2016-17 Midseason Rainbow Charts

As the title says, these are the midseason “Rainbow Charts” I released on twitter back in mid-January. Yes, I am putting them up on the blog just now, mid-February. I’ve been busy, yo.

Anyway, much like the original rainbow charts, these charts compare players against the results of their peers who have similar TOI. So a second pairing defender is being compared against the historical average of all second pairing defenders, not against his teammates.

By using standard deviations, the idea is to provide some context around usage, as often we get hung up on decimal places. Especially when looking at relative zone starts, the deviations are quite large, so it takes a lot to move a player outside of the “average” zone (the middle square).

Weighted Corsi is a combination of CF60, CA60 and Relative CF% standard deviations, weighted.

Click “Read More” to get to the charts. Ctrl+F will get you directly to your team if you don’t want to scroll through everything.

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2015-16 Rainbow Chart Masterpost (M-W)

Ok guys, get ready for an absolute crap-ton of charts coming your way. This isn’t an explainer, merely just a data dump.

The best way to find your team is to hit Ctrl+F and use the team name. Otherwise, everyone’s in alphabetical order.

Feel free to save and use. Consider this blanket permission to use in blogs or other posts as long as it links back to here or my twitter, @Classlicity.

Click this link for Part 1: A-L 

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2015-16 Rainbow Chart Masterpost (A-L)

Ok guys, get ready for an absolute crap-ton of charts coming your way. This isn’t an explainer, merely just a data dump.

The best way to find your team is to hit Ctrl+F and use the team name. Otherwise, everyone’s in alphabetical order.

Feel free to save and use. Consider this blanket permission to use in blogs or other posts as long as it links back to here or my twitter, @Classlicity.

Click here to go to Part 2: M-W

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Eat Like a Star…On a Budget

On Friday, Jonathan Toews did his best convince the world he’s not a robot, and of course, failed dramatically.

A lot of people ignore the fact that what they eat can have an impact on their bodies. Eating the right food not only helps you get lean, but it changes how you think, act, feel and perform in all aspects of your life. This morning is a great example of the balance of whole foods I try to find with most meals. Today I have a small salad with mixed greens, a couple eggs, sauerkraut, nuts, seeds and goji berries. Alongside, a small bowl of gluten free chia coconut oatmeal with wild berries, a scoop of Walnut Almond Cashew butter from @Onnit and a nice big cup of @Onnit coffee with their MCT oil. Now I'm ready to overcome whatever obstacle the day decides to throw my way. #whatmakesyoubetter #totalhumanoptimization

A post shared by Jonathan Toews (@jonathantoews) on

He also revealed that he, like most athletes, have zero concept of how real people stay healthy. After all, most of us don’t make $10.5 million a year, and budgeting is a concern of ours.

While it’s obvious that this was a sponsored post, one enterprising hockey fan added up how much it would cost to make Toews’ ideal breakfast.

That said, eating healthy is important. Food fuels us, and while I always scoff at fads like “paleo” or diets like “Atkins” there are some pretty easy ways to up your nutrition, cut empty calories, and not have to pay $17 for fancy nut butter.

Of course, diets and budgets are extremely personal, so make sure you’re following a plan that is best for your situation.

Still, here are a few tips and strategies that have helped me eat healthier without breaking the bank. Most of these are extremely common sense, so you may have heard them before, but I hope this is helpful to a few people.

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3.10.16 Rolling Average Charts

Under the cut you’ll find all the Rolling average charts I tweeted this weekend. They include all games up through 3/10/16.

These show a teams’ 5v5 score adjusted Scoring Chances For per 60 (SCF/60), Scoring Chances Against per 60 (SCA/60) and PDO as a 10 game rolling average for the 2015-16 season. They’re expressed as standard deviations from the NHL Average. Basically that means very little, other than the fact that positive is good, and negative is bad.

They also include a rolling average of the win/loss record of each team. In these averages, I’ve devalued OT and Shootout wins, as these are far more unpredictable (read as: up to chance) than regulation wins.

The intent of these charts are to show the biggest influences on winning, and why one metric never tells the whole story. (Heck, they don’t even include special teams play!)

Anyway, they’re alphabetical by city, so if you’re interested in one team in particular use Ctrl+F to find them. They’re all watermarked, so feel free to take and share. A tag back to here, or my twitter @Classlicity would be much appreciated.

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