Black Box: Even-Strength Data

12 years ago

We’ll be presenting weekly statistical summaries of the Calgary Flames every Monday, each update being composed of four sections, the 2nd of which will involve data related to how they spend the majority of their time: at even-strength.

Having already covered how each player is being used with an OZQoC chart in the first section, we’ll have the proper context to understand how Calgary’s over-all performance differs with each individual player on the ice. Without this data we’d be comparing players who start in their own zone against top competition, to those getting premium minutes in the opposing zone against depth lines, and drawing the wrong conclusions.

This even-strength summary will include a player’s individual scoring rate (ESP/60), but will more importantly also measure the entire team’s success when they’re on the ice, because hockey is ultimately a team sport. This data will be presented in three common fashions, that will generally all tell the exact same story.

1. Corsi events (CE), which is simply all of a team’s attempted shots: goals, saves, blocked shots and missed nets, and is pretty much the best possession-oriented metric available (over sub-season data samples).

2. Scoring chances (SC), defined and studiously recorded by Kent Wilson, and for some select other teams by the other Kents of the world.

3. Goals (G), which may be the smallest sample size, but the easiest to truly understand, and likely the one that most closely matches our own subjective observations (which is not necessarily a good thing).

Each of these will be recorded as a percentage, and also in For/Against format (over 60 minutes) so you can look at the offensive and defensive component separately.

Defensemen

Before looking at the data, keep the following things in mind:

Context! Who plays in the toughest situations, based on what we saw in the charts in section 1. For instance, we know that Jay Bouwmeester’s data will be pulled down from playing the tough minutes, while carefully-sheltered Anton Babchuk’s numbers will be inflated.
Linemates! Who plays with who, because a mediocre talent like Cory Sarich might have his numbers pulled up by playing with a bigger star like Mark Giordano. To figure this out, we’d need to do a WYWO (With-You-Without-You) analysis, to see which one continues to play well when they’re split up.
Playing time! Derek Smith, T.J. Brodie, Brett Carson and Brendan Mikkelson didn’t play frequently enough to draw any real conclusions. Also, Smith, Babchuk, Carson, Mikkelson and Scott Hannan played at least part of the season on other teams, skewing their results in one direction or another.

Not to beat a dead horse, but remind yourself to think of such things every time you look at this data – or any other data, for that matter. It’s what ultimately separates those whose understanding of hockey is truly improved by this type of analysis, and those who are eventually forced to dismiss it altogether.

Player            ESP/60 CEF CEA  CE%   SCF  SCA  SC%  GF   GA   G%
Derek Smith         0.5   43  32 57.8%                 1.4 0.0 100.0%
Mark Giordano       0.7   57  51 53.1% 17.5 14.2 55.2% 2.2 2.2  50.0%
Brett Carson        0.0   48  43 52.8%                 4.5 1.5  75.0%
Cory Sarich         0.3   55  50 52.2% 16.0 14.9 51.8% 2.4 2.1  53.8%
T.J. Brodie         0.0   60  55 52.1% 12.9 14.5 47.1% 0.0 3.2   0.0%
Anton Babchuk       1.1   59  55 52.0%           49.4% 2.4 1.3  64.7%
Chris Butler        0.6   53  51 51.3%                 2.4 1.9  55.4%
Steve Staios        1.0   53  51 51.0% 14.3 14.7 49.2% 3.0 2.2  57.4%
Jay Bouwmeester     0.6   53  51 50.9% 16.9 15.8 51.7% 2.3 2.3  50.0%
Scott Hannan        0.4   54  53 50.5%                 2.2 2.1  51.0%
Brendan Mikkelson   0.4   47  59 44.7%           44.2% 1.6 2.8  36.4%

Oh man – look at all these numbers. How do we make sense of these?

First of all, we’ll sort them not by even-strength scoring rate (remember: team game!), but by CE% (Corsi Event Percentage). You can conceptually say that the Calgary Flames had possession of the puck, and were therefore in control of the play, that percentage of the time. For instance, they enjoyed control 53.1% of the time when Giordano was on the ice, and 50.9% with Bouwmeester – but remember the tough minutes he had to play!!

Then, glance at the other percentages, because they should usually match. Those which differ greatly either signal a player who possesses/lacks the ability to help the team translate possession into chances and goals, and/or are just lucky/unlucky and will soon regress back to their CE%. In general, the amount of luck involved increases as you read to the right (key point!).

You can ignore the F/A (For/Against) columns for now, unless you really want to dive into the data and look at the offensive/defensive splits. This is how you know that Babchuk is being called upon for offense, and Sarich for defense.

In the end you can learn a great deal from this simple little table, and what you can’t learn can easily flow from this table in separate analyses conducted by the Kent Wilsons of the world.

Forwards

Having seen this table in the more easily digestible portion of defensemen, take a look at the forwards. Read it the same way, because the exact same concepts apply, except that the differences between players tends to be a little wider.

Remember: it’s ordered by CE% because it’s the best representation of how well a team controlled the play with the given player on the ice, and more luck (or “transient factors” if you prefer) comes into play as you read to the right.

And don’t study Raitis Ivanans, Clay Wilson, Lance Bouma, Steffan Meyer, Paul Byron, Greg Nemisz, Guillaume Desbiens and Pierre-Luc Letourneau-Leblond too carefully, since they have very limited playing time, often for other teams.

Player            ESP/60 CEF CEA  CE%   SCF  SCA  SC%  GF   GA   G%
Raitis Ivanans      0.0   79  50 61.1% 14.4 14.4 50.0% 0.0 7.2  0.0%
Tim Jackman         1.6   60  43 58.3% 14.9 12.5 54.3% 2.1 1.6 56.5%
Mikael Backlund     1.6   60  46 56.7% 17.3 14.0 55.4% 2.5 2.2 53.3%
Clay Wilson         1.4   45  37 55.0%                 3.0 1.7 64.3%
Lance Bourma        0.6   60  49 55.0% 14.0 14.0 50.0% 1.3 1.9 40.0%
David Moss          2.4   59  51 53.6% 15.8 12.2 56.4% 2.9 1.9 61.2%
Steffan Meyer       1.0   53  46 53.4% 11.0 10.4 51.2% 2.6 2.1 55.6%
Curtis Glencross    2.1   55  49 53.0% 16.1 14.2 53.2% 2.5 2.2 53.8%
Matt Stajan         1.8   58  52 52.6% 17.3 15.5 52.8% 2.7 2.7 49.4%
Niklas Hagman       1.3   56  52 51.8% 15.6 14.8 51.3% 2.1 2.1 50.8%
Tom Kostopoulos     1.2   62  58 51.7%           50.2% 2.4 2.6 47.5%
Paul Byron          0.7   58  56 51.2%                 2.8 2.8 50.0%
Jarome Iginla       2.4   54  52 51.1% 18.7 16.1 53.7% 2.9 2.3 55.4%
Olli Jokinen        1.8   52  52 50.1% 15.8 15.1 51.2% 2.0 2.5 44.7%
Alex Tanguay        2.5   52  52 49.9% 18.1 15.4 54.0% 3.1 2.5 55.5%
Lee Stempniak       1.7   55  57 49.4%                 2.2 1.9 54.1%
Greg Nemisz         2.0   61  63 49.2% 13.7 21.6 38.9% 2.0 3.9 33.3%
Brendan Morrison    2.0   51  54 48.5% 15.7 15.1 50.8% 2.7 2.1 56.9%
Rene Bourque        1.7   51  54 48.5% 14.4 16.4 46.6% 2.0 2.3 46.8%
Guillaume Desbiens  0.0   39  63 38.5%                 0.0 2.1  0.0%
P. Letourneau-Leblond     17  35 33.3%                 0.0 17.3 0.0%

If converting territorial advantage (represented by CE%) into scoring chances (SC%) and goals (G%) were truly a skill, then Jarome Iginla and Alex Tanguay would be those most likely to have it, correct? If so, it doesn’t seem to amount to much, only an increase somewhere between 2.8% and 5.6%. It would also mean that David Moss possesses the exact same talent.

In fact, it’s interesting how the Flames’ better players were actually worse at controlling the play, even though our earlier look at the OZQoC chart didn’t show them playing in tougher situations. At the very least, the OZQoC data prevented us from getting too excited by Jackman and Backlund’s success, since we know they didn’t line up in their own zone and against top competition as often as the others.