Puck Prediction. Not even Nicholas was a match for the gut of Steve Simmons, though, who went 8-0 in the first round. It’s the Simmons Hockey League, y’all, and he’s just sliding into our DMs.

But the big question is how our brackets, built on the tried and tested virtues of truculence, size, and experience, fared in this ultimate battle of wits and twits?

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Grantland Features: Knuckles vs. Numbers

Sometimes, I hear questions float around about whether the analytics movement has changed the NHL all that much. I wrote about this a bit in my most recent post, looking at player usage, but there’s more to be said. Thankfully, I had a great opportunity to contribute to a documentary for Grantland and ESPN called “Knuckles vs. Numbers,” which focused on the influence of analytics on the reduction of the role of the enforcer. Including myself, you’ll also see interviews with Sean McIndoe (@DownGoesBrown), Steve Burtch, Paul Bissonnette, Colton Orr, and Brian McGrattan. Check it out, get the word out, it’s worth your time.

Now that you’ve enjoyed that, I have some behind-the-scenes anecdotes and information from the experience that are worth mentioning.
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2015 NHL Stanley Cup 1st Round Predictions: Too Cool For School

Uploaded by "ali", via Wikimedia Commons; altered by author

Uploaded by “ali”, via Wikimedia Commons; altered by author

I don’t like to predict the playoffs, in part because it makes plenty of people look like geniuses that probably aren’t, and makes geniuses look pretty dumb. And really, that’s because the stakes are high but the influence of luck is pretty drastic — not surprising when a team can advance by only winning 4 of 7 (i.e. barely successful enough to make the playoffs in the first place). Lastly, given a flood of predictions and contests in the wake of the Summer of Analytics, nobody who “wins” their predictions is going to look a lot better than the other person who almost “won.” So I’m exercising my right to fart around.

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Who are NHL Coaches Playing More with the (Big) Lead?

Photo by Michael Miller, via Wikimedia Commons

Johan Larsson’s TOI% jumps 8.5% when the Sabres are leading by 2 or more goals (which is never). Photo by Michael Miller, via Wikimedia Commons

Right out the gates, I knew two things: 1) I wanted to take TOI% data from close scores and subtract it from TOI% data from 2+ goal leads, and 2) that it would automatically tell us that perceived poorer players are given more playing time with the lead. Why? Because they tend to play less when the score is close, which increases the likelihood that a differential with 2+ goal time on-ice will show they get to play more with a big lead. That said, I wanted to run a quick study to see just how much of a difference that time swing could be, and which players come out of the woodwork on either end.

But first, I want to whittle away the small sample players, and to do that I’m going to run a quick test to see at what # of games played this TOI 2+ minus TOI Close differential (let’s call it “TOI Lead Diff”) stabilizes.

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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

Five Players to Avoid in Your Fantasy Draft

Image from Ivan Makarov via Wikimedia Commons

Image from Ivan Makarov via Wikimedia Commons

Fantasy hockey season is just around the corner, and many drafts will take place in the upcoming two weeks. I’ve identified five players whose underlying (and in some cases overlying) numbers suggest their 2013-14 statlines may contain some mirage-like components, and who are getting picked higher than they likely should be.

1. Joe Pavelski

Pavelski finished third in league goal scoring last season with 41 goals, shattering his previous career high of 31 goals set in 2011-12. What should be concerning to potential fantasy owners is that the spike in goal scoring was driven by a jump in shooting percentage, not an increase in shots on goal. In fact, Pavelski’s 2.74 shots on goal per game was his second lowest mark since his sophomore 2007-08 season. His drop in shots was more than made up for by his shooting percentage jumping up to 18.2%, well up from his previous career mark of 10.0%.

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