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.