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October Scoring Chance Summaries

The month ended with the New York Rangers having a scoring chance differential of -8. All of that -8, however, comes from the teams ‘talent’ of sending two people to the penalty box at a time. At even strength, the most important strength, the team finished with a +4 differential, a positive sign going forward. We can look at the overall totals by checking out the numbers that each goaltender finished with.

EVF EVA EV +/- EV Time EVF/60 EVA/60 DIFF
Lundqvist 105 110 -5 363.73 17.32 18.15 -0.82
Biron 32 23 9 100.95 19.02 13.67 5.35
Total 137 133 4 464.68 17.69 17.17 0.52
PPF PPA PP +/- PP Time PPF/60 PPA/60 DIFF
Lundqvist 36 5 31 61.33 35.22 4.89 30.33
Biron 2 0 2 5.32 22.57 0.00 22.57
Total 38 5 33 66.65 34.21 4.50 29.71
SHF SHA SH +/- SH Time SHF/60 SHA/60 DIFF
Lundqvist 5 33 -28 48.95 6.13 40.45 -34.32
Biron 0 9 -9 11.95 0.00 45.19 -45.19
Total 5 42 -37 60.90 4.93 41.38 -36.45

*Data does not reflect the 5v3 situations

As you can see, the team played much better in the two starts by Martin Biron than in games started by Henrik Lundqvist. The other thing that really stands out is the difference in the special team units. On the power play, the team has generated just a hair over 1 chances per 2 minutes (1.14 to be exact), while the penalty kill has allowed nearly 1.4 chances per 2 minutes (1.37 to be exact). For the long term, both of those numbers will need to improve if the Rangers have plans to make the playoffs.

Another thing we should look at is the comparison between the scoring chances and the overall shot rates, both Fenwick (without blocked shots) and Corsi (with blocked shots).  This comparison is for 5v5 play only.

GF SCF FenwickF CorsiF EV Time GF/SC GF/Fen GF/Corsi
Lundqvist 15 105 246 333 363.73 14.29% 6.10% 4.50%
Biron 5 32 76 104 100.95 15.63% 6.58% 4.81%
Total 20 137 322 437 464.68 14.60% 6.21% 4.58%
GA SCA FenwickA CorsiA EV Time GA/SC GA/Fen GA/Corsi
Lundqvist 12 110 281 384 363.73 10.91% 4.27% 3.13%
Biron 5 23 52 87 100.95 21.74% 9.62% 5.75%
Total 17 133 333 471 464.68 12.78% 5.11% 3.61%

*The Rangers were an abysmal -22 Fenwick and -44 Corsi in game 10, driving them from positive to negative overall

What you see here is the rates at which goals are scored based on various shots.  So the Rangers are scoring goals on 14.6% of their scoring chances, 6.21% of all shots that make it through to the goalie, and 4.58% on all shots they take.  You can also use this data to figure out how often they have shots blocked (Fenwick divided by Corsi) or the percentage of shots taken from the defined area (SC/Fenwick).

Early on, we can see our all-star goaltender has had a tremendous start to the season, ‘stopping’ more than 95% of all shots that get through (including the missed nets). OTOH, the performance against the Atlanta Thrashers has bloated Biron’s numbers to levels that for a season would be unacceptable.

After the jump, the meat of the data, looking at the skaters.

We’ll start with the forwards:

TOI SCF/15 SCA/15 DiFF FenF/15 FenA/15 Diff CorF/15 CorA/15 Diff SC/Fen GF/SC GF/Fen GF/Corsi
Dubinsky 151.13 4.17 4.17 0.00 9.33 9.83 -0.50 12.90 12.51 0.40 44.68% 16.67% 7.45% 5.38%
Callahan 147.18 4.59 3.67 0.92 9.89 9.38 0.51 13.55 12.03 1.53 46.39% 17.78% 8.25% 6.02%
Frolov 135.05 4.11 4.55 -0.44 10.44 9.55 0.89 14.88 14.33 0.56 39.36% 8.11% 3.19% 2.24%
Avery 134.85 4.89 4.78 0.11 11.90 12.12 -0.22 15.35 17.24 -1.89 41.12% 22.73% 9.35% 7.25%
Anisimov 131.68 4.78 4.10 0.68 10.59 9.11 1.48 14.01 11.62 2.39 45.16% 14.29% 6.45% 4.88%
Fedotenko 131.40 4.22 4.00 0.23 11.76 10.84 0.91 14.84 16.78 -1.94 35.92% 24.32% 8.74% 6.92%
Stepan 125.33 3.95 2.87 1.08 11.37 9.22 2.15 15.32 14.12 1.20 34.74% 21.21% 7.37% 5.47%
Christensen 110.32 3.81 3.94 -0.14 8.43 10.88 -2.45 11.69 15.64 -3.94 45.16% 7.14% 3.23% 2.33%
Prust 108.65 5.66 4.97 0.69 11.18 11.73 -0.55 16.43 16.43 0.00 50.62% 4.88% 2.47% 1.68%
Boyle 96.47 4.66 5.29 -0.62 11.04 13.06 -2.02 15.55 19.75 -4.20 42.25% 16.67% 7.04% 5.00%
Gaborik 37.38 3.61 6.02 -2.41 8.83 12.44 -3.61 12.84 15.65 -2.81 40.91% 44.44% 18.18% 12.50%
Boogaard 34.85 3.87 3.44 0.43 9.04 12.05 -3.01 12.05 20.23 -8.18 42.86% 11.11% 4.76% 3.57%
White 33.53 3.58 4.47 -0.89 8.95 12.08 -3.13 11.63 17.00 -5.37 40.00% 12.50% 5.00% 3.85%
Grachev 14.93 4.02 2.01 2.01 10.04 10.04 0.00 12.05 13.06 -1.00 40.00% 0.00% 0.00% 0.00%
Drury 7.27 2.06 10.32 -8.26 2.06 24.77 -22.71 6.19 24.77 -18.58 100.00% 0.00% 0.00% 0.00%
Williams 3.72 0.00 16.14 -16.14 4.04 32.29 -28.25 12.11 48.43 -36.32 0.00% #DIV/0! 0.00% 0.00%

*Table is sortable by clicking the column headers

There is alot of information there, so lets tackle a few things.  The first thing we’ll note is that while I have listed the various shot rates against, when looking at the forwards, concentrating on the shots generated would be more important when trying to evaluate their effectiveness.  Although not listed here, one should remember that Corsi is influenced by the starting zone position.  

For starters, we can see why Tortorella has frowned often about Erik Christensen, and recently Derek Stepan as well. The two are expected to be playmaking centers, yet they have been on the ice for the fewest chances of any of the regular forwards. Christensen has been especially note worthy, as not only is he not helping to generate chances on par with the rest of the team, but all of his shot metrics are in the negative. If his play doesn’t pick up, he’ll be one of the first targets for exile when Chris Drury or Vinny Prospal returns.

There are two other players who should certainly be focused on here. The first is Brandon Prust. While the simple boxscore numbers show Prust has only one point thus far, it is certainly not for a lack of opportunities. When he is on the ice, the team is generating more shots and more scoring chances than any other Rangers forward. Yet, the team is only scoring a goal once in every 20.49 opportunities. Much like Ryan Callahan‘s early underlying numbers reliably predicted the flood gates would open for him, the same should be true of Prust’s line.

That leads me to Prust’s common linemate, Alex Frolov. I touched on Frolov’s ineffectiveness when I summarized the individual chances after the first seven games. While individually that continues to hold true, we do see that the underlying shot rates are not all that different from Brandon Dubinsky‘s, and in some cases actually better. He too should see a bump in scoring as some of the team’s chances start finding the back of the net.

Now on to the defense:

TOI SCF/20 SCA/20 DiFF FenF/20 FenA/20 Diff CorF/20 CorA/20 Diff SCA/Fen GA/SC GA/Fen GA/Corsi
Staal 184.05 6.95 5.22 1.74 12.82 14.67 -1.85 17.17 19.13 -1.96 35.56% 14.58% 5.19% 3.98%
Rozsival 179.08 6.03 5.81 0.22 12.06 14.41 -2.35 16.98 19.21 -2.23 40.31% 15.38% 6.20% 4.65%
Girardi 174.40 6.19 6.88 -0.69 14.45 14.68 -0.23 19.95 20.87 -0.92 46.88% 11.67% 5.47% 3.85%
Del Zotto 173.20 5.43 6.93 -1.50 14.78 13.97 0.81 21.13 20.44 0.69 49.59% 13.33% 6.61% 4.52%
Eminger 94.58 4.44 4.65 -0.21 13.74 13.74 0.00 16.92 20.93 -4.02 33.85% 13.64% 4.62% 3.03%
Gilroy 92.82 4.09 4.31 -0.22 15.08 12.93 2.15 19.39 19.61 -0.22 33.33% 20.00% 6.67% 4.40%
Sauer 41.67 7.68 2.40 5.28 16.80 14.40 2.40 22.56 22.56 0.00 16.67% 0.00% 0.00% 0.00%

Once again, the numbers here have been adjusted to 20 minutes, rather than the 15 minutes for forwards, which is why the rates are noticeably higher for the defense.  Also, while their offense matters, for the percentages, this time I am concentrating on the chances against, which are the responsibility of the defense to prevent.  It makes sense to divide the defense into two groups, the top four defenders and the 3rd pair carousel.  This is because the top four have spent a majority of their time against top six forwards, while the bottom pair has spent a higher majority against bottom six forwards.

Within the first group, once accounting for ZoneStart (not shown), the four have Corsi ratings which are roughly equivalent. Even without that adjustment, we do see the significance that blocked shots can have. Despite allowing almost about 1.5 shots more against while they are on the ice, Dan Girardi and Michael Del Zotto have nearly identical Fenwick rates to their counterparts. The difference between the two pairs comes down to the quality approximated by the scoring chance data.

Girardi and Del Zotto have allowed 46-50% of the shots that get through to come from dangerous scoring areas, while Marc Staal and Michal Rozsival have been more adept at keeping shots to the outside. This being the case, you would expect that they would be the pair with the better goals against numbers. Observation becomes the key difference at this stage. From a strictly numbers basis, it would look like mathematical luck has hurt the pair. Anyone who has watched the team knows that Staal especially has been responsbile for some critical mistakes that have led directly to goals against. This is not to say that Girardi and Del Zotto have not been victimized as well, but not nearly to the same extent. All things considered, limiting the mistakes should eventually put Staal back on top of the defensive food chain, but for now, that place is reserved for Girardi, whose numbers are good, but intangilbles have been better. What ultimately matters, however, is that all continue to improve and limit shots closer to the rates the bottom pair guys have.

With our 3rd pairing, we see pretty clearly that Michael Sauer has been the best of the three defensively. The team sees both the fewest chances against, and the most chances for while he has been on the ice. Small sample comes into play here. With only ~42 minutes of time, there are some clear anomalies, most notably the zero goals against thus far. Overall, he is still the only positive Corsi player among the three, so unless he is truly banged up, there should be no reason he continues to get waiter service during games. Between Steve Eminger and Matt Gilroy, advantage certainly goes to Gilroy. Keeping in mind again that their assignments are mostly limited to bottom six forwards, Eminger’s corsi rate is simply unacceptable, no matter how physical he might be playing.

So there you have the sum total of data for October.  Keep in mind, that with 10 games of data, a lot can and will change as the year progresses.   If you have any questions regarding what is here, or what may have been overlooked, go ahead and post them in the comments.

Special thanks again to Vic Ferrari and timeonice.com for the ability to produce the data, and behindthenet.ca for the GF/GA and zonestart data, even if I didn’t publish any of it.

Talking Points