Feb 092012
 

It has been shown on numerous occasions that players can influence their own teams on-ice shooting percentage be that through their talents or their style of play.  An example is the PDO vs Luck article I posted the other day.  In that article there is a table that clearly shows that shooting percentage varies across players and that players who are given more ice time (presumably because they are better players) have higher shooting percentages.  The same was not true for on-ice save percentage though.  On-ice save percentages were not ‘stratified’ according to ice time. That study looked at forwards and I have since looked at defensemen and have also attempted to see if organizing players according to defensive zone starts percentages would allow for ‘stratification’ of on-ice shooting percentages but to no avail.  But I am stubborn and didn’t give up.

The next thing I chose to do is compare a players on-ice save percentage with the weighted average of the save percentages of all the goalies the player played with.  The weighted average is based on the number of shots against the goalie and the player were on the ice together for.  So, lets say for example Player A was on the ice for 100 shots against, 30 of those shots were when he was on the ice with Goalie A and 70 were when he was on the ice with Goalie B.  When Goalie A is not playing with Player A his save percentage is 91%.  When Goalie B is not playing with Player A his save percentage is 92%.  The weighted average of the two goalies is (91% * 30 + 92% * 70) /100 or 91.7%.  I then compare that goalie save percentage 91.7% to the players on-ice save percentage by dividing the players save percentage by the goalies save percentage.  So, for example, if Player A’s on-ice save percentage is 92% then I calculate 92% divided by 91.7% to get 100.33.  Any numbers above 100 indicate the player improved his goalies save percentage and any numbers below 100 indicate the player hurt the goalies save percentage.

In order to get an indication of whether the player could produce that much of an improvement due solely to luck I employed a binomial distribution estimation of the likelihood that the player would have an on-ice save percentage greater than the one he posted considering the goalies he played in front of.  The results of all of this are below.  Forwards first followed by defensemen and top 25 and bottom 25 for both.  The data I used was 4 year 2007-11 5v5 zone start adjusted data and only using players with 1250 shots against.

Forward Sv% Infl. Chance > Forward Sv% Infl. Chance >
TAYLOR PYATT 101.94% 0.54% MATT STAJAN 98.87% 90.41%
MANNY MALHOTRA 101.95% 1.00% DEREK ROY 98.98% 90.50%
ZACH PARISE 101.86% 1.08% DAVID BACKES 98.90% 90.86%
JEFF CARTER 101.61% 1.32% SAM GAGNER 98.83% 91.74%
LEE STEMPNIAK 101.70% 1.34% HENRIK ZETTERBERG 98.77% 92.60%
JORDAN STAAL 101.50% 2.45% SIDNEY CROSBY 98.83% 92.92%
TEEMU SELANNE 101.51% 2.95% SHANE DOAN 98.98% 93.52%
TRAVIS MOEN 101.30% 3.59% PATRICK KANE 98.76% 93.64%
CORY STILLMAN 101.34% 3.62% DAINIUS ZUBRUS 98.67% 93.73%
RADIM VRBATA 101.34% 4.54% RICK NASH 98.77% 94.30%
TRAVIS ZAJAC 101.22% 5.22% MARTIN HAVLAT 98.64% 94.72%
BRIAN GIONTA 101.11% 6.15% MARTIN ERAT 98.75% 95.04%
SAMUEL PAHLSSON 101.22% 6.30% DAVID BOOTH 98.61% 95.77%
RADEK DVORAK 101.08% 6.99% PAUL STASTNY 98.44% 96.62%
VALTTERI FILPPULA 101.28% 7.14% ANDREW LADD 98.42% 96.99%
JASON POMINVILLE 101.01% 7.72% MARK RECCHI 98.54% 97.07%
WOJTEK WOLSKI 101.07% 8.24% EVGENI MALKIN 98.48% 97.67%
MIKE KNUBLE 101.03% 8.40% ALEXANDER FROLOV 98.16% 97.93%
MARC SAVARD 101.05% 9.02% RYAN KESLER 98.29% 98.12%
CHRIS THORBURN 101.07% 10.39% THOMAS VANEK 98.41% 98.39%
CHRIS DRURY 100.98% 11.55% TODD WHITE 98.05% 98.45%
MICHAEL RYDER 100.88% 11.62% CHRIS KELLY 98.02% 98.63%
RENE BOURQUE 100.98% 11.81% KRISTIAN HUSELIUS 97.85% 99.39%
NICKLAS BACKSTROM 100.87% 12.22% BRANDON DUBINSKY 97.51% 99.89%
MIKKO KOIVU 100.84% 12.65% ILYA KOVALCHUK 97.65% 99.96%

 

Defenseman Sv% Infl. Chance > Defenseman Sv% Infl. Chance >
KENT HUSKINS 102.22% 0.26% AARON WARD 99.21% 81.98%
NICKLAS LIDSTROM 102.09% 0.31% JORDAN LEOPOLD 99.23% 83.79%
ROB SCUDERI 101.78% 0.52% KEVIN BIEKSA 99.13% 84.68%
SEAN O’DONNELL 101.55% 1.26% JAROSLAV SPACEK 99.25% 84.75%
BRYCE SALVADOR 101.87% 1.28% NICK BOYNTON 99.14% 85.31%
SHANE O’BRIEN 101.63% 1.52% DAN BOYLE 99.19% 85.70%
MIKE WEAVER 101.61% 2.15% STEPHANE ROBIDAS 99.13% 87.86%
ROSTISLAV KLESLA 101.60% 3.15% SHEA WEBER 99.22% 87.88%
TREVOR DALEY 101.23% 3.16% JOHN-MICHAEL LILES 98.98% 89.07%
BRYAN MCCABE 101.25% 3.30% LUBOMIR VISNOVSKY 99.04% 90.41%
TIM GLEASON 101.20% 3.55% DENNIS WIDEMAN 99.11% 91.36%
ROB BLAKE 101.48% 3.86% MARK STREIT 98.79% 91.57%
MARC-EDOUARD VLASIC 101.22% 3.95% BRENT SEABROOK 98.94% 92.21%
PAUL MARTIN 101.37% 4.29% SHAONE MORRISONN 98.80% 92.52%
MIKE LUNDIN 101.51% 4.97% SCOTT NIEDERMAYER 98.82% 93.29%
ANDREJ MESZAROS 101.09% 5.88% ANDREJ SEKERA 98.71% 94.38%
NICK SCHULTZ 101.00% 5.96% FILIP KUBA 98.63% 94.44%
KEITH YANDLE 101.00% 6.79% MARTIN SKOULA 98.61% 95.27%
ANDREI MARKOV 101.07% 7.22% DUNCAN KEITH 98.80% 95.91%
MATT GREENE 101.14% 7.30% BARRET JACKMAN 98.73% 95.96%
ROMAN HAMRLIK 100.81% 9.44% DAN GIRARDI 98.69% 97.15%
TONI LYDMAN 100.83% 10.05% ZBYNEK MICHALEK 98.74% 97.37%
DUSTIN BYFUGLIEN 100.98% 10.12% FEDOR TYUTIN 98.53% 97.74%
JAN HEJDA 100.89% 10.14% DAN HAMHUIS 98.63% 97.87%
CHRIS PRONGER 100.89% 10.72% JACK JOHNSON 97.80% 99.95%

There were a total of 172 forwards and 141 defensemen in the study.  What is interesting is that there were 15 defensemen (10.6% of them) that had their binomial chance of posting their on-ice save percentage at 5% or lower when we would expect 7 by chance.  That means there were more than twice as many really really good on-ice save percentages for defensemen than we would expect by chance alone.

For forwards, there were just 10 who had their binomial chance at 5% or lower which equates to 5.81% so not far off of what we would expect.  We had 10 we expected 8.6.  There were 19 forwards with binomial chance <10% when we should expect 17 by chance.  Not a huge difference.  Conversely, there were 14 forwards with binomial chance >95% or 8.1% compared to the expected 8.6 players and there were 25 forwards above 90% when we should expect 17.

It seems the really good defenders are defensemen and the players most apt to hurt their goalies save percentage are forwards.

That was a pure numbers analysis, what if we looked at the players themselves.  Looking at the list of forwards with better than expected on-ice save percentages we see a lot of third line players that primarily play defensive roles (Pyatt, Malhotra, Moen, Pahlsson, Drury, Staal, etc.).  The bottom 25 forwards contain a lot of more offensive oriented players (Kovalchuk, Huselius, Vanek, Frolov, Malkin, Recchi, Stastny, Booth, Havlat, Nash, Kane, Crosby, Roy, etc.).  There is actually only a 0.04% chance (one in 2500) that Kovakchuk’s on-ice save percentage was due to luck alone.

Much the same can be said for the defensemen.  The defensemen that are  best at improving on-ice save percentage are often defensemen we consider to be defensive defensemen (Huskins, Scuderi, O’Donnell, Salvadore, Weaver, Vlasic, Martin, etc.) or elite 2-way defensemen (Lidstrom, Blake, Yandle, Pronger, etc.) and the ones at the bottom of the list are more offensive specialists (J. Johnson, Keith, Kuba, Sekera, Wideman, Liles, Visnovsky, Boyle, Streit, etc.).  Yes, this is more evidence that Jack Johnson is a horrific defensive defenseman.

All things considered, there does seem to be some order in the list and order is the enemy of luck and the binomial analysis indicates that there may be more going on than one would expect purely from luck.  It seems that players can, to some degree, influence on-ice shooting percentage.  We can’t credit, or blame, the goalies all the time.

 

Update:  Several people suggested that quality of competition needs to be considered since good defenders will play against good opposition.  I agreed so here are some updated results.

To account for quality of competition I considered the opposition shooting percentage of both the player (OppSh%) and the goalie (GoalieOppSh% – weighted average opposition shooting percentage).  I then took the difference (OppSh% – GoalieOppSh%) and added it on to the players save percentage.  So, if a goalie’s opposition shooting percentage was 8.5% and the players was 9.5% I would add 1.0% to the players on-ice save percentage because in theory he should give up goals on an additional 1.0% of the shots because the opponents he faced shot 1.0% better.  In reality though the differences were minor.  The player with the toughest quality of competition relative to his goalies was Nicklas Lidstrom who’s opposition shooting percentage was 9.172% vs his goalies opposition shooting percentage of 8.926% for a difference of 0.246%.

Here are the updated tables.

Defenseman Save% Infl. Chance > Defenseman Save% Infl. Chance >
NICKLAS LIDSTROM 102.37% 0.08% KEVIN KLEIN 99.29% 82.64%
KENT HUSKINS 102.13% 0.36% JAROSLAV SPACEK 99.28% 82.77%
ROB SCUDERI 101.84% 0.41% JORDAN LEOPOLD 99.22% 83.64%
BRYCE SALVADOR 102.01% 0.97% DAN BOYLE 99.26% 83.74%
SEAN O’DONNELL 101.58% 1.26% STEPHANE ROBIDAS 99.26% 84.41%
MIKE WEAVER 101.71% 1.67% KEVIN BIEKSA 99.17% 84.62%
SHANE O’BRIEN 101.53% 2.26% SHEA WEBER 99.29% 86.10%
TIM GLEASON 101.33% 2.43% BRENT SEABROOK 99.12% 88.30%
ROSTISLAV KLESLA 101.73% 2.51% LUBOMIR VISNOVSKY 99.11% 88.96%
ROB BLAKE 101.59% 3.07% SCOTT NIEDERMAYER 98.99% 90.48%
MARC-EDOUARD VLASIC 101.28% 3.31% JOHN-MICHAEL LILES 98.91% 90.57%
PAUL MARTIN 101.42% 3.41% NICK BOYNTON 98.94% 90.79%
TREVOR DALEY 101.19% 3.78% DENNIS WIDEMAN 99.12% 91.41%
NICK SCHULTZ 101.09% 4.30% MARK STREIT 98.77% 91.51%
BRYAN MCCABE 101.12% 4.74% SHAONE MORRISONN 98.80% 92.44%
MIKE LUNDIN 101.51% 4.93% DUNCAN KEITH 98.95% 93.60%
ANDREJ MESZAROS 101.08% 5.90% FILIP KUBA 98.66% 94.41%
ANDREI MARKOV 101.15% 6.11% BARRET JACKMAN 98.87% 94.45%
TONI LYDMAN 100.98% 6.39% ANDREJ SEKERA 98.66% 95.41%
JAN HEJDA 101.04% 6.66% DAN GIRARDI 98.77% 96.02%
ROMAN HAMRLIK 100.85% 8.13% MARTIN SKOULA 98.52% 96.09%
KEITH YANDLE 100.92% 8.13% ZBYNEK MICHALEK 98.78% 96.84%
MATT GREENE 101.07% 8.76% DAN HAMHUIS 98.72% 96.96%
CHRIS PRONGER 100.97% 9.29% FEDOR TYUTIN 98.55% 97.76%
JOHNNY ODUYA 100.86% 9.92% JACK JOHNSON 97.78% 99.95%

 

Forward Save% Infl. Chance > Forward Save% Infl. Chance >
TAYLOR PYATT 101.98% 0.41% DAVID BACKES 99.01% 87.80%
MANNY MALHOTRA 102.02% 0.75% MATT STAJAN 98.92% 89.06%
ZACH PARISE 101.97% 0.84% HENRIK ZETTERBERG 98.93% 90.04%
JEFF CARTER 101.67% 1.07% DEREK ROY 99.00% 90.50%
LEE STEMPNIAK 101.72% 1.36% SAM GAGNER 98.83% 91.80%
JORDAN STAAL 101.56% 1.96% RICK NASH 98.88% 92.22%
TRAVIS MOEN 101.39% 2.90% SHANE DOAN 99.02% 92.29%
TEEMU SELANNE 101.51% 2.98% SIDNEY CROSBY 98.83% 92.96%
CORY STILLMAN 101.35% 3.65% PATRICK KANE 98.73% 93.56%
RADIM VRBATA 101.39% 3.67% DAINIUS ZUBRUS 98.65% 93.64%
SAMUEL PAHLSSON 101.41% 4.18% MARTIN ERAT 98.84% 94.13%
TRAVIS ZAJAC 101.31% 4.32% MARTIN HAVLAT 98.63% 94.72%
BRIAN GIONTA 101.15% 5.05% PAUL STASTNY 98.58% 95.07%
JASON POMINVILLE 101.10% 5.57% DAVID BOOTH 98.60% 95.81%
WOJTEK WOLSKI 101.13% 7.03% ANDREW LADD 98.48% 96.39%
RADEK DVORAK 101.09% 7.06% MARK RECCHI 98.59% 96.40%
VALTTERI FILPPULA 101.30% 7.09% RYAN KESLER 98.38% 97.64%
MIKE KNUBLE 101.10% 7.18% EVGENI MALKIN 98.46% 97.71%
MARC SAVARD 101.11% 7.42% ALEXANDER FROLOV 98.13% 97.92%
MIKKO KOIVU 100.97% 9.25% THOMAS VANEK 98.38% 98.38%
CHRIS DRURY 101.04% 9.75% TODD WHITE 98.03% 98.46%
RENE BOURQUE 101.08% 10.09% CHRIS KELLY 97.96% 98.90%
DEVIN SETOGUCHI 100.97% 11.94% KRISTIAN HUSELIUS 97.89% 99.38%
NICKLAS BACKSTROM 100.86% 12.28% BRANDON DUBINSKY 97.50% 99.89%
CHRIS THORBURN 100.96% 12.40% ILYA KOVALCHUK 97.67% 99.96%

There are 16 defensemen whose odds of having an on-ice save percentage at or above the one they posted is less than 5% when we would expect just 7 by chance alone.

There are 25 defensemen whose odds of having an on-ice save percentage at or above the one they posted is less than 10% when we would expect just 14 by chance alone.

There are 16 defensemen >90% and 7 >95% when we would expect 14 and 7 respectively.  If there are defensemen who are bad at suppressing shooting percentage (and there has to be) they faced fewer than 1250 shots against which likely means they weren’t given the ice time because they are a defensive liability.

For forwards there are 12 players <5% chance by luck alone and 21 <10% by chance alone when we would expect 8.5 and 17 respectively.  There are 23 forwards >90% and 13 forwards >95% when we would expect 17 and 8.5 respectively.  It seems forwards can be sub-par at suppressing shooting percentage and still get the ice time, presumably because coaches put some forwards on the ice to score goals, not prevent them.

In real terms, what does it matter?  Well, there are 13 defensemen (9.1%) who (with the help of their linemates) save 5+ goals per season when they are on the ice in 5v5 situations solely due to their higher than expected on-ice save percentage and 22 defensemen (15.6%) who save 4+ goals.  Nick Lidstrom (and his linemates) save % boost saved his team 9.8 goals per season while Jack Johnson (and his linemates) save % suppression cost his team 11 goals per season, just at 5v5.

 

  4 Responses to “Defenders effect on Save %”

  1.  

    David, very interesting articles and I appreciate you sharing them. In your recent one I have a question on the chance category and what it represents. I’m not by nature a talented, let alone knowledgeable, person in development of statistical information so what does the “chance >” signify? I’m reading your article and trying to grasp what it means in relation to the save % infl.

    Thanks

    •  

      Chance > is the chance that the player could post his on-ice save (or better) percentage by luck alone. I calculate this by using the save percentage of the goalies he is playing in front of and the number of goals and shots he gives up and plugging it into a standard binomial distribution.

      So, for Lidstrom, there is just a 0.31% chance (according to a binomial distribution) that he could post his on-ice save percentage (or better) by luck alone. In other words, the reason why Lidstrom’s on-ice save percentage is so good is probably not due to luck.

      For Kovalchuk there is a 99.96% chance that he would post his on-ice save percentage or better by luck alone. But flip that around it means there is just a 0.04% chance he could post his on-ice save percentage or worse by luck alone. In other words, the reason why Kovalchuk’s on-ice save percentage is so bad is probably not due to luck.

      Hope this helps.

  2.  

    This is tilting at windmills. Six players in the entire league have a 2% or greater effect in your data. This entirely ignores the situation in which a player plays, which is probably the biggest non-random driver of these numbers (I know you want to imagine its a persistent skill). Thus a good rule of thumb is that players don’t affect saves percentage in any significant manner and you have provided strong evidence for this.

    •  

      Lidstrom was on the ice for 1851 zone start adjusted 5v5 shots against over the past 4 seasons. Based on his on-ice save percentage improvement he and the players he was on the ice for saved 34.6 goals against or 8.65 per year. I wouldn’t consider that insignificant. Also remember that this is relative to their teammates so Lidstrom relative to league average might actually save more (since he generally plays with pretty good teammates). The standard deviation of goals saved per year is 3.12 for forwards and 3.39 for defensemen.

      Now, I will grant you that this is almost certainly a smaller effect than the effect of shooting percentage on goals scored but I wouldn’t necessarily call it completely insignificant.

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