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Penalty Kill Save Percentage - Final Tally and Playoffs

With the regular season now over I decided to take one last look at penalty kill save percentage.  First off, I'll take a look at the numbers of the leaders from December to show the kind of regression to league average we expect with this statistic.  Then, I'll take a look at each of the goalies involved in the playoffs and their save percentage by game state (PK and EV) to see if there's anyone who had a PK save percentage that made them look great in the regular season but isn't such a good bet to repeat throughout the playoffs.

Star-divide

In the original study earlier this season I looked at goalies who had faced at least 60 shots and created two tables, one for goalies who had a PK save percentage above .900 and another for those with a PK save percentage below .840.  So we're looking at the guys who did the very best and the guys who did the very worst.  Historically, an average save percentage on the PK is about .866.  I like to calculate "goals saved above/below average" and the number I'm using for "average" is that .866 figure.  The two tables below show how the best and worst up to December 8th fared in the rest of the season: 

Playoff_pk_1_medium

Playoff_pk_2_medium

As we can see, in both instances there was a strong tendency to regress toward the average of .866.  Of the fourteen goalies in the study, only Vesa Toskala, Ray Emery and Jonas Hiller ended up further away from .866 in the second sample of games than they did in the first.  In Emery's case the second sample only had 33 shots so an extreme result can easily be discarded.  Toskala's number is amazing considering just how bad his first number was.  What a frustrating season for fans of the Toronto Maple Leafs.  In Jonas Hiller's case, he's further away from .866 but in the opposite direction.  His season started off very poorly on the PK and ended very well.  At any rate, I hope that this shows a couple of things.  First, a playoff series is going to be a very small sample so anything can happen! Second, goalies with an extreme result one way or another probably shouldn't be counted on to reproduce that number.  If we were taking bets on the PK save percentage for these 14 goalies for the last part of the season, league average (.866) was a better predictor of their performance than was the first part of the season in 9 out of the 14 cases!

So now let's look at the playoff goalies.  The goalies that have benefited from a high PK save percentage without the EV save percentage to back it up are highlighted in red.  The goalies with a reasonably strong EV save percentage but who have been hurt by a weak number on the PK are highlighted in green:

Playoff_pk_3_medium

Some interesting things to note include Nashville and Washington who each have both goalies highlighted in green.  This may imply that those teams aren't very good at controlling shot quality on the PK.  It may also signal that those teams have had very poor luck with their goaltending on the PK.  If it's the former, it's a somewhat new development.  Although both Nashville goalies had a poor PK save percentage last season, in 2007-08 Dan Ellis was among the league leaders with a PK save percentage of .918.  For Washington, Semyon Varlamov posted .886 last season if we combine his results in the regular season and playoffs.  If I were a fan of the Predators or Capitals, I would find some hope in the strong EV performance and realize that my goaltenders are probably better than their overall save percentages imply.

Another interesting case is the Philadelphia FlyersMichael Leighton benefited from some extremely good luck on the penalty kill while Brian Boucher was very poor.  At EV - a better predictor of future performance - Boucher is actually the better goalie.  Now Boucher still isn't very good but if Leighton were healthy the Flyers would likely be playing someone who is even worse. 

The goalies who are probably worse than the overall save percentage might imply?  In addition to Leighton we have Tim Thomas who isn't likely to play in the playoffs.  But even if he did, at least he has a nice track record of performance to suggest he's a pretty good goalie.  Brian Elliott does not.  The Penguins weren't able to get enough pucks past him last night but Ottawa is much weaker in goal than Elliott's .909 overall save percentage implies.  And finally, Mr. Vezina himself, Ryan Miller.  .928 at EV is still awesome.  He's definitely a good goalie but his numbers this season have been somewhat inflated.

Finally, Jonathan Quick - not as bad as you might think.  GOilerskings!

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If I were a fan of the Predators or Capitals, I would find some hope in the fact that they have strong goaltenders at EV who are probably better than their overall save percentage imply.

Now, why wouldn’t you say that about the Canucks? ;-)

by antro on Apr 15, 2010 1:25 PM MDT reply actions  

Alas, it’s true. Luongo’s had a poor season by his standards but a lot of that is poor performance on the PK that’s not likely to be a good reflection of his true talent.

by Scott Reynolds on Apr 15, 2010 3:59 PM MDT up reply actions  

Am I missing something, or is the conclusion that EV Sv% is talent-driven, but SH Sv% is luck-driven??

Surely EV Sv% regresses toward the mean as the season wears on as well? The sample sizes are quite a lot different: the typical goalie seems to face ~20% of his total shots on the PK, so in a month he’ll face the number of SH shots that he faces in a week at evens. It stands to reason that there will be way larger variation in the SH numbers.

 Small sample sizes capture “hot” and “cold” streaks well, and are interesting and informative in their own right, but are not necessarily representative of the player overall. Not just goalies, either.

It stands to reason that the guys at the very top and bottom will largely regress to the mean over time. If they’re chosen simply because they are outliers, those were the guys who were likely the hottest and coldest during the period in question. You could do the same thing by studying, say, the Three Stars of the Week and seeing how they performed in the subsequent week. Well, duh, their production went down, but should we be surprised? I’m certainly not.

While I’m being picky, I also question the characterization of Luongo and Theodore to name two examples, of guys “who have been hurt by a weak number on the PK”. Theodore’s SHSv% is .857, just .009 below the league norm on just 258 shots. That’s 2.3 goals. Luongo is .008 below the norm, which over 345 shots equates to 2.8 goals. Surely to goodness guys within a couple goals of league average should themselves be considered average. Given the low number of shots, 200-400 for most of the guys named above, a difference of +/- 5 real goals from norm will yield a range in Sv% of roughly +/- .015, whereas the same difference of +/- 5 goals would affect the overall Sv% by just +/- .003. In both cases I would consider any number within that range to be about average.

Put another way, an extra 2 or 3 GA one way or the other over 82 games is not statistically significant when placed on the scale of a playoff series. Even if we could agree on what those “extra” 2 or 3 goals really mean w.r.t. luck/skill.

Or, as the Falconer put it in a comment on MC79’s original post,

But when PP TOI is just a fraction of ES TOI, the statistical weight of a fluky goal or lucky bounce is exaggerated that much more. So it seems to me it is a measurement problem.If it is a measurement problem, I wonder if a goalie’s PP SV% could be forecast more accurately by using a weighted average of the TWO previous seasons? This is certainly true for forecasting skater PP scoring rates. A two year average improves the R-squared substantially IIRC.

Writer for The Copper & Blue and primary shareholder of Zorg Industries

"Never be ashamed of who you are" -- Jean-Baptiste Emanuel Zorg

by Bruce McCurdy on Apr 15, 2010 5:01 PM MDT reply actions  

The conclusion is that SH save percentage is largely luck-driven because the sample size is so small. And yet, because goals are rare, an extreme result in the PK department can give a big effect on overall save percentage even though it’s not likely to be a repeatable skill. The reason I picked up Luongo and Theodore is because they both had very good EV save percentage numbers that get lost in the “overall” category because they were below average on the PK. As you and the Falconer say, perhaps over a much larger sample of shots we would get a better idea of talent level. I’m not sure that two years would be enough to beat “league average” as a predictor of future performance but it could be something fun to look at over the summer.

by Scott Reynolds on Apr 15, 2010 7:51 PM MDT up reply actions  

Tim Thomas is running out of ways to prove that he’s the least conventional goalie in the NHL.

by Passive Voice on Apr 16, 2010 1:19 AM MDT reply actions  

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Northwest Standings

GP W L OTL PT
Vancouver 52 32 15 5 69
Minnesota 52 25 19 8 58
Calgary 53 24 22 7 55
Colorado 54 26 25 3 55
Edmonton 53 21 27 5 47

(updated 2.7.2012 at 7:26 AM MST)

21 - 27 - 5

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Clear Victory Standings

Western Conference

  1. Detroit Red Wings (22-7, .759)
  2. San Jose Sharks (13-5, .722)
  3. Vancouver Canucks (17-7, .708)
  4. St. Louis Blues (11-6, .647)
  5. Chicago Blackhawks (16-11, .593)
  6. Nashville Predators (11-10, .524)
  7. Los Angeles Kings (9-9, .500)
  8. Phoenix Coyotes (11-12, .478)
  9. Dallas Stars (11-14, .440)
  10. Edmonton Oilers (11-15, .423)
  11. Anaheim Ducks (10-14, .417)
  12. Colorado Avalanche (8-13, .381)
  13. Calgary Flames (9-15, .375)
  14. Minnesota Wild (7-13,.350)
  15. Columbus Blue Jackets (5-19, .208)

Eastern Conference

  1. Boston Bruins (21-3, .875)
  2. New York Rangers (18-8, .692)
  3. Pittsburgh Penguins (16-9, .640)
  4. Philadelphia Flyers (14-11, .560)
  5. Toronto Maple Leafs (14-12, .538)
  6. Washington Capitals (13-13, .500)
  7. Montreal Canadiens (11-11, .500)
  8. Ottawa Senators (10-12, .455)
  9. New Jersey Devils (10-12, .455)
  10. Winnipeg Jets (10-14, .417)
  11. Carolina Hurricanes (9-13, .409)
  12. Florida Panthers (7-11, .389)
  13. Buffalo Sabres (7-14, .333)
  14. Tampa Bay Lightning (9-19, .321)
  15. New York Islanders (6-14, .300)

Division Standings

  1. Central (50-38, .568)
  2. Northeast (49-38, .563)
  3. Atlantic (45-37, .549)
  4. Pacific (36-36, .500)
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