It has been shown on numerous occasions that players can influence their own teams onice 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 onice save percentage though. Onice 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 onice 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 onice 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 onice save percentage by dividing the players save percentage by the goalies save percentage. So, for example, if Player A’s onice 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 onice 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 200711 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% 

JOHNMICHAEL 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% 
MARCEDOUARD 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 onice 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 onice 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 onice 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 onice save percentage was due to luck alone.
Much the same can be said for the defensemen. The defensemen that are best at improving onice save percentage are often defensemen we consider to be defensive defensemen (Huskins, Scuderi, O’Donnell, Salvadore, Weaver, Vlasic, Martin, etc.) or elite 2way 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 onice shooting percentage. We can’t credit, or blame, the goalies all the time.
Continue reading »