AP Photo/Bill Kostroun

One of the major drawbacks of using raw puck possession (Corsi/Fenwick) numbers is that it doesn’t account for game and score situations. More accurate measures have been made available by using these numbers at even strength –where most of the game is played– and in score close situations. Score close –where the game is within one goal in the first two periods or tied in the third– is what has been the driving force for the predictive measure of the stats.

Score close situations wasn’t perfect because it limits the number of events recorded, but since it was the most accurate metric that was readily available, we leveraged it*. The most accurate has always been score adjusted puck possession, but it was only available via manual calculations. Luckily, the indispensable war-on-ice added this calculation to their page, and now we can use this to more accurately assess player performance.

*-It’s worth noting that we are limited by not only what is available, but what is available for us to analyze/evaluate. Several intelligent people have manually tracked/adjusted some of these stats, but it’s not something that has been available for all due to the manual nature of that tracking.  There will be many more advancements as more data –specifically player movement tracking– is available.

The preference for score adjusted puck possession is still the same as with regular (non-score adjusted) possession: You want to be on the positive side of 50% for CF%/FF% and on the positive side of 0%. I sorted by difference between score adjusted and raw (right column) to show how the numbers were effected. First the forwards:

Skater ES rel CF% ES rel SA CF% Difference
Fast -6.1 -5.2 0.9
Glass -6.3 -5.7 0.6
Moore 0.1 0.5 0.4
Kreider 1.3 1.6 0.3
Hagelin 1.8 1.8 0
Stepan -5.7 -5.7 0
St. Louis -2.9 -3 -0.1
Miller 5.1 4.9 -0.2
Brassard 0.2 0 -0.2
Zuccarello 4.2 4 -0.2
Stempniak 4.7 4.4 -0.3
Duclair 0.3 0 -0.3
Hayes 2.1 1.8 -0.3
Nash -0.1 -0.6 -0.5

The most interesting aspect here is that Jesper Fast has the biggest improvement when taking score into effect. His numbers are weighted down by 25% offensive zone starts though. Tanner Glass is at least a pleasant surprise at the second largest differential, but he’s seeing 45% OZ starts, so it’s less impressive than Fast and Dominic Moore’s improvements.

The person on this list I’m worried about the most is Derek Stepan, who is having a Tyler Bozakian season, and that’s not a good thing. He’s putting up numbers, but he’s been off this year.

Now the defense:

Skater ES rel CF% ES rel SA CF% Difference
Kostka 4.8 5.5 0.7
McDonagh -4.3 -3.8 0.5
Klein 4.1 4.3 0.2
Staal -2.2 -2.1 0.1
Girardi -7 -7 0
Moore 2 1.6 -0.4
Hunwick 3.8 3.2 -0.6
Boyle 2.7 2.1 -0.6

Mike Kostka got a bad rap with those awful turnovers during his short stay. The eye test catches big things like that, but not the small, subtle things done regularly. I’m not saying Kostka should be in the lineup, I’m just saying he wasn’t the huge tire fire everyone made him out to be. Unsurprisingly, Ryan McDonagh takes the biggest leap forward among the regulars (these tables contain players with > 100 minutes played).

My biggest concern, aside from Girardi, is Dan Boyle. Boyle has been underwhelming this season, and his recent demotion to bottom pairing has been evidence of that. There have been rumblings that he’s playing hurt, and I hope that’s the case since his play has been poor this year. The Rangers have been able to overcome Boyle’s ineffectiveness because of the impressive play of Kevin Klein.

Going forward, we are going to use score adjusted numbers to analyze players and teams, since they are a more accurate representation of a player’s effectiveness. However like all possession numbers, deployment –zone starts and quality of competition/teammates– needs to be factored into the equation as well.

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