I put together a series of simulations to see what spread of shooting production we might expect to see from average players and how the variability in that production compares to what we might expect from Kovi. In looking at the data over the last few years, it appears roughly 500 players have had greater than 20 even strength shots each season, with the maximum shots around 200. I used these as the basis for my models. I'm using a coin-flipping model, with every one of the 500 players other than Kovi having a known shooting percentage of 8.0%

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I opened another can of worms.

In an earlier post, Scott Reynolds pointed out the discrepancy between on ice save percentage and off ice save percentage for Smid, Fistric, Weaver, and Giordano. So I computed it, and sure enough, Smid's on ice save percentage falls outside the 95% CI established by his off ice save percentage. Same for Weaver. Same for Fistric. Same for Giordano. Uh-oh!

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I think it makes sense to incorporate QualComp and QualTeam into DGVA.  It just doesn't make much difference.

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Having thoroughly messed up goaltender performance metrics, I lay awake a while last night pondering what to do next. I thought, "I'm a contrarian. Nobody looks at defensive contribution. Let's look at that."

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Replacement goaltenders are not the same as threshold goaltenders.

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For goaltenders, GGVT is the same as GVA.

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In a previous post, I looked at simulating GVA.  Here I simulate goaltender performance and calculate the goaltender portion of GVT.

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Turns out it's harder than you think.

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Sunny Mehta recently suggested that the observed range of goaltender save percentages over the course of a single season could essentially be duplicated by an average goalie and random factors.  It dawned on me that, if this were true, that observed goaltender performance metrics ought to similarly be indistinguishable from metrics derived from an average goaltender plus random factors.  To explore this, I ran some simulations using a pseudo-random number generator.  For these simulations I used an  ES save percentage of 0.917, PK save percentage of 0.868, and PP save percentage of 0.914

The range of GVA seen in a single season can be duplicated by an average goaltender and random factors.  GVA is a fairly imprecise estimate, with a large standard error and a large confidence interval.

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The final chapter.

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I expanded my look at save percentages to power play data. Once again I calculated logits from the save percentages. I removed goalies with logits that were infinity or undefined. Power play save percentage looks a lot like the even strength data. Goalies differ, teams don't. Only here, some goalies are better than average.

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I expanded my look at save percentages to penalty kill data. Once again I calculated logits from the save percentages. I removed goalies with logits that were infinity or undefined. Compared to even strength, the penalty kill data is a real muddle. The best model explains only about 13% of the variability seen. About 87% of the variability remains unexplained.

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The Big TOE. The big fella, the Lizard King. Godzilla.

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I ran some simulations to see if rink bias really matters. It does not seem to make much difference.

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I used my Even Strength Save Data to look at the effect of Teams. There is not much there.

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I reran my Even Strength Save Data using logistic regression. The results again show a significant difference between goalies.

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Using a psuedo-random number generator, I simulated up to 5 seasons of goalie save data.  Results after the jump.

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Goalie differences

Do goalies differ? As a fan of the Quebec Nordiques, my answer is an emphatic "Yes!" I would bet fans of the pre-lockout Ottawa Senators would wholeheartedly agree.

Not everybody does. Sunny Mehta recently said "No one has conclusively shown a meaningful difference in skill between NHL goaltenders. I'm not saying it doesn't exist, I'm just saying no one's really proved it, and that all signs point to goaltending differences being far less important than everyone thinks."

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I've looked at even strength save percentage for all goalies from 1997-2010. The data for 1997-1998 for special teams on the NHL.com web site has been fixed and I am now including it. In part 1 I looked at goalie age and even strength save percentage. In part 2, I look at the trend of even strength save percentage over time.

Unweighted

A first look at the relationship comes from unweighted data. Each goalie is one data point, whether he played 1 game or 80. Here is the scatter plot:

via 2.bp.blogspot.com

Model Summary

ANOVA

Coefficients

Not a lot there.

Weighted

We can rerun the regression, using shots faced as a weighting factor. The scatter plot of the means does look promising:

via 2.bp.blogspot.com

And there is a significant relationship.

Model Summary

ANOVA

Coefficients

So not a huge effect from year to year, but over the 12 years the predicted average even strength save percentage does go from 0.914 to 0.919. As an aside, the 1997-1998 data put this over the hump in terms of statistical significance. Looking at the trend in overall save percentage, I suspect that, if we go back further, the trend continues. If anyone has the data from the years before 1997 and would like to make it available to me I would be deeply appreciative. If not, I can see if I can construct it myself.

Conclusion

There is a statistically significant relationship between year and even strength save percentage.

The following goalies made their debut at 19 or 20, and had at least 3 years of data. The first four made their debut at 19, the remainder at 20.

Backstrom

Dipietro

Fleury

Ouellet

Auld

Emery

Esche

Fiset

Labarbara

Lehtonen

Luongo

Pavelec

Pelletier

Price

Ramo

Rask

Raycroft

Schwarz

T-TEST

I f we include goalies who made their debut at 21, we add in:

Anderson

Aubin

Biron

Bryzgalov

Crawford

Denis

Finley

Giguere

Halak

Harding

Howard

Johnson

Leclaire

Leighton

Noronen

Quick

Toivonen

Ward

T-TEST

So neither group achieves statistical significance.

I've looked at even strength save percentage for all goalies from 1998-2010. The data for 1997-1998 for special teams on the NHL.com web site is flawed, so I have omitted it.

Unweighted

A first look at the relationship comes from unweighted data. Each goalie is one data point, whether he played 1 game or 80. Here is the scatter plot:

via 2.bp.blogspot.com

So, the slope of save percentage over age isn't just close to 0.0, it is 0.0.

Weighted

We can rerun the regression, using shots faced as a weighting factor. The scatter plot does not look any more promising:

via 2.bp.blogspot.com

And, indeed, it is not any better.

Serial Measures

One last way to look for a relationship would be to look at each goalie relative to himself as he ages.  For this analysis, I have restricted the analysis to goalies with at least 3 seasons.  For each goalie, I computed his personal slope.  We can then look at the average of these slopes.  The null hypothesis is that the average slope is 0.0.

DISTRIBUTION PARAMETER ESTIMATES

========================================================

Slope (N = 140) Mean = -0.001 Variance = 0.000 Std.Dev. = 0.011

0.950 Confidence Interval for mean : -0.003 to 0.001

Once again, the slope is not significantly different from 0.0.

Conclusion

Analyzing the data every way I can think up, there is no evidence whatsoever of any relationship between goalie age and even strength save percentage.