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Previewing the 2021-22 Oilersnation player review articles

Come one, come all. It’s time for the annual player review articles to begin.

This is a yearly endeavour that I take on here at Oilersnation where I break down the season of each Edmonton Oilers player. Like always, I’ll be using a fair amount of analytics in my analysis — as always — so I wanted to write this up as a glossary of sorts that can be referred back to.

To note, I’m in the final stages of setting up a schedule for all the posts, but I will be taking a look at all the Oilers’ RFA’s and UFA’s first, before diving into the rest of the team afterwards.

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Without further ado, here are the analytics that I’ll be referencing, and what each of them means.

The underlying numbers

Corsi for percentage – CF%

You’ll see me reference Corsi, or shot attempts, quite a bit. Corsi is a fancy name used to reference shot attempts. This includes ones that count as goals, shots on net, misses, or blocks. This is a strong metric used to constitute pace of play. A team who has a higher CF% is the team that a) has the puck more, and b) is taking more shot attempts.

This is a fundamental analytic and in it’s most simple terms, is a plus-minus for shot attempts. It’s also a strong gauge as to predicting future goals scored, too. The more attempts you are getting at scoring, the more likely you are to do so. Same thing when it’s comes to the defensive side of it.

CF/60 stands for shot attempts for per hour, while CA/60 stands for shot attempts against per hour.

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Scoring chances for – SCF%

As with all these analytics, we utilize the great resource that is Natural Stat Trick.

Scoring chances for means how many scoring chances you are getting on the ice, while scoring chances against means how many you’re giving up.

The model used to cite scoring chances is done so by looking at each shot attempt in the offensive zone and assigning it a value based on the area of the zone it’s taken in. You can read more about scoring chances, as originally defined by War-On-Ice, here.

It’s also worth noting I will utilize HDCF%, which is the scoring chances taken from high-danger areas on the ice.

SCF/60 means scoring chances for per hour, while SCA/60 means scoring chances against per hour.

Goal share – GF%

This one is fairly straightforward. It simply looks at what percentage of goals scored occurs with ‘x’ player on the ice.

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GF/60 is the rate at which goals are scored per hour, while GA/60 is goals against per hour.

Expected goals -xG%

Expected goals, in one sense of the terms, is very similar to shot attempts, but expected goals takes it a step further. It addresses the issues of where the shot attempt locations are coming from.

In doing that, expected goals places a valuation on each shot attempt and where it comes from on the ice. Instead of thinking about it as expected goals, think of it as shot quality.

This statistic can be used in tandem with Corsi. The former regards simply the zone advantage on the ice, while expected goals looks at the quality of the zone advantage.

Here’s an example.

At 5×5 this season, the Oilers controlled 52.47 of the shot attempts and 51.78 percent of the expected goals. It shows that the Oilers did a very good job of controlling the zone advantages, even if some of it came from lower percentage areas.

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PDO

PDO is one of my favourites, and most telling analytics you can use.

At its core, think of PDO as a statistic to determine luck while on the ice. With 100 as it’s median, any number above it is considered to be lucky, while any number below it is considered to be unlucky.

The statistic combines on-ice shooting percentage and on-ice save percentage together. Over time and a larger sample size, as mentioned above, this number should sit around 100. This isn’t always the case, however.

Teams like the Tampa Bay Lightning, who have a 101.6 PDO over the last three seasons at 5×5 — the second-best rate in the league over that time — have elite goaltending and elite scoring, that gives them the slight bump upwards.

Teams like the Detroit Red Wings, who have had a 98.4 PDO over the last three seasons at 5×5 — the second-worst rate in the league over that time — have been plagued by poor goaltending and poor scoring.

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Through it all, the Oilers rank an even 100 at 5×5 over the last three years.

Per 60 rates

All that’s done is for whatever statistic is being referenced whether it’s goals, points, shot attempts, scoring chances, expected goals or goals scored, it’s taken as an average number per 60 minutes of ice time.

Why do we do this? It allows us to even out ice-time averages and allow you to compare a player who plays first-line minutes, to someone who plays third line minutes, for example. Over small sample sizes, raw counts can still work, but when we look at the course of a full season, using a per 60 rate helps even us out.

Relative to the team

Similarly to the per 60 rates, statistics relative to the team (RelTM%) takes a look at the statistic over a larger scale referencing it to the rest of that players teammates. A player who has a positive RelTM% shows that they are ‘x’ percentage better on average, while a player with a negative RelTM% shows they’re worse on average.

It’s important to note that when it comes to any statistic against relative to their teammates, a negative number is actually considered a good thing, as that player is allowing less than the team average.

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For example, a player with a -6.9 CA/60 means they are allowing 6.9 fewer shot attempts per hour than their average teammate.

Individual rates

All the above analytics can be looked at on an individual basis, per 60.

Isolated impact charts

Now here’s where things get fun. The isolated impact charts are a pay-walled metric built by Micah Blake McCurdy at hockeyviz.com and breaks down each player on a personal level. This is done by accounting for their teammates, their competition and their coaching, to give a balanced look at any individual player at even strength.

McCurdy’s charts use heatmaps to help create a picture of what that player does on the ice. As described in his how-to, “red areas show where the given player causes shots to be taken at a larger rate than league average, blue areas less.”

To simplify: red means more shots, blue means less shots. Offensively, you want more red than blue and defensively, you want more blue than red.

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This will be referenced like this:

According to hockeyviz.com, Connor McDavid contributed offence at a 29 percent rate above league average and defence at a four percent rate above league average.


Zach Laing is the Nation Network’s news director and senior columnist. He can be followed on Twitter at @zjlaing, or reached by email at [email protected]