## Defenders effect on Save %

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.

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.

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.