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.

On Monday I outlined an all-encompassing player evaluation model that allows us to evaluate every forward, defenseman and goalie under the same methodology.  In short, the system compares how many goals are scored for and against while a player is on the ice and compares it to how many goals scored for/against one should expect based on the quality of his line mates and opposition.  That model, I believe, makes a reasonable attempt at evaluating a players performance, but it can be improved.

The first method of improvement is to utilize the additional information we have about the quality of a players line mates and opposition once we have run the model.  Initially I use the goals for and against performance of his line mates and opposition when the player being evaluated is not on the ice at the same time as his line mates and opposition.  But now that we have run the model we, at least theoretically, have a better understanding of the quality of his team mates and opposition.  I can then take the output of the first model run and use it as the input of the second model run to get new and better results.  I can then continue doing this iteratively and the good news is that after every iteration the difference between the player rating from that iteration and the previous iteration trends towards zero which is a very nice result.

Earlier today I posted an article showing how a goalies save percentage varies by age. It was pointed out that one of the flaws in that analysis is that I didn’t account for the fact that over time the average NHL save percentage has varied, and has generally increased over time. In fact, the change from the 1980’s to the 1990’s is quite significant. As a result I decided it was important enough to take the next step and account for variations in league wide save percentages.

To accomplish this I took each goalies save percentage and divided it by the league wide save percentage for that year which essentially tells us how much a goalie was better or worse than his peers in that given year. Anything greater than 1 meant the goalie was better than the average goalie and anything less than 1 meant the goalie was not as good as the average goalie that year. I then performed the same analysis using this ratio number instead of straight save percentage.

The end result is that a goalies peak years generally start sooner than seen under the straight save percentage analysis and the drop off in a goalies latter years is more pronounced as well. Generally speaking a goalie will have his best years between ages 22 and 34 after which the drop off is fairly pronounced. This isn’t true for all goalies though as the truly elite goalies such as Roy, Belfour, Hasek and Brodeur played above their peers well beyond age 34 but for the majority of goalies it is downhill once you get past your early 30’s.

Note: In the above chart I only included ages for which data was available for at least 3 goalies and I only included years where a goalie played at least 5 games. This was done so as to not skew the chart at the edges and the result is only ages 19-41 are shown though Barasso played at age 18 and Hasek played until age 43.