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How Do Zone Starts Affect Possession?

Even while the team struggles Subban has been a possession monster. (Photo by Claus Andersen/Getty Images)

For awhile now I've been wondering how much of a disadvantage players are at when they start their shifts predominantly outside the offensive zone. In order to create a better sample and use as many players as possible, I determined that I would have to use Relative Corsi as my possession metric in order to account for different team dynamics.

For the purposes of this exploration I ignored quality of competition, mostly because I didn't want to create a convoluted calculation. I'm not sure what conclusions can be drawn from the following information but I found it extremely interesting.

Star-divide

What I wanted to look for is how big of a change would be applied to a Relative Corsi score for each percentage on either side of 50% of the time a player started in the offensive zone on average. This way I could take that average and see how much different a player's Relative Corsi score is compared to what would be expected from an average player with the same offensive zone starts percentage.

For the purposes of better sample size I used data from Behind The Net from last season, only using players who had played 40 or more games. This yielded 566 players to draw data from. In order to avoid negative numbers, I took the absolute value of every player's Relative Corsi, as well as the absolute value of every player's offensive zone starts that differed from 50 [ABS(OZS-50)]. From there I divided the resulting Relative Corsi number for each player by the resulting deviation from 50% offensive zone starts for each player.

What I was left with was a large section of usable data, and some outliers. Any player who started less than 50+/-1% outside of the offensive zone gave back an outlier number. This resulted in an elimination of ~120 players.

With outliers removed I took the average, and what came back was that for each percentage point plus or minus 50% offensive zone starts, an average NHL player's Relative Corsi should increase or decrease by 2.3.

With this number, we can see how a player is performing in terms of relative possession while factoring in zone starts. As an exercise, i took the numbers for last year's Montreal Canadiens squad and weighed their expected Relative Corsi against what they actually produced. Here's what I got:

Click to zoom

Habs_2010-11_possession_medium

What we can see is a few things that are pretty intuitive, and a few surprises. P.K. Subban and Brian Gionta are absolute monsters by this hypothetical measure. Both players performed way above what would be expected of them with low offensive zone starts. Max Pacioretty was sheltered but still over-performed expectations by a large degree.

Others who performed better than what would be expected include Alexandre Picard, Benoit Pouliot, David Desharnais, James Wisniewski, Jeff Halpern, Hal Gill and Ryan White. Since this analysis doesn't account for quality of competition or quality of teammates, these guys make sense even if you don't think they would would be dominant intuitively. Picard was carried by Subban for 30+ games while Gill was carried by Subban the last half of the season. Similar caveats for Desharnais and White in that they played against weak competition.

The big surprises gleaned were a Corsi monster like Scott Gomez not performing to expectation, Travis Moen looking abhorrent, Josh Gorges being worse than expected and the entire top line of Mike Cammalleri, Tomas Plekanec and Andrei Kostitsyn being underwater. This metric also shows Roman Hamrlik may have been carried by Wisniewski and not the other way around as many believed, although it's not refined enough to make a conclusion.

With curiosity piqued, I figured I'd look at how this year's team is performing:

Habs_2011-12_possession_medium

This time the strongest player on the team is Max Pacioretty, going from his sheltered role last year to taking on more tough competition. Other really strong players include Palushaj, Subban again (although less so as he's facing tougher competition), Alexei Emelin, Gionta, Plekanec and Erik Cole.

Other players playing better than average include Josh Gorges, Lars Eller, Mike Blunden, Petteri Nokelainen, Raphael Diaz, Rene Bourque, and Travis Moen.

Again Gomez is way below what you'd expect him to produce, which may be a sign of decline that we've been missing at EOTP. Chris Campoli, Kostitsyn, Desharnais, Gill, Louis Leblanc, Mathieu Darche, Tomas Kaberle and Yannick Weber are all on the negative side here.

As I said before, I don't know if any conclusions can be drawn from this, and I'm going to be refining everything a bit more over the next while, however the results seem to be intuitive of what we see on the ice. Hope it was an interesting new perspective.

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You may be interested in some of the work I did on zone starts. Here and here.

In general, zone starts affects corsi more significantly than goals but when considering corsi as a percentage the effects are still relatively minor for the majority of players.

You can pretty much eliminate all zone start effects by ignoring the first 10 seconds after an offensive or defensive zone start. Also, shots taken during these first 10 seconds are of very low quality. Shooting percentages in those first 10 seconds are about 3% while they are closer to 9% at all other times (in 5v5 even strength situations).

Scott Gomez is an example of where Corsi analysis does not work well. He has never been good on capitalizing on his good corsi numbers as he has generally always posted a very poor on-ice shooting percentage. Take a look at this Gomez vs Cammalleri comparison I did to see what I mean.

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by HockeyAnalysis on Feb 13, 2012 8:10 AM EST reply actions  

If you’re going to try to use a sample of two players and a span of two seasons to try to make that demonstration, then Gomez is a terrible example because of the outlier nature of his on-ice shooting percentage in 2009-2010.

Thanks to Olivier, we have scoring chance data on both players, and the Habs had about as many scoring chances with Gomez on the ice as they did with Cammalleri when 5-on-5. Both had conversion drops last year, but Gomez’s was considerably deeper than Cammy’s.

by MathMan on Feb 13, 2012 8:44 AM EST up reply actions  

I am not looking at just 2 years. Gomez has had below average on-ice shooting percentages for (at least) 5 straight seasons. Cammalleri has posted on-ice shooting percentages above Gomez for (at least) 5 straight seasons.

Of the 210 forwards with 2500 minutes of 5v5 faceoff adjusted ice time over the previous 4 seasons (2007-08 to 2010-11) Gomez ranks 203rd in on-ice shooting percentage, Cammalleri is at 101. Gomez’s shooting percentage is 7.06% compared to Cammalleri’s 9.48%.

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by HockeyAnalysis on Feb 13, 2012 9:01 AM EST up reply actions  

Sure but the point is, Gomez’s superior puck possession generally overwhelms Cammy’s superior shooting percentage — it did not in 2010-2011 because Gomez’s on-ice shooting percentage was such a low outlier.

I don’t think you need to prove that players can have an effect on on-ice shooting percentage, I think that’s well-established (just look at the on-ice shooting percentage of NHL goons!), but the difference in this ability between Cammy and Gomez isn’t nearly what the last two seasons will make it look. The majority of it will be luck, as should be expected given the high volatility of shooting percentages.

by MathMan on Feb 13, 2012 10:36 AM EST up reply actions  

Sure but the point is, Gomez’s superior puck possession generally overwhelms Cammy’s superior shooting percentage

But it doesn’t. Gomez has never scored at a higher rate than Cammalleri in any of the previous 4 seasons and the previous 2 seasons Cammalleri has had a better 5v5 zone-start adjusted goals for %.

2009-11 (2yr): Cammalleri 53.6%, Gomez: 48.6%
2010-11: Cammalleri 50.9% Gomez 43.8%
2009-10: Cammalleri 55.6% Gomez 52.4%

As for persistence vs luck, I have shown that goals for rates are as good as corsi for rates in predicting future goal production with just one year of data and anything beyond one season the edge is clearly on the side of goal rates. The fact that Gomez can’t produce goals at a higher rate than Cammalleri for 4 straight seasons is not something we can attribute to luck alone.

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by HockeyAnalysis on Feb 13, 2012 11:03 AM EST up reply actions  

I’m curious to see whether we can really deduce a statistically significant difference on a 4-season, two-player sample.

Also, are we talking about scoring or outscoring? We seem to be jumping from one to the other.

by MathMan on Feb 13, 2012 11:46 AM EST up reply actions  

Over the course of the 2007-08 and 2008-09 seasons Gomez and Cammalleri posted the following zone adjusted 5v5 on ice stats:

Cammalleri: 74 goals for, 816 shots for, 9.07 SH%
Gomez: 72 goals for, 1079 shots for, 6.67 SH%

The chance that Cammalleri would score 74 goals on 816 shots if his natural on-ice shooting percentage was Gomez’s 6.67% is just 0.4%.

The chance that Gomez would score just 72 goals on 1079 shots if his natural on-ice shooting percentage was Cammalleri’s 9.07% is just 0.3%.

Combined the two of them had a shooting percentage of 7.70%. The chance of Cammalleri scoring 74 goals with a natural shooting percentage of 7.7% is 6.62%. The chance of Gomez scoring just 72 goals 11.1%. The chance that both of those things happen (7.7% * 11.1%) is just 0.7%. Statistically significant? Absolutely. With just 2 years of data.

(Notes: I used straight “coin flipping” binomial distribution to calculate probabilities and when I say “chance of Gomez scoring 72 goals” I really mean chance of Gomez having 72 goals scored when he is on the ice. Same for Cammalleri.)

Generally I have talked about scoring than out scoring because it is really really difficult to isolate players defensive ability from the goaltender. I did it above for Cammalleri and Gomez because they played on the same team in front of the same goalies for those two seasons.

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by HockeyAnalysis on Feb 13, 2012 12:08 PM EST up reply actions  

That’s not what I meant (and my statistics-and-probabilities course is a lot rusty, but I don’t think your response actually answers the question in a valid manner). What are the confidence interval for Cammy and Gomez’s shooting percentages given these samples, and can we use them to disprove that either of them is a 7.7% true talent?

My initial puttering around with this gave me a qualified “no”, but let me get home and refresh my memory more on the subject so I can give actual data (unless you have it).

Incidentally, if you believe that goaltending talent makes a player’s impact on on-ice save percentage difficult to isolate, I wonder: would it not be similarly difficult to isolate players’ impact on on-ice shooting percentage based on their linemates’ shooting talent?

by MathMan on Feb 13, 2012 1:08 PM EST up reply actions  

Well, if there is a 0.4% chance that Cammalleri will have 74 goals scored on 816 shots if his natural on-ice shooting percentage is 6.67% then we can also say with 99.6% confidence that his on-ice shooting percentage is something above 6.67%. That will pass even the most stringent confidence tests. We can also say with 93.38% confidence that his natural on-ice shooting percentage is above 7.7% and we can say with 88.9% confidence that Gomez’s is below 7.7%.

Incidentally, if you believe that goaltending talent makes a player’s impact on on-ice save percentage difficult to isolate, I wonder: would it not be similarly difficult to isolate players’ impact on on-ice shooting percentage based on their linemates’ shooting talent?

In theory yes, in reality no. If Gomez played all his ice time against Tim Thomas and Cammalleri played all his ice time against Steve Mason you would have a valid claim. But players don’t play against one or two goalies like they play in front of one or two goalies. They all play against a wide array of goalies so things mostly balance out. There may be slight quality of opposition goalie differences across players but it would have marginal impact on a players overall stats. In general, quality of competition factors are largely over rated.

You can even go beyond that. In general quality of competition effects are over stated.

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by HockeyAnalysis on Feb 13, 2012 2:45 PM EST up reply actions  

Well, if there is a 0.4% chance that Cammalleri will have 74 goals scored on 816 shots if his natural on-ice shooting percentage is 6.67% then we can also say with 99.6% confidence that his on-ice shooting percentage is something above 6.67%

Oh, I get it. You gave the probability that Cammalleri will score 74 goals or more if his true talent shooting percentage was 6.67% — needless to say that makes a great deal more sense.

You don’t quite have 95% confidence on the 7.7% but I imagine you would if you added another season.

In theory yes, in reality no. If Gomez played all his ice time against Tim Thomas and Cammalleri played all his ice time against Steve Mason you would have a valid claim.

I don’t mean in terms of opposing goaltending, I mean in terms of playing with Andrei Kostitsyn as opposed to playing with Travis Moen. So that’s “quality of teammates”, as is the case when talking defense vs. goaltending.

by MathMan on Feb 13, 2012 3:44 PM EST up reply actions  

95% confidence is at about 7.57%.

Yes, of course. Quality of teammates matters. Can all the blame be put on Gomez for that low on-ice shooting percentage? Not necessarily, but let’s look at Gomez’s top 5 line mates over those 2 seasons including the ice time they played together and their on-ice shooting percentage (overall, not while playing with Gomez).

Gionta, 1357:56, 7.91%
Pouliot, 471:59, 8.87%
Cammalleri, 445:14, 9.07%
Moen, 386:01, 7.29%
Pacioretty: 330:18, 6.33%

Only Pacioretty has a worst on-ice shooting percentage than Gomez’s 6.67% so I think it is safe to assume that Gomez himself plays the most significant factor in his own weak on-ice shooting percentage.

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by HockeyAnalysis on Feb 13, 2012 4:06 PM EST up reply actions  

I’m guessing we’re back to talking about 2009-2010 and 2010-2011, as opposed to 07-08/08-09 from 6 posts earlier?

Hmm. I thought Moen was a low outlier for on-ice shooting percentage. I’m looking at BehindTheNet, and not once has Moen cracked 7% on-ice sh% before this season. Likewise Pouliot’s best season is listed as 8.71 in 09-10, except for the 11 games he played in 07-08.

I can’t reach stats.hockeyanalysis.com (DNS error). Where does the discrepancy come from?

by MathMan on Feb 13, 2012 4:27 PM EST up reply actions  

Sorry, that 2007-08/2008-09 from 6 posts earlier should read 2009-10 and 2010-11. I have mostly been talking about the past 2 seasons.

Moen is an outlier but his worst seasons were 2007-08 and 2008-09. He was better than Gomez the past 2 seasons, and so far this year too.

The difference between Behind The Net and the numbers I quoted above are that I account for zone starts by not considering the first 10 seconds after an offensive or defensive zone start.

Ughhh. More DNS problems. Not sure what is going on there. I’ll look into it.

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by HockeyAnalysis on Feb 13, 2012 4:45 PM EST up reply actions  

You can try hockeyanalysis.com/HockeyStats instead of stats.hockeyanalysis.com. That might/should work.

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by HockeyAnalysis on Feb 13, 2012 4:47 PM EST up reply actions  

Thanks, I can reach it from home anyway. I like the navigation options.

Interesting — that 2010-2011 year where Gomez combined ludicrous shot generation with equally ludicrous low on-ice shooting percentage is really killing his shooting percentage over that 2-season span, so that even his 2009-2010 season can’t pull him above Travis Moen.

by MathMan on Feb 13, 2012 5:56 PM EST up reply actions  

Waaaaay late to the party, but maybe somebody still reads this?

A couple of things:

- I know nothing about binomial distributions. Nothing. One would walk up to me and round kick me in the face and then the police officer would ask me “Sir, were you kicked in the face by a binomial distribution?”, my answer would be “The hell I know man, I mean what am I to you, a martial arts expert?”

- Through this thick smog of ignorance of mine, I still hold a conviction: you base something on shooting percentage, you are on thin ice.

- Another thing: maybe it’s because I see binomial distribution as chuck norris type entities, but I remain skeptic not of the existence of such a thing as “shot quality”, but rather of the possibility of measuring it over a span of two (or four) seasons with shots and goals.

- Gomez had the following numbers over the last two seasons:

09/10: 17,9 scoring chances per 60 minutes, 55% zone start, 14,1% conversion rate
10/11: 18,4/60, 54% ZS, 8,7% chances converted into goals

Plekanec:

09/10: 15,5/60 43,7% 14%
10/11: 20,5/60 51,6% 13%

Cammalleri:
09/10: 17/60 47,3% 16,1%
10/11: 19,4/20, 52,2%, 11%

- Your assertions about zonestarts and the possible over-reliance on quality of competition are interesting.

by Olivier on Feb 15, 2012 12:54 AM EST up reply actions  

Those numbers are 5v5 thus excluding empty net situations, of course.

I don’t have the splits at hand, but Gomez’s numbers falls sharply compared to Plekanec’s when you look at score-tied data only.

by Olivier on Feb 15, 2012 12:56 AM EST up reply actions  

Thanks for the links, I’ll read those today.

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by Andrew Berkshire on Feb 13, 2012 10:52 AM EST up reply actions  

Andrew, did you want a calculation that results in a linear relationship? The calculation you’ve used gives more weights to those players that are close to 50% because the absolute value of their denominator is so small.

Consider adding the absolute value of the lowest value in each of Corsi Rel and Zonestart before dividing to figure out the “corsi rel” value of 1% of zonestart. That’ll give you something linear that you can use the average of and use all your data points in. Right now I suspect your method overvalues the difference 1% of zonestart makes because of the small denominators.

That is, of course, assuming the relationship between zonestart and corsi rel is linear.

by MathMan on Feb 13, 2012 8:20 AM EST reply actions  

What I ended up doing was eliminating all players who ended up giving me an overvalued number. Like Gionta for example who started 50.1% in the offensive zone so it was dividing by .1 and not giving me a reliable number.

There’s definitely some major holes in trying to find any concrete relationship here, but mostly I’m just playing around.

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by Andrew Berkshire on Feb 13, 2012 10:56 AM EST up reply actions  

Yeah, that’s sort of my point. If you set the zero baseline at, say, 25% zonestart (or even 0) rather than 50%, and set the baseline for Corsi Rel at -30 or whatever your lower value was rather than 0, you could avoid dealing with small denominators and have a linear relationship.

Right now, you’re giving more weight to differences in Corsi Rel when zonestart approaches 50%, and comparatively little weight to the Corsi Rels of people with extreme zone starts. Your calculation to figure the value of a zone start is effectively expecting a guy with 60% zone start to have a Corsi Rel 10 times as large as someone who has 51%.

I think your methodology overstates the importance of zonestarts, which makes Gomez looks worse than he is and Blunden look better.

by MathMan on Feb 13, 2012 11:12 AM EST up reply actions  

What % of shifts start with faceoffs anyways? And then what % (total) are from O-Zone or D-Zone Faceoffs? I think ZS% is important to see how a player is generally used, but I think its overall effect, as HockeyAnalysis states above, isn’t all that great.

I think you also have to weigh how much a player gets sent out for a faceoff situation vs. his total shifts as part of the equation, too. For some, it might be higher and lead to greater overall Corsi/Fenwick variation than another player would.

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by Bruce Peter on Feb 13, 2012 11:17 AM EST up reply actions  

Hmm. Most non-faceoff shift changes occur either on the rush or when the puck is deep in one zone and not moving, so effectively situations that are neutral on the Corsi ledger.

by MathMan on Feb 13, 2012 11:22 AM EST up reply actions  

Yes, but there is a difference between these three guys who both have 55% OZone%.

55 OZone, 45DZone, 300 NZone
110 OZone, 90DZone, 200 NZone
165 OZone, 135 DZone, 100NZone

NZone is neutral zone face offs and line changes. The first guy has a +10 Ozone starts vs DZone starts and the second guy has a +20 and the third guy +30. All have had the same number of shift starts.

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by HockeyAnalysis on Feb 13, 2012 11:30 AM EST up reply actions  

Well yes — for adjusting for zone starts it probably makes more sense to adjust for total Ozone/Dzone faceoffs rather than try to finangle with the ratio (say each Ozone faceoff is the equivalent of +X corsi rel and each defensive zone faceoff is worth -X).

by MathMan on Feb 13, 2012 11:42 AM EST up reply actions  

Of course, in theory, we should be taking into account faceoff wins/losses as well. If you lose all the faceoffs in the offensive zone and win them all in the defensive zone, zone start differences are probably negligible. Thus looking at OZone faceoff wins vs DZone faceoff losses is likely what matters most.

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by HockeyAnalysis on Feb 13, 2012 12:27 PM EST up reply actions  

I’m not so sure about that. Faceoffs are inherently overvalued in hockey. If a team is poor at faceoffs but excellent at puck recovery, for example Montreal (especially with Pacioretty and Cole on the ice), then faceoffs end up being almost a non-factor.

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by Andrew Berkshire on Feb 13, 2012 12:39 PM EST up reply actions  

Good points. I could play around with that for sure.

The problem lies in the one major assumption that I’ve made, that 50% offensive zone starts would result in a 0.0 Rel Corsi. Could I still make that assumption with your calculation? I’m not sure since it’s using a different baseline.

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by Andrew Berkshire on Feb 13, 2012 11:35 AM EST up reply actions  

Strictly speaking, I don’t think you can make that assumption at all because it implies that the team as a whole has 50% zonestart, which isn’t the case.

But the point is, you’re just adding a constant to your calculations. If your lowest Corsi Rel value is -30, then your assumption would be that 50% zone start = +30 adjusted corsi rel = 0.0 real corsi rel.

by MathMan on Feb 13, 2012 11:40 AM EST up reply actions  

I see what you’re saying.

As for assuming 50% zone starts, I actually didn’t assume that, I just took the average. League average zone starts from the sample of 566 players I used was 50.09%. Habs was 50.56% Not far off.

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by Andrew Berkshire on Feb 13, 2012 11:44 AM EST up reply actions  

As per Gomez’s decline, I don’t think any of us did not notice that he’s more suitable to 2nd line toughs than 1st line toughs like he had when he first arrived. I guess maybe we’ve underestimated the extent of his decline? Because I’m pretty sure we’ve noted he isn’t as good in his 30s as he was in his 20s.

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by Bruce Peter on Feb 13, 2012 9:45 AM EST reply actions  

Personally, I keep puzzling why a guy who’s #1game is heavily skewed towards carrying the puck through the neutral zone has consistently been given such a high offensive zone start. Why not start him in the D zone and have him haul the puck up, since he’s pretty much what he does best?

by MathMan on Feb 13, 2012 10:48 AM EST up reply actions  

That’s a good point. I figured this year that they were trying to get him going offensively, but I have no idea why he had such high offensive zone starts last year.

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by Andrew Berkshire on Feb 13, 2012 10:58 AM EST up reply actions  

Mostly my point was that maybe he isn’t the Corsi beast that we all thought he was.

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by Andrew Berkshire on Feb 13, 2012 10:57 AM EST up reply actions  

I think this has legs, but like everything else, it present limits as to the conclusions that should be drawn.

I don’t need to point out that Corsi is not actually possession, it is just a proxy for possession, so two players with the same Corsi can actually look completely different to the eye. Zone Starts are interesting too, as they capture one moment in time, but do not account for all of a player’s time on the ice.

Anyway, it’s ineteresting stuff because of the way you use expected vs. actual. I think this is a very powerful way of doing anything, and it doesn’t matter much what the stat is. Expected goals, expected shooting percentage, save percentage. They are all interesting when compared to actual because of the questions they raise.

Here the questions seem to be new. Why does Gomez, who seems to take more than his share of attempts on net come out on the negative ledger? How does a player who holds the puck more fare? Why, as Mathman said, is Gomez not started in the defensive zone more? Is this different for centres (they actually take the faceoffs)?

I look forward to more examination.

by Topham on Feb 13, 2012 12:44 PM EST reply actions  

I think one of the big reasons why people don’t accept advanced stats in hockey is because they don’t realize that the author knows there are limitations to the applications. What I did here seems to be going over well because I’ve said from the outset that it’s an exercise without a real conclusion.

As with a lot of things it brings up more questions than it answers, but isn’t that more interesting in the end?

Thanks for the comments, Chris.

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by Andrew Berkshire on Feb 13, 2012 1:38 PM EST up reply actions  

Interesting stuff.

As it is with anything like this, proxies are employed and methodologies are developed to massage those proxies. The end result is not necessarily predictive, but it is an enhancement that can be layered on to subjective observation.

Predictive tools.

What does emerge fairly clearly are the outliers. It is no surprise the most blatant underperformer last season, Dusty Voyd, even if hampered by being placed in a largely defensive role, does not have an NHL job this year.

This season, you clearly see the contribution being made by Cole & MPac. And, yet, DD underperforms. One can only imagine what his numbers might look like if he wasn’t sheltered by the redwoods.

This is, however, how a coach has to balance the use of his assets. There might be a temptation to put Gionta between the two wingers – were he not injured – which might actually produce stellar results, but then the likely dropoff in DD’s production would very possibly more than offset the upside from such a move.

I’m sure it’s not lost on anyone that the strongest outperformer in the absolute is Blunden.

Blunden: Saved by the bell…curve.

by JD__ on Feb 13, 2012 1:39 PM EST reply actions  

I think Blunden looking like he’s over-performing is what gives most people pause.

What’s worrying to me look at this data set, is that Weber is in an even more sheltered role than he was last year, but performing worse by a degree I need to find a new word to describe. He hasn’t been as bad as Dustin Boyd was last year, but he’s been the worst overall performer on the team after being only slightly below what was expected last year. That’s a big step backwards.

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by Andrew Berkshire on Feb 13, 2012 1:45 PM EST up reply actions  

It might explain why his bro Dizzy has played more hockey this season.

by JD__ on Feb 13, 2012 1:51 PM EST reply actions  

A very good point.

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by Andrew Berkshire on Feb 13, 2012 2:25 PM EST up reply actions  

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