How to be a little wrong everywhere

NHL skater and goalie projections 2016-17

Posted by RDJ on September 17, 2016

Image is If I was an alien, visiting Earth, I'd disguise myself as a cactus too. by Carl Jones used under license CC BY-NC-ND

I’ve decided to upload my projections for NHL skaters and goalies this year. These are derived from basic statistical models. They cover many players and a variety of stats, but of course come with some caveats. Mainly, they’re wrong.

Why

There aren’t many sources that share NHL projections. The ones that do suffer from a few limitations:

  • They often cover limited statistics, not always covering blocks, hits, or faceoffs
  • They might be shared in a presentable format (e.g. a pretty website), but not parser-friendly (e.g. CSV)
  • They are often opaque about their methodology. I don’t know if they use models or how they arrive at their conclusions
  • Many charge a fee

Of course, mine also suffer from their own set of limitations, so I’ll share a few links to others.

I haven’t evaluated any of those quantitatively, nor purchased the ones with fees (not that I think fees are unfair, $5 isn’t a lot of money for someone’s time). So this isn’t an endorsement. But I would hope that the effort level plus domain knowledge of those writers would result in better predictions than my lazy stats.

Skater projections

These formed the basis of my fantasy draft last year, and the methodology is mostly unchanged from how I described it previously. I use LASSO models with lags, averages, position, and recent experience. What this means is that any given stat prediction ends up (mostly) being a combination of last year’s value for that player, and their career average. There’s not much to it.

This is data in, data out. No manual review.

Some of the things my models will do a poor job of:

  • It doesn’t (directly) know about injuries
  • It doesn’t know about trades, of players or the players they will play with
  • It doesn’t know about movements up and down the depth charts
  • For that matter, it doesn’t know about retirement

Those are harder things to put into the model, because they require a lot of manual data creation. Not just for this year, but imagine going back and filling out 15 years of data. Not with after the fact knowledge, but what we knew as of September in each of those years.

Here are the top 20 players, ranking by one of the metrics I described in that earlier piece. My predictions come with decimal precision, because round numbers are for jocks.

name gp g a hits blocks ppp shg sog fow
Brent Burns 81 24.921 44.27 109.2 132.02 25.5178 7.0e-01 294.8 12.2
Patrice Bergeron 77 26.822 40.16 73.6 51.93 21.9328 1.1e+00 242 960.3
Claude Giroux 74 23.52 42.66 80.0 38.31 23.9544 9.7e-01 209.6 871.5
Alex Ovechkin 71 32.857 34.43 182.7 34.73 23.7982 4.6e-01 306.5 0.0
Jamie Benn 71 25.245 34.5 125.3 46.34 19.2462 7.8e-01 207.8 208.7
Tyler Seguin 76 29.343 40.53 87.2 33.83 23.455 6.7e-01 268 402.5
Sidney Crosby 68 24.786 43.23 66.2 33.64 24.231 5.4e-01 193.9 764
Erik Karlsson 69 16.16 38.31 72.6 130.34 20.8205 3.3e-01 201.5 0.0
Ryan O’Reilly 74 20.553 36.02 30.6 44.23 18.0807 1.0e+00 172 887.5
Kris Letang 68 15.588 35.39 118.5 110.14 20.6859 2.5e-01 191.5 0.0
Ryan Kesler 69 16.675 26.72 125.1 69.51 11.9275 8.8e-01 148.3 744.8
Anze Kopitar 70 19.542 36.56 65.1 52.06 17.9106 8.5e-01 160.3 722.5
Blake Wheeler 75 24.046 34.75 92.4 49.54 16.7632 8.4e-01 222 18.9
Evander Kane 72 23.837 23.92 180.3 31.89 11.6929 9.2e-01 254.2 64.2
Nazem Kadri 70 19.851 29.23 131.8 42.36 14.8053 6.9e-01 201.1 493.1
Patrick Kane 71 28.767 42.29 42.3 23.43 26.052 3.1e-01 235.8 21.9
Nathan MacKinnon 73 22.473 32.68 59.6 51.52 17.0451 6.7e-01 225.4 435.6
John Tavares 68 23.546 34.9 49.6 30.25 18.9998 5.4e-01 202.4 605.7
Brandon Dubinsky 69 15.315 25.19 186.5 40.24 10.7869 7.2e-01 142 613

Yeah, I didn’t expect to see Dubinsky in there either.

Silly Dubinsky

I greatly enjoy the two facial expressions present during this groin kick

00064735 from Keith Allison used under licence CC BY-SA

Goalie projections

These are a little different and a touch more complicated than my skater models. I used a slightly more thorough feature set, and instead of LASSO I used gradient tree boosting. I’m planning on writing a follow-up piece with more details on that.

These stats are new. They’re also infuriating. As Matt Cane apparently said, “Goalies are voodoo and hate statistical analysts.

I couldn’t agree more.

I have an academic background in economics (I know, I know…) and I’ve heard the following nerdy joke a few times.

Three econometricians went out hunting, and came across a large deer. The first econometrician fired, but missed, by a meter to the left. The second econometrician fired, but also missed, by a meter to the right. The third econometrician didn’t fire, but shouted in triumph, “We got it! We got it!”

What happens when you predict something that has a lot of noise in it? You (using most common loss functions) end up with predictions that are highly regressed to the league average. Because that actually makes sense. It’s totally possible that Braden Holtby puts up even better numbers next year than his Vezina year last season, but it’s also totally possible that his save percentage and goals against average drop 15 points and jump by 0.3, respectively. So we end up with something in between.

Bambi

Bambi likes economists

So in the end, we end up no extreme values in the predictions. Last year, 8 goalies got 35 or more wins. The highest in my predictions? 34 (Braden Holtby). 8 goalies (minimum 10 games) ended up with a GAA below 2.20. Lowest in my dataset? 2.32 (Carey Price). For save percentage, that most fluctuating of major stats, the highest I have is 0.918 (Matt Murray). A third of the league did better than that last year!

So while I know many goalies will put up better numbers (or worse numbers) than my extremes, for no one goalie do I predict extreme values.

These also suffer from the same issues as the skater projections, which is probaby more damaging in the case of goalies. The models don’t know who the starters and the reserves are. They aren’t constrained to make them consistent by team. You can manually scale the wins and shut-out projections to match the number of games you expect.

Without further ado, here’s 20.

player_name year p_GP p_W p_SO p_GAA p_SV%
Braden Holtby 2017 59.697 33.521 4.542 2.344 0.914
Jonathan Quick 2017 62.109 31.199 4.663 2.361 0.913
Pekka Rinne 2017 58.053 30.868 4.073 2.46 0.912
Marc-Andre Fleury 2017 58.656 30.328 4.354 2.389 0.913
Ben Bishop 2017 57.048 30.174 4.246 2.346 0.913
Henrik Lundqvist 2017 56.242 29.904 3.946 2.46 0.912
Corey Crawford 2017 55.906 29.57 3.669 2.415 0.912
Martin Jones 2017 56.686 28.42 4.471 2.408 0.913
Roberto Luongo 2017 55.108 27.547 3.71 2.461 0.913
Devan Dubnyk 2017 56.89 27.33 3.419 2.537 0.915
Petr Mrazek 2017 55.551 26.857 3.779 2.534 0.913
Cory Schneider 2017 57.048 24.967 3.824 2.353 0.914
Tuukka Rask 2017 56.393 24.745 4.19 2.424 0.915
Semyon Varlamov 2017 56.301 24.468 3.383 2.632 0.915
Cam Ward 2017 56.72 23.865 3.004 2.576 0.907
Jake Allen 2017 48.043 23.797 3.867 2.442 0.913
Craig Anderson 2017 50.236 22.972 2.973 2.726 0.91
Steve Mason 2017 56.014 22.93 3.431 2.512 0.915
Antti Niemi 2017 47.639 22.461 3.577 2.427 0.91
John Gibson 2017 45.651 21.999 2.836 2.495 0.913

Final words

So there you have it, some no-holds-barred nothing-to-hide projections. Full tables are in my github link. I’ll use them as a starting point, but by no means an ending point, on my opinions and pool draft this year.

Remember, they’re horribly wrong.

Enjoy!