Hockey Outsider
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Goalies: Adjusted Playoff Save Percentage (1984-2022)
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Hockey Outsider
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- Jan 16, 2005
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- 15,794
Career Save Percentage - minimum 1,000 adjusted shots
* This table is now updated for 2022
This table shows why it's critically important to take the era into consideration when evaluating goalies' playoff performances. For example, Grant Fuhr posted a seemingly unimpressive 89.9% save percentage between 1984 and 1988, when he helped the Oilers win four Stanley Cups in five years. Adjusted for era, Fuhr stopped 91.8% of the shots he faced during those four seasons. That's not quite elite, but it's a very strong performance over a large sample size (79). That doesn't even take into account the strong likelihood that Fuhr faced tougher quality shots than average due to playing on a run-and-gun team.
Keep in mind that career save percentage is, by definition, a career average. Tom Barrasso had a few rough playoffs at the start and end of his career, and that dragged down his average. His career average of 90.8% is barely above average; if one focuses on his prime from 1988 to 1996, Barrasso's save percentage rises to a very strong 91.6%.
Patrick Roy is tied for the second highest career save percentage out of any goalie who faced at least 1,000 shots (Roy faced more shots than the other top five goalies combined). He's also faced 33% more shots than the next closest goalie (Brodeur). No goalie during the past thirty years has surpassed (or even approached) Roy's combination of an extremely high level of performance, and longevity.
Osgood is slightly above average at stopping the puck. I think the Hall of Fame should be a balance between ability and accomplishments. Osgood has is consistent and durable, but is only slightly above average ability. In my opinion, he shouldn’t get a spot in the Hall due to having the fortune of spending most of his career playing behind the best franchise of the past two decades.
Update for 2012: Fleury has the second-worst adjusted save percentage after a number of horrific games in the spring. Thomas' save percentage drops slightly, but he still has the highest adjusted playoff save percentage 1983-present (minimum 1,000 shots).
Update for 2013: We all know how game six of the Stanley Cup finals ended, but Rask still had a phenomenal postseason. He has the 4th best playoff save percentage of all-time. Quick has now faced enough shots to qualify for the list and based on the threshold (minimum 1,000 shots), he has the T-8th best save percentage of all-time. Lundqvist moves up slightly but is still well below his regular season performance. Fleury drops further and now has the worst adjusted save percentage of all-time.
Update for 2014: Quick and Lundqvist duelled in the Stanley Cup finals and now rank 14th and 15th in career adjusted save percentage. If we increase the threshold to 2,000 adjusted shots, they rank 6th and 7th. Fleury has a decent spring, but still ranks last (minimum 2,000 adjusted shots).
Update for 2015: Crawford wins a second Stanley Cup this year, but his career save percentage is virtually unchanged (slightly above average). Lundqvist had a great spring. He now ranks in the top ten in shots faced (of those, only Roy, Hasek and Belfour have a better save percentage). Holtby was incredible (despite a second round loss) and now has the 2nd highest playoff save percentage of all time (although he barely meets the 1,000 shot threshold).
Update for 2016: with another strong performance, Braden Holtby now has the second highest save percentage all-time. Lundqvist had a tough spring and his numbers decline somewhat (though he's still well above average).
Update for 2017: Holtby has a tough spring and slides down several spots. Fleury was very good but he's had so many poor postseasons that his numbers are still near the bottom of this list. Lundqvist now has faced the 6th most adjusted shots from 1984 onwards.
Update for 2018: with a strong performance culminating in a Stanley Cup victory, Holtby raises his save percentage to 6th highest - believe it or not, on par with Hasek and Belfour. Fleury had an excellent first three rounds (with a disappointing SCF), and raises his save percentage noticeably, despite still being near the bottom of the list. With a brutal first round loss (with numbers that would have been bad even in the high-flying 1980s), Elliott now has the lowest save percentage of anyone on this list.
Update for 2019: with an excellent spring, Tuukka Rask's adjusted playoff save percentage is now on par with Hasek's.
Update for 2020: two-time Vezina trophy winner Sergei Bobrovsky has the dubious distinction of taking the last spot on this list.
Update for 2021: After his Conn Smythe performance, Andrei Vasilevskiy's adjusted save percentage jumps five points. With another rough postseason, two-time Vezina trophy with Sergei Bobrovsky's adjusted save percentage tumbles further.
Update for 2022: Andrei Vasilevskiy played in 23 more games, maintaining his 91.4% adjusted career save percentage. Believe it or not, he already ranks 13th in adjusted shots against. Igor Shesterkin had an excellent debut, with an adjusted save percentage of 92.2% (ahead of Patrick Roy), but he doesn't qualify for the list yet, as he hasn't reached 1,000 adjusted shots.
* This table is now updated for 2022
| Goalie | Shots | Saves | Sv% |
| Tim Thomas | 1,526 | 1,409 | 92.4% |
| Olaf Kolzig | 1,446 | 1,330 | 92.0% |
| Patrick Roy* | 7,218 | 6,638 | 92.0% |
| John Vanbiesbrouck | 2,030 | 1,865 | 91.9% |
| Ken Wregget | 1,767 | 1,622 | 91.8% |
| Dominik Hasek | 3,422 | 3,140 | 91.7% |
| Ed Belfour* | 4,641 | 4,256 | 91.7% |
| Jean-Sebastien Giguere | 1,546 | 1,416 | 91.6% |
| Tuukka Rask | 3,083 | 2,821 | 91.5% |
| Craig Anderson | 1,549 | 1,417 | 91.5% |
| Braden Holtby | 2,798 | 2,558 | 91.4% |
| Andrei Vasilevskiy | 2,937 | 2,685 | 91.4% |
| Mike Smith | 1,488 | 1,360 | 91.4% |
| Kirk McLean | 2,099 | 1,918 | 91.4% |
| Patrick Lalime | 1,105 | 1,010 | 91.4% |
| Cam Ward | 1,137 | 1,038 | 91.3% |
| Dwayne Roloson | 1,478 | 1,348 | 91.2% |
| Jaroslav Halak | 1,070 | 975 | 91.1% |
| Felix Potvin | 2,186 | 1,992 | 91.1% |
| Curtis Joseph | 4,044 | 3,685 | 91.1% |
| Connor Hellebuyck | 1,047 | 954 | 91.1% |
| Ben Bishop | 1,445 | 1,316 | 91.1% |
| Martin Brodeur | 5,439 | 4,953 | 91.1% |
| Jonathan Quick | 2,644 | 2,406 | 91.0% |
| Grant Fuhr* | 3,966 | 3,610 | 91.0% |
| Mike Liut | 1,064 | 968 | 91.0% |
| Mike Richter | 2,182 | 1,985 | 91.0% |
| Henrik Lundqvist | 3,807 | 3,463 | 90.9% |
| Miikka Kiprusoff | 1,679 | 1,527 | 90.9% |
| Bill Ranford | 1,536 | 1,396 | 90.9% |
| Semyon Varlamov | 1,694 | 1,539 | 90.8% |
| Tom Barrasso | 3,521 | 3,197 | 90.8% |
| Roberto Luongo | 2,087 | 1,895 | 90.8% |
| Carey Price | 2,611 | 2,368 | 90.7% |
| Chris Osgood | 3,246 | 2,943 | 90.7% |
| Jimmy Howard | 1,424 | 1,291 | 90.6% |
| Ryan Miller | 1,708 | 1,548 | 90.6% |
| Matt Murray | 1,070 | 970 | 90.6% |
| Martin Jones | 1,663 | 1,506 | 90.5% |
| Nikolai Khabibulin | 2,155 | 1,951 | 90.5% |
| Ron Hextall | 2,632 | 2,382 | 90.5% |
| Corey Crawford | 2,832 | 2,562 | 90.4% |
| Reggie Lemelin | 1,147 | 1,036 | 90.3% |
| Frederik Andersen | 1,572 | 1,420 | 90.3% |
| Marty Turco | 1,345 | 1,215 | 90.3% |
| Kelly Hrudey | 2,531 | 2,286 | 90.3% |
| Pekka Rinne | 2,480 | 2,238 | 90.2% |
| Sean Burke | 1,101 | 993 | 90.2% |
| Greg Millen | 1,336 | 1,205 | 90.2% |
| Brian Boucher | 1,069 | 964 | 90.2% |
| Marc-Andre Fleury | 4,646 | 4,188 | 90.1% |
| Don Beaupre | 1,538 | 1,386 | 90.1% |
| Mike Vernon | 3,493 | 3,146 | 90.1% |
| Jose Theodore | 1,730 | 1,559 | 90.1% |
| Jon Casey | 1,789 | 1,611 | 90.1% |
| Andy Moog | 2,655 | 2,385 | 89.8% |
| Jordan Binnington | 1,120 | 1,005 | 89.8% |
| Evgeni Nabokov | 2,314 | 2,077 | 89.7% |
| Ilya Bryzgalov | 1,304 | 1,169 | 89.7% |
| Arturs Irbe | 1,513 | 1,357 | 89.6% |
| Antti Niemi | 1,808 | 1,618 | 89.5% |
| Ray Emery | 1,051 | 937 | 89.2% |
| Brian Elliott | 1,208 | 1,077 | 89.1% |
| Sergei Bobrovsky | 1,435 | 1,279 | 89.1% |
This table shows why it's critically important to take the era into consideration when evaluating goalies' playoff performances. For example, Grant Fuhr posted a seemingly unimpressive 89.9% save percentage between 1984 and 1988, when he helped the Oilers win four Stanley Cups in five years. Adjusted for era, Fuhr stopped 91.8% of the shots he faced during those four seasons. That's not quite elite, but it's a very strong performance over a large sample size (79). That doesn't even take into account the strong likelihood that Fuhr faced tougher quality shots than average due to playing on a run-and-gun team.
Keep in mind that career save percentage is, by definition, a career average. Tom Barrasso had a few rough playoffs at the start and end of his career, and that dragged down his average. His career average of 90.8% is barely above average; if one focuses on his prime from 1988 to 1996, Barrasso's save percentage rises to a very strong 91.6%.
Patrick Roy is tied for the second highest career save percentage out of any goalie who faced at least 1,000 shots (Roy faced more shots than the other top five goalies combined). He's also faced 33% more shots than the next closest goalie (Brodeur). No goalie during the past thirty years has surpassed (or even approached) Roy's combination of an extremely high level of performance, and longevity.
Osgood is slightly above average at stopping the puck. I think the Hall of Fame should be a balance between ability and accomplishments. Osgood has is consistent and durable, but is only slightly above average ability. In my opinion, he shouldn’t get a spot in the Hall due to having the fortune of spending most of his career playing behind the best franchise of the past two decades.
Update for 2012: Fleury has the second-worst adjusted save percentage after a number of horrific games in the spring. Thomas' save percentage drops slightly, but he still has the highest adjusted playoff save percentage 1983-present (minimum 1,000 shots).
Update for 2013: We all know how game six of the Stanley Cup finals ended, but Rask still had a phenomenal postseason. He has the 4th best playoff save percentage of all-time. Quick has now faced enough shots to qualify for the list and based on the threshold (minimum 1,000 shots), he has the T-8th best save percentage of all-time. Lundqvist moves up slightly but is still well below his regular season performance. Fleury drops further and now has the worst adjusted save percentage of all-time.
Update for 2014: Quick and Lundqvist duelled in the Stanley Cup finals and now rank 14th and 15th in career adjusted save percentage. If we increase the threshold to 2,000 adjusted shots, they rank 6th and 7th. Fleury has a decent spring, but still ranks last (minimum 2,000 adjusted shots).
Update for 2015: Crawford wins a second Stanley Cup this year, but his career save percentage is virtually unchanged (slightly above average). Lundqvist had a great spring. He now ranks in the top ten in shots faced (of those, only Roy, Hasek and Belfour have a better save percentage). Holtby was incredible (despite a second round loss) and now has the 2nd highest playoff save percentage of all time (although he barely meets the 1,000 shot threshold).
Update for 2016: with another strong performance, Braden Holtby now has the second highest save percentage all-time. Lundqvist had a tough spring and his numbers decline somewhat (though he's still well above average).
Update for 2017: Holtby has a tough spring and slides down several spots. Fleury was very good but he's had so many poor postseasons that his numbers are still near the bottom of this list. Lundqvist now has faced the 6th most adjusted shots from 1984 onwards.
Update for 2018: with a strong performance culminating in a Stanley Cup victory, Holtby raises his save percentage to 6th highest - believe it or not, on par with Hasek and Belfour. Fleury had an excellent first three rounds (with a disappointing SCF), and raises his save percentage noticeably, despite still being near the bottom of the list. With a brutal first round loss (with numbers that would have been bad even in the high-flying 1980s), Elliott now has the lowest save percentage of anyone on this list.
Update for 2019: with an excellent spring, Tuukka Rask's adjusted playoff save percentage is now on par with Hasek's.
Update for 2020: two-time Vezina trophy winner Sergei Bobrovsky has the dubious distinction of taking the last spot on this list.
Update for 2021: After his Conn Smythe performance, Andrei Vasilevskiy's adjusted save percentage jumps five points. With another rough postseason, two-time Vezina trophy with Sergei Bobrovsky's adjusted save percentage tumbles further.
Update for 2022: Andrei Vasilevskiy played in 23 more games, maintaining his 91.4% adjusted career save percentage. Believe it or not, he already ranks 13th in adjusted shots against. Igor Shesterkin had an excellent debut, with an adjusted save percentage of 92.2% (ahead of Patrick Roy), but he doesn't qualify for the list yet, as he hasn't reached 1,000 adjusted shots.
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Hockey Outsider
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Top Thirty Playoffs – minimum 1,000 minutes
* updated for 2022
I realize that 93.0% is an arbitrary threshold, but it's a pretty good summary of the best playoff performances of the past thirty years.
As I said in the previous post, there is little doubt that Roy is the greatest playoff goalie of the past three decades. He has three of the top seven performances, and five of the top thirty-three. He performed at an exceptionally high level on five different occasions where his team made the Stanley Cup finals, and he was a major reason why they were victorious four times.
Brodeur doesn't get enough credit for his spectacular performance in 1995. His 92.7% save percentage looks strong on paper, but it's even more incredible when you consider that the league average was only 89.3% that year (88.9% after removing Brodeur's shots and saves). I am adamantly opposed to the idea that Brodeur deserved the Smythe in 2003, but arguably he deserved it in 1995.
Update for 2012: there are two additions to this list. Smythe winner Jonathan Quick ranks 12th all-time. Mike Smith also had a very strong postseason.
Update for 2013: Rask joins the list in 25th place. The 2013 playoffs were very low-scoring and featured a lot excellent goaltending. Unfortunately this made it harder for each individual goalie to stand out since everything is evaluated on a relative basis. In other words, Rask's 94.0% save percentage is less impressive on a relative basis because all eight starting goalies for teams advancing to the second round posted at least a 91.8% save percentage.
Update for 2014: Lundqvist barely joins the list, placing 30th. That sounds about right; it was a very strong postseason, but I wouldn`t put it in the upper echelon of the past thirty years either.
Update for 2015: No all-time great performances this year. Lundqvist was the best of the conference finalists, but he didn't separate himself from the pack. Holtby almost made the list but fell slightly short both in terms of minutes and save percentage.
Update for 2016: No additions to the list. Only three goalies played 1,000 minutes (Jones, Murray, Elliot) and they all performed at a similar level.
Update for 2017: For the third year in a row, no new additions. Pekka Rinne was superb heading into the Stanley Cup finals (the clear Conn Smythe favourite if Nashville won) but played poorly in three of the series' first five games. This sealed his (and his team's) fate.
Update for 2018: For the fourth year in a row, there are no additions. There were many very good performances, but none that met the threshold for "great". Fleury was outstanding through three rounds though.
Update for 2019: Rask has another great performance, statistically almost identical to 2013.
Update for 2020: no additions to the list.
Update for 2021: Vasilevskiy won the Smythe, and places in the top 25 here. Keep in mind that we're in an era of high save percentages, so his 93.7%, although still impressive, isn't quite as good as it looks historically.
Update for 2022: Igor Shesterkin earns a spot on this list with a strong performance for the Rangers. Despite the uptick in scoring, we're still in an era with higher than average save percentages, so his 92.9% mark gets deflated somewhat to 92.4%.
* updated for 2022
| Goalie | Cup? | Smythe? | Year | Team | Minutes | Shots | Saves | Sv% |
| Martin Brodeur | Yes | 1995 | NJD | 1,222 | 475 | 448 | 94.4% | |
| Patrick Roy* | Yes | Yes | 1993 | MTL | 1,293 | 611 | 577 | 94.3% |
| Pelle Lindbergh | 1985 | PHI | 1,008 | 468 | 441 | 94.3% | ||
| Ed Belfour* | 1995 | CHI | 1,014 | 491 | 462 | 93.9% | ||
| Patrick Roy* | Yes | Yes | 1986 | MTL | 1,218 | 489 | 458 | 93.7% |
| Jean-Sebastien Giguere | Yes | 2003 | MDA | 1,407 | 760 | 711 | 93.6% | |
| Patrick Roy* | 1989 | MTL | 1,206 | 521 | 488 | 93.6% | ||
| Reggie Lemelin | 1988 | BOS | 1,027 | 442 | 414 | 93.5% | ||
| Olaf Kolzig | 1998 | WSH | 1,351 | 770 | 720 | 93.5% | ||
| John Vanbiesbrouck | 1996 | FLA | 1,332 | 720 | 672 | 93.4% | ||
| Tim Thomas | Yes | Yes | 2011 | BOS | 1,542 | 789 | 736 | 93.3% |
| Jonathan Quick | Yes | Yes | 2012 | LAK | 1,238 | 546 | 509 | 93.2% |
| Dominik Hasek | 1999 | BUF | 1,217 | 616 | 574 | 93.2% | ||
| Tom Barrasso | Yes | 1991 | PIT | 1,175 | 600 | 559 | 93.2% | |
| Bill Ranford | Yes | Yes | 1990 | EDM | 1,401 | 676 | 629 | 93.2% |
| Patrick Roy* | Yes | Yes | 2001 | COL | 1,451 | 693 | 645 | 93.0% |
| Mike Smith | 2012 | PHX | 1,027 | 611 | 568 | 93.0% | ||
| Dwayne Roloson | 2006 | EDM | 1,160 | 625 | 581 | 92.9% | ||
| Sean Burke | 1988 | NJD | 1,001 | 530 | 492 | 92.9% | ||
| Kirk McLean | 1994 | VAN | 1,544 | 813 | 755 | 92.8% | ||
| Martin Brodeur | 1994 | NJD | 1,171 | 526 | 488 | 92.7% | ||
| Andy Moog | 1990 | BOS | 1,195 | 489 | 453 | 92.7% | ||
| Arturs Irbe | 2002 | CAR | 1,078 | 511 | 474 | 92.7% | ||
| Marc-Andre Fleury | 2008 | PIT | 1,251 | 603 | 559 | 92.6% | ||
| Andrei Vasilevskiy | Yes | Yes | 2021 | TBL | 1,390 | 663 | 614 | 92.6% |
| Tuukka Rask | 2019 | BOS | 1,459 | 699 | 647 | 92.5% | ||
| Tuukka Rask | 2013 | BOS | 1,466 | 724 | 669 | 92.4% | ||
| Igor Shesterkin | 2022 | NYR | 1,182 | 628 | 580 | 92.4% | ||
| Alain Chevrier | 1989 | CHI | 1,013 | 478 | 441 | 92.3% | ||
| Ed Belfour* | Yes | 1999 | DAL | 1,544 | 648 | 597 | 92.3% |
I realize that 93.0% is an arbitrary threshold, but it's a pretty good summary of the best playoff performances of the past thirty years.
As I said in the previous post, there is little doubt that Roy is the greatest playoff goalie of the past three decades. He has three of the top seven performances, and five of the top thirty-three. He performed at an exceptionally high level on five different occasions where his team made the Stanley Cup finals, and he was a major reason why they were victorious four times.
Brodeur doesn't get enough credit for his spectacular performance in 1995. His 92.7% save percentage looks strong on paper, but it's even more incredible when you consider that the league average was only 89.3% that year (88.9% after removing Brodeur's shots and saves). I am adamantly opposed to the idea that Brodeur deserved the Smythe in 2003, but arguably he deserved it in 1995.
Update for 2012: there are two additions to this list. Smythe winner Jonathan Quick ranks 12th all-time. Mike Smith also had a very strong postseason.
Update for 2013: Rask joins the list in 25th place. The 2013 playoffs were very low-scoring and featured a lot excellent goaltending. Unfortunately this made it harder for each individual goalie to stand out since everything is evaluated on a relative basis. In other words, Rask's 94.0% save percentage is less impressive on a relative basis because all eight starting goalies for teams advancing to the second round posted at least a 91.8% save percentage.
Update for 2014: Lundqvist barely joins the list, placing 30th. That sounds about right; it was a very strong postseason, but I wouldn`t put it in the upper echelon of the past thirty years either.
Update for 2015: No all-time great performances this year. Lundqvist was the best of the conference finalists, but he didn't separate himself from the pack. Holtby almost made the list but fell slightly short both in terms of minutes and save percentage.
Update for 2016: No additions to the list. Only three goalies played 1,000 minutes (Jones, Murray, Elliot) and they all performed at a similar level.
Update for 2017: For the third year in a row, no new additions. Pekka Rinne was superb heading into the Stanley Cup finals (the clear Conn Smythe favourite if Nashville won) but played poorly in three of the series' first five games. This sealed his (and his team's) fate.
Update for 2018: For the fourth year in a row, there are no additions. There were many very good performances, but none that met the threshold for "great". Fleury was outstanding through three rounds though.
Update for 2019: Rask has another great performance, statistically almost identical to 2013.
Update for 2020: no additions to the list.
Update for 2021: Vasilevskiy won the Smythe, and places in the top 25 here. Keep in mind that we're in an era of high save percentages, so his 93.7%, although still impressive, isn't quite as good as it looks historically.
Update for 2022: Igor Shesterkin earns a spot on this list with a strong performance for the Rangers. Despite the uptick in scoring, we're still in an era with higher than average save percentages, so his 92.9% mark gets deflated somewhat to 92.4%.
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Hockey Outsider
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Stanley Cup Winners
* updated for 2022
Only eight goalies have won the Stanley Cup while posting an adjusted save percentage of 93.0% or higher. Six of those netminders won the Conn Smythe. As for the other two - as incredible as Barrasso was in 1991, I don't think he was more valuable than Lemieux's insane 44 point performance. The more I think about it, the more I think that Brodeur should have won the 1995 Smythe.
Detroit won three Stanley Cups in six years with only above-average goaltending. Vernon, Osgood and Hasek played well enough not to cost the powerhouse Red Wings any series, but rarely stole any games. I was critical of Osgood in a previous post but, to his credit, he played very well in 2008.
Only two of the past thirty Stanley Cup winners posted a below average save percentage: Marc-Andre Fleury and Jonathan Quick (just barely below average).
Update for 2012: Quick was phenomenal this spring. He has the fifth best adjusted save percentage among Stanley Cup winning goalies.
Update for 2013: the numbers indicate that Crawford's performance this spring was below-average compared to other Cup winning goalies. Keep in mind this statistic is based on relative performance and 2013 may have featured the most consistent display of goaltending I've ever seen in the postseason. As the quality of goaltending rises, it becomes increasingly difficult for any individual to stand out. What do others think?
Update for 2014: see the opposite of my commentary in 2012. Quick was good enough not to cost the Kings games, but generally wasn't a difference maker. Statistically, he was one of the worst performing Cup winning goalies of the the past three decades.
Update for 2015: another Stanley Cup winning goalie with a mediocre performance (Crawford). He wasn't bad, of course, he was solidly above average, but nothing more than that.
Update for 2016: Murray was very good compared to the average goalie, but was below average compared to other Stanley Cup winning goaltenders. That sounds right to me.
Update for 2017: this is the closest a Stanley Cup winner has been to having a goalie platoon in years. Fleury was a bit above average while playing 867 minutes, while and Murray was excellent (92.3% save percentage) while playing 669 minutes.
Update for 2018: Holtby was objectively good this spring, but still below average compared to the high standards of the average Stanley Cup winning netminder.
Update for 2019: Binnington had the 2nd lowest adjusted save percentage of any goalie going back to 1985. He wasn't bad - but he was definitely inconsistent.
Update for 2020: Vasilevskiy had a good playoff run. He was probably the Lightning's 4th best player. Adjusted for era, his numbers were slightly below average for a Cup winning goalie.
Update for 2021: Vasilevskiy had a strong playoff run - not in the upper echelon for a Cup winner, but well above average.
Update for 2022: Darcy Kuemper was the primary starter for Colorado (he won three games against Nashville, all four against St. Louis, one against Edmonton, and all four against Tampa Bay). He had the worst adjusted save percentage of any Stanley Cup winning goalie going back to 1984 (by a decent margin, too). Kuemper had a stretch of five consecutive games where he stopped no more than 90% of the shots he faced (pretty good for 1982, not so much for 2022). But he had three strong games in the Stanley Cup finals and, given Colorado's firepower, that was enough.
* updated for 2022
| Goalie | Cup? | Smythe? | Year | Team | Minutes | Shots | Saves | Sv% |
| Martin Brodeur | Yes | 1995 | NJD | 1,222 | 475 | 448 | 94.4% | |
| Patrick Roy* | Yes | Yes | 1993 | MTL | 1,293 | 611 | 577 | 94.3% |
| Patrick Roy* | Yes | Yes | 1986 | MTL | 1,218 | 489 | 458 | 93.7% |
| Tim Thomas | Yes | Yes | 2011 | BOS | 1,542 | 789 | 736 | 93.3% |
| Jonathan Quick | Yes | Yes | 2012 | LAK | 1,238 | 546 | 509 | 93.2% |
| Tom Barrasso | Yes | 1991 | PIT | 1,175 | 600 | 559 | 93.2% | |
| Bill Ranford | Yes | Yes | 1990 | EDM | 1,401 | 676 | 629 | 93.2% |
| Patrick Roy* | Yes | Yes | 2001 | COL | 1,451 | 693 | 645 | 93.0% |
| Andrei Vasilevskiy | Yes | Yes | 2021 | TBL | 1,390 | 663 | 614 | 92.6% |
| Ed Belfour* | Yes | 1999 | DAL | 1,544 | 648 | 597 | 92.3% | |
| Martin Brodeur | Yes | 2003 | NJD | 1,491 | 678 | 626 | 92.2% | |
| Chris Osgood | Yes | 2008 | DET | 1,160 | 425 | 392 | 92.2% | |
| Grant Fuhr* | Yes | 1988 | EDM | 1,136 | 485 | 446 | 92.1% | |
| Patrick Roy* | Yes | 1996 | COL | 1,454 | 635 | 585 | 92.1% | |
| Cam Ward | Yes | Yes | 2006 | CAR | 1,320 | 590 | 543 | 92.0% |
| Grant Fuhr* | Yes | 1985 | EDM | 1,064 | 498 | 458 | 92.0% | |
| Mike Richter | Yes | 1994 | NYR | 1,417 | 618 | 568 | 92.0% | |
| Mike Vernon | Yes | 1989 | CGY | 1,381 | 543 | 499 | 91.9% | |
| Tom Barrasso | Yes | 1992 | PIT | 1,233 | 604 | 554 | 91.7% | |
| Nikolai Khabibulin | Yes | 2004 | TBL | 1,401 | 652 | 598 | 91.7% | |
| Andrei Vasilevskiy | Yes | 2020 | TBL | 1,708 | 709 | 650 | 91.6% | |
| Braden Holtby | Yes | 2018 | WSH | 1,386 | 600 | 550 | 91.6% | |
| Grant Fuhr* | Yes | 1987 | EDM | 1,148 | 499 | 457 | 91.6% | |
| Martin Brodeur | Yes | 2000 | NJD | 1,450 | 575 | 527 | 91.5% | |
| Corey Crawford | Yes | 2013 | CHI | 1,504 | 641 | 587 | 91.5% | |
| Mike Vernon | Yes | Yes | 1997 | DET | 1,229 | 469 | 429 | 91.4% |
| Matt Murray | Yes | 2016 | PIT | 1,267 | 566 | 517 | 91.2% | |
| Jean-Sebastien Giguere | Yes | 2007 | ANA | 1,067 | 462 | 420 | 90.9% | |
| Chris Osgood | Yes | 1998 | DET | 1,361 | 612 | 556 | 90.9% | |
| Corey Crawford | Yes | 2015 | CHI | 1,223 | 604 | 548 | 90.8% | |
| Dominik Hasek | Yes | 2002 | DET | 1,455 | 599 | 543 | 90.7% | |
| Antti Niemi | Yes | 2010 | CHI | 1,322 | 614 | 557 | 90.7% | |
| Jonathan Quick | Yes | 2014 | LAK | 1,605 | 768 | 694 | 90.4% | |
| Grant Fuhr* | Yes | 1984 | EDM | 883 | 479 | 441 | 92.0% | |
| Marc-Andre Fleury | Yes | 2017 | PIT | 867 | 473 | 430 | 90.9% | |
| Jordan Binnington | Yes | 2019 | STL | 1,560 | 699 | 631 | 90.2% | |
| Marc-Andre Fleury | Yes | 2009 | PIT | 1,447 | 650 | 584 | 89.7% | |
| Darcy Kuemper | Yes | 2022 | COL | 887 | 337 | 301 | 89.3% |
Only eight goalies have won the Stanley Cup while posting an adjusted save percentage of 93.0% or higher. Six of those netminders won the Conn Smythe. As for the other two - as incredible as Barrasso was in 1991, I don't think he was more valuable than Lemieux's insane 44 point performance. The more I think about it, the more I think that Brodeur should have won the 1995 Smythe.
Detroit won three Stanley Cups in six years with only above-average goaltending. Vernon, Osgood and Hasek played well enough not to cost the powerhouse Red Wings any series, but rarely stole any games. I was critical of Osgood in a previous post but, to his credit, he played very well in 2008.
Only two of the past thirty Stanley Cup winners posted a below average save percentage: Marc-Andre Fleury and Jonathan Quick (just barely below average).
Update for 2012: Quick was phenomenal this spring. He has the fifth best adjusted save percentage among Stanley Cup winning goalies.
Update for 2013: the numbers indicate that Crawford's performance this spring was below-average compared to other Cup winning goalies. Keep in mind this statistic is based on relative performance and 2013 may have featured the most consistent display of goaltending I've ever seen in the postseason. As the quality of goaltending rises, it becomes increasingly difficult for any individual to stand out. What do others think?
Update for 2014: see the opposite of my commentary in 2012. Quick was good enough not to cost the Kings games, but generally wasn't a difference maker. Statistically, he was one of the worst performing Cup winning goalies of the the past three decades.
Update for 2015: another Stanley Cup winning goalie with a mediocre performance (Crawford). He wasn't bad, of course, he was solidly above average, but nothing more than that.
Update for 2016: Murray was very good compared to the average goalie, but was below average compared to other Stanley Cup winning goaltenders. That sounds right to me.
Update for 2017: this is the closest a Stanley Cup winner has been to having a goalie platoon in years. Fleury was a bit above average while playing 867 minutes, while and Murray was excellent (92.3% save percentage) while playing 669 minutes.
Update for 2018: Holtby was objectively good this spring, but still below average compared to the high standards of the average Stanley Cup winning netminder.
Update for 2019: Binnington had the 2nd lowest adjusted save percentage of any goalie going back to 1985. He wasn't bad - but he was definitely inconsistent.
Update for 2020: Vasilevskiy had a good playoff run. He was probably the Lightning's 4th best player. Adjusted for era, his numbers were slightly below average for a Cup winning goalie.
Update for 2021: Vasilevskiy had a strong playoff run - not in the upper echelon for a Cup winner, but well above average.
Update for 2022: Darcy Kuemper was the primary starter for Colorado (he won three games against Nashville, all four against St. Louis, one against Edmonton, and all four against Tampa Bay). He had the worst adjusted save percentage of any Stanley Cup winning goalie going back to 1984 (by a decent margin, too). Kuemper had a stretch of five consecutive games where he stopped no more than 90% of the shots he faced (pretty good for 1982, not so much for 2022). But he had three strong games in the Stanley Cup finals and, given Colorado's firepower, that was enough.
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Hockey Outsider
Registered User
- Jan 16, 2005
- 9,491
- 15,794
Conn Smythe winners
* updated for 2022
Over the past 38 years, ten goalie have won the Conn Smythe. The first seven listed above were strong selections; although there were other strong candidates in some years, it’s impossible to argue that the actual winners were poor selections.
Cam Ward was very good in 2006, but I would have given the Smythe to Chris Pronger (no defenseman has ever won the Smythe while failing to win the Stanley Cup). I probably would have given the Smythe to Brind’Amour ahead of Ward as well.
Wayne Gretzky deserved the Smythe in 1987, but probably fell victim to the impossibly high expectations others had of him. Although his 34 points in 21 points are staggering, it was weaker than his previous four playoffs! Ron Hextall’s save percentage is almost certainly understated since he faced so many shots from the incredibly dangerous Oilers.
I don’t think that Mike Vernon had a good case for winning the Smythe in 1997. He was solidly above average, but that’s not Conn Smythe material. There were several better candidates including Fedorov (led Wings in scoring and provided exceptional two-way play), Shanahan (second on Wings in scoring and was credited for giving the Wings the toughness and grit they lacked in previous years) and Lidstrom & Murphy (for shutting down Lindros so effectively in the Cup finals).
Update for 2012: Quick was phenomenal this spring. He has the fifth best adjusted save percentage among Conn Smythe winning goalies.
Update for 2013: Patrick Kane won the Smythe, no changes to this list.
Update for 2014: Justin Williams won the Smythe, no changes to this list.
Update for 2015: Duncan Keith was exceptional this spring - maybe the best performance by a non-goalie since Malkin and Crosby in 2009 - thus no changes to this list.
Update for 2016: Sidney Crosby won the Smythe, no changes to this list.
Update for 2017: Crosby became the first player in 25 years to win consecutive Conn Smythe trophies, so no changes to this list.
Update for 2018: Alexander Ovechkin won the Smythe, no changes to this list.
Update for 2019: Ryan O'Reilly won the Smythe, no changes to this list.
Update for 2020: Victor Hedman won the Smythe, no changes to this list. (It's now been eight years since a goalie won the Conn Smythe).
Update for 2021: we have our first Smythe-winning goalie in nine years. Personally I would have given the award to Kucherov, but Vasilevskiy was also a worth recipient.
Update for 2022: Cale Makar won the Smythe, no changes to this list.
* updated for 2022
| Goalie | Cup? | Smythe? | Year | Team | Minutes | Shots | Saves | Sv% |
| Patrick Roy* | Yes | Yes | 1993 | MTL | 1,293 | 611 | 577 | 94.3% |
| Patrick Roy* | Yes | Yes | 1986 | MTL | 1,218 | 489 | 458 | 93.7% |
| Jean-Sebastien Giguere | Yes | 2003 | MDA | 1,407 | 760 | 711 | 93.6% | |
| Tim Thomas | Yes | Yes | 2011 | BOS | 1,542 | 789 | 736 | 93.3% |
| Jonathan Quick | Yes | Yes | 2012 | LAK | 1,238 | 546 | 509 | 93.2% |
| Bill Ranford | Yes | Yes | 1990 | EDM | 1,401 | 676 | 629 | 93.2% |
| Patrick Roy* | Yes | Yes | 2001 | COL | 1,451 | 693 | 645 | 93.0% |
| Andrei Vasilevskiy | Yes | Yes | 2021 | TBL | 1,390 | 663 | 614 | 92.6% |
| Cam Ward | Yes | Yes | 2006 | CAR | 1,320 | 590 | 543 | 92.0% |
| Ron Hextall | Yes | 1987 | PHI | 1,540 | 751 | 688 | 91.6% | |
| Mike Vernon | Yes | Yes | 1997 | DET | 1,229 | 469 | 429 | 91.4% |
Over the past 38 years, ten goalie have won the Conn Smythe. The first seven listed above were strong selections; although there were other strong candidates in some years, it’s impossible to argue that the actual winners were poor selections.
Cam Ward was very good in 2006, but I would have given the Smythe to Chris Pronger (no defenseman has ever won the Smythe while failing to win the Stanley Cup). I probably would have given the Smythe to Brind’Amour ahead of Ward as well.
Wayne Gretzky deserved the Smythe in 1987, but probably fell victim to the impossibly high expectations others had of him. Although his 34 points in 21 points are staggering, it was weaker than his previous four playoffs! Ron Hextall’s save percentage is almost certainly understated since he faced so many shots from the incredibly dangerous Oilers.
I don’t think that Mike Vernon had a good case for winning the Smythe in 1997. He was solidly above average, but that’s not Conn Smythe material. There were several better candidates including Fedorov (led Wings in scoring and provided exceptional two-way play), Shanahan (second on Wings in scoring and was credited for giving the Wings the toughness and grit they lacked in previous years) and Lidstrom & Murphy (for shutting down Lindros so effectively in the Cup finals).
Update for 2012: Quick was phenomenal this spring. He has the fifth best adjusted save percentage among Conn Smythe winning goalies.
Update for 2013: Patrick Kane won the Smythe, no changes to this list.
Update for 2014: Justin Williams won the Smythe, no changes to this list.
Update for 2015: Duncan Keith was exceptional this spring - maybe the best performance by a non-goalie since Malkin and Crosby in 2009 - thus no changes to this list.
Update for 2016: Sidney Crosby won the Smythe, no changes to this list.
Update for 2017: Crosby became the first player in 25 years to win consecutive Conn Smythe trophies, so no changes to this list.
Update for 2018: Alexander Ovechkin won the Smythe, no changes to this list.
Update for 2019: Ryan O'Reilly won the Smythe, no changes to this list.
Update for 2020: Victor Hedman won the Smythe, no changes to this list. (It's now been eight years since a goalie won the Conn Smythe).
Update for 2021: we have our first Smythe-winning goalie in nine years. Personally I would have given the award to Kucherov, but Vasilevskiy was also a worth recipient.
Update for 2022: Cale Makar won the Smythe, no changes to this list.
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Hockey Outsider
Registered User
- Jan 16, 2005
- 9,491
- 15,794
Support Neutral Wins & Losses
* update for 2022
This is a concept developed by Taco McArthur – link. Essentially, it shows how many games a goalie would be expected to win, had they played on an average team. I’m not sure if I like this or Wins Added more (the latter is a statistic I created), but TM’s statistic is far easier to calculate and gives fairly similar results, so let’s go with his! The chart above shows the results for all goalies with 30+ decisions.
Roy’s dominance continues. He has the most Support Neutral Wins by a massive margin. He also has the best win percentage out of any goalie with 60+ decisions. Once again, there is little doubt that Roy is greatest playoff goalie of the past three decades.
Tim Thomas has a staggering SNWL record. He has an exceptional win percentage, and his 50 decisions are a substantial number – only 27 goalies have earned the decision in more than fifty playoff games.
Fuhr actually went 90-47 in the playoffs (from 1984 onwards). The numbers suggest that he would have been 72-65 on an average team. That means that Fuhr was a very good goalie (and even better during his prime), but he clearly benefited by playing on the highest-scoring team of the modern era.
For all the criticism he's received, Joseph ranks 5th in playoff wins and has the 10th best win percentage of the 30 goalies who earned 50+ decisions. I don't think he's a HOF goalie, but he's often unfairly criticised (especially in Detroit, when his teammates scored just 1.88 goals per game, despite finishing 1st and 2nd in regular season scoring those two years).
Update for 2012: Brodeur extends his lead over third place. Despite a first-round exit, Thomas breaks the 30 win barrier.
Update for 2013: Quick continues to climb the charts and how has almost matched Thomas' career playoff record. Lundqvist continues to climb the chart, albeit slowly. Rask is off to an excellent 21-14 start to his career.
Update for 2014: Lundqvist moves up to 10th place all-time, and Quick moves up to 13th place.
Update for 2015: Corey Crawford, with his second Stanley Cup in three years, continues to rise. He's in 18th place (his win percentage is good, but not great). Lundqvist also continues to climb - he`s now 9th place. One deep playoff run could push him into 4th.
Update for 2016: Lundqvist climbs into 9th place. Holtby's numbers are almost identical to Tim Thomas (but Holtby has never been past the second round).
Update for 2017: Lundqvist moves into 6th place in playoff wins. Fleury is 11th, but has a losing record. Holtby's numbers are still impressive (with the important asterisk that he's never been past the second round).
Update for 2018: Fleury climbs into 7th place in victories, tied with Hasek and Vernon, though he still has a losing record overall. Holtby moves into 12th, and he has the 5th best win percentage of anyone on this list. Out of all goalies with at least 60 decisions, only Roy has a better win percentage.
Update for 2019: Rask climbs to 12th on the all-time list, and he has the 5th highest win percentage of anybody on this list (2nd highest win percentage, behind Roy, of any goalie with 80+ decisions).
Update for 2020: Fleury moves into sole possession of 5th place, while Lundqvist moves into a tie for 6th (with Joseph). Holtby and Rask now rank 12th and 13th. None of these netminders had long (or particularly good) postseasons, so this is more a reflection of pushing up their career numbers bit by bit.
Update for 2021: Fleury climbs to 4th place in support neutral wins (still with a losing record though). Vasilevskiy and Price both move into the top 20.
Update for 2022: Andrei Vasilevskiy climbs to 13th in support-neutral wins (and he has the 7th best win percentage on this list).
* update for 2022
| Goalie | Decision | Wins | Losses | Win% |
| Patrick Roy* | 245 | 143 | 102 | 58.2% |
| Martin Brodeur | 204 | 109 | 95 | 53.5% |
| Ed Belfour* | 156 | 89 | 67 | 57.0% |
| Marc-Andre Fleury | 165 | 81 | 84 | 48.8% |
| Grant Fuhr* | 137 | 72 | 65 | 52.3% |
| Curtis Joseph | 129 | 69 | 60 | 53.6% |
| Henrik Lundqvist | 128 | 69 | 59 | 53.5% |
| Mike Vernon | 133 | 65 | 68 | 49.1% |
| Dominik Hasek | 114 | 65 | 49 | 57.2% |
| Chris Osgood | 123 | 63 | 60 | 51.4% |
| Tom Barrasso | 115 | 59 | 56 | 51.7% |
| Tuukka Rask | 103 | 58 | 45 | 56.6% |
| Andrei Vasilevskiy | 101 | 57 | 44 | 56.1% |
| Braden Holtby | 97 | 54 | 43 | 56.0% |
| Jonathan Quick | 92 | 50 | 42 | 54.7% |
| Andy Moog | 100 | 47 | 53 | 47.3% |
| Corey Crawford | 94 | 47 | 47 | 50.2% |
| Carey Price | 88 | 46 | 42 | 52.3% |
| Ron Hextall | 90 | 46 | 44 | 50.8% |
| Pekka Rinne | 89 | 44 | 45 | 49.0% |
| Kelly Hrudey | 82 | 41 | 41 | 49.8% |
| Mike Richter | 74 | 40 | 34 | 53.4% |
| Evgeni Nabokov | 84 | 39 | 45 | 46.5% |
| Felix Potvin | 72 | 38 | 34 | 53.0% |
| Kirk McLean | 68 | 37 | 31 | 54.8% |
| Nikolai Khabibulin | 70 | 36 | 34 | 51.2% |
| Roberto Luongo | 69 | 36 | 33 | 51.7% |
| Tim Thomas | 50 | 31 | 19 | 62.4% |
| Ken Wregget | 53 | 31 | 22 | 58.6% |
| Semyon Varlamov | 58 | 30 | 28 | 52.1% |
| Jon Casey | 63 | 30 | 33 | 47.6% |
This is a concept developed by Taco McArthur – link. Essentially, it shows how many games a goalie would be expected to win, had they played on an average team. I’m not sure if I like this or Wins Added more (the latter is a statistic I created), but TM’s statistic is far easier to calculate and gives fairly similar results, so let’s go with his! The chart above shows the results for all goalies with 30+ decisions.
Roy’s dominance continues. He has the most Support Neutral Wins by a massive margin. He also has the best win percentage out of any goalie with 60+ decisions. Once again, there is little doubt that Roy is greatest playoff goalie of the past three decades.
Tim Thomas has a staggering SNWL record. He has an exceptional win percentage, and his 50 decisions are a substantial number – only 27 goalies have earned the decision in more than fifty playoff games.
Fuhr actually went 90-47 in the playoffs (from 1984 onwards). The numbers suggest that he would have been 72-65 on an average team. That means that Fuhr was a very good goalie (and even better during his prime), but he clearly benefited by playing on the highest-scoring team of the modern era.
For all the criticism he's received, Joseph ranks 5th in playoff wins and has the 10th best win percentage of the 30 goalies who earned 50+ decisions. I don't think he's a HOF goalie, but he's often unfairly criticised (especially in Detroit, when his teammates scored just 1.88 goals per game, despite finishing 1st and 2nd in regular season scoring those two years).
Update for 2012: Brodeur extends his lead over third place. Despite a first-round exit, Thomas breaks the 30 win barrier.
Update for 2013: Quick continues to climb the charts and how has almost matched Thomas' career playoff record. Lundqvist continues to climb the chart, albeit slowly. Rask is off to an excellent 21-14 start to his career.
Update for 2014: Lundqvist moves up to 10th place all-time, and Quick moves up to 13th place.
Update for 2015: Corey Crawford, with his second Stanley Cup in three years, continues to rise. He's in 18th place (his win percentage is good, but not great). Lundqvist also continues to climb - he`s now 9th place. One deep playoff run could push him into 4th.
Update for 2016: Lundqvist climbs into 9th place. Holtby's numbers are almost identical to Tim Thomas (but Holtby has never been past the second round).
Update for 2017: Lundqvist moves into 6th place in playoff wins. Fleury is 11th, but has a losing record. Holtby's numbers are still impressive (with the important asterisk that he's never been past the second round).
Update for 2018: Fleury climbs into 7th place in victories, tied with Hasek and Vernon, though he still has a losing record overall. Holtby moves into 12th, and he has the 5th best win percentage of anyone on this list. Out of all goalies with at least 60 decisions, only Roy has a better win percentage.
Update for 2019: Rask climbs to 12th on the all-time list, and he has the 5th highest win percentage of anybody on this list (2nd highest win percentage, behind Roy, of any goalie with 80+ decisions).
Update for 2020: Fleury moves into sole possession of 5th place, while Lundqvist moves into a tie for 6th (with Joseph). Holtby and Rask now rank 12th and 13th. None of these netminders had long (or particularly good) postseasons, so this is more a reflection of pushing up their career numbers bit by bit.
Update for 2021: Fleury climbs to 4th place in support neutral wins (still with a losing record though). Vasilevskiy and Price both move into the top 20.
Update for 2022: Andrei Vasilevskiy climbs to 13th in support-neutral wins (and he has the 7th best win percentage on this list).
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quoipourquoi
Goaltender
I came to the same conclusion about 14 months ago.
http://hfboards.mandatory.com/showpost.php?p=31408501&postcount=113
Adjusting to the regular season average made more sense to me though. For one, it's a larger sample size that includes all NHL goalies. More than that, it eliminates the issue of a 1995 Dominik Hasek going from an NHL best .930 to a .863 and helping throw the average off from where one would expect it to be from having watched the season. After all, 1995 was one of those rare three seasons in which the playoff save percentage was lower than the regular season save percentage (which really shouldn't happen for any reason other than sample size, given that half the leagues worst teams are out).
Doctor No
Registered User
Very interesting stuff - I'm curious to see how it compares to what I've got in my database.
Agreed on Brodeur's non-Smythe in 1995; on the whole, he probably has the right number of Smythes, but they're not allocated optimally to my tastes.
I'm also working on a concept called "par", which Bill James used to use to measure managers' success. I'm not sure how well it will work for goalies, but I like what I'm seeing so far.
Nice to see Kirk McLean up there!
Agreed on Brodeur's non-Smythe in 1995; on the whole, he probably has the right number of Smythes, but they're not allocated optimally to my tastes.
I'm also working on a concept called "par", which Bill James used to use to measure managers' success. I'm not sure how well it will work for goalies, but I like what I'm seeing so far.
Nice to see Kirk McLean up there!
Johnny Engine
Moderator
- Jul 29, 2009
- 5,054
- 2,483
You'd have given Brodeur his zero Conn Smythes in different years than he didn't win them?
Either way, 1 Conn Smythe seems about right for his career, but that never works as a measuring stick for players anyway (unless the question is "was Patrick Roy awesome? Y/N"), since only one person on one of two teams wins them every year.
Doctor No
Registered User
You'd have given Brodeur his zero Conn Smythes in different years than he didn't win them?
Ah, sorry - in my defense, I'm drunk (on work, not alcohol) right now.
seventieslord
Student Of The Game
poor poor irbe. ._. And ill chip in my obligatory broken record of I think he should have won the conn smythe in 2k2.
seventieslord
Student Of The Game
Yeah, Irbe does pretty poorly from a career perspective when you consider that includes one very stellar year.
Hah yea, the first couple of san jose years he had some great high light games...then followed with some blow outs that really hurt him. I still wish dallas would have given him a chance that year he was with them. Moog wasnt doing anything, and irbe actually played pretty well that year at times. Woulda been nice to see him get some playoff action with a team that was actually expected to win. Oh well...
Dalton
Registered User
I think anyone using or assuming Gausian distribution in their adjustments should take a look at the results of this large study which included NHL players.
I'm not an expert in this so I would encourage interested readers to read the study rather than simply accept my summary.
http://onlinelibrary.wiley.com/doi/10.1111/j.1744-6570.2011.01239.x/full
I saw some criticism but nothing that seemed more deep than a casual defense of the bell curve.
In short the idea seems to be that a small percentage of elite performers including outliers have a disproportionately large impact on the average result. The rest fall below the mathematical average of the group.
The other end of the scale, negative performances have the same impact and no they don't balance out.
They show that a bell curve doesn't match the data without massaging the data first. the data aligns much more closely to a Paretian http://www.vigorinnovation.com/from-winning-to-the-long-tail or power curve.
I suppose one could say that the outliers at both ends are the data. Eliminating or normalizing them distorts the data.
So when examining data for a season say goals scored or save % one must look at the outliers for explanation since they are most responsible for the data. Adjusting them to make the curve smoother or bell-like is wrong. That just ignores and distorts the most meaningful data.
It appears to support the 80-20 rule. 20% of sales people are responsible for 80% of the sales. Within that group the same rule applies. Furthermore this appears to apply within a team, season or career.
Similarly a small percentage of the group is responsible for most of the negative stats.
So a season with a few really bad goalies would greatly impact the overall data just as a season with a few really good goalies.
Normalizing this data creates the false impression that the group as a whole were better or worse. It would also serve to lessen the performance of the good goalies or enhance the performance of the bad goalies.
I did see a link to using Excel to calculate power curves which may interest those of you who've amassed the raw data.
The first two paragraphs-
"We revisit a long-held assumption in human resource management, organizational behavior, and industrial and organizational psychology that individual performance follows a Gaussian (normal) distribution. We conducted 5 studies involving 198 samples including 633,263 researchers, entertainers, politicians, and amateur and professional athletes. Results are remarkably consistent across industries, types of jobs, types of performance measures, and time frames and indicate that individual performance is not normally distributed—instead, it follows a Paretian (power law) distribution. Assuming normality of individual performance can lead to misspecified theories and misleading practices. Thus, our results have implications for all theories and applications that directly or indirectly address the performance of individual workers including performance measurement and management, utility analysis in preemployment testing and training and development, personnel selection, leadership, and the prediction of performance, among others.
Research and practice in organizational behavior and human resource management (OBHRM), industrial and organizational (I-O) psychology, and other fields including strategic management and entrepreneurship ultimately build upon, directly or indirectly, the output of the individual worker. In fact, a central goal of OBHRM is to understand and predict the performance of individual workers. There is a long-held assumption in OBHRM that individual performance clusters around a mean and then fans out into symmetrical tails. That is, individual performance is assumed to follow a normal distribution (Hull, 1928; Schmidt & Hunter, 1983; Tiffin, 1947). When performance data do not conform to the normal distribution, then the conclusion is that the error “must†lie within the sample not the population. Subsequent adjustments are made (e.g., dropping outliers) in order to make the sample “better reflect†the “true†underlying normal curve. Gaussian distributions are in stark contrast to Paretian or power law distributions, which are typified by unstable means, infinite variance, and a greater proportion of extreme events. Figure 1 shows a Paretian distribution overlaid with a normal curve."
I'm not an expert in this so I would encourage interested readers to read the study rather than simply accept my summary.
http://onlinelibrary.wiley.com/doi/10.1111/j.1744-6570.2011.01239.x/full
I saw some criticism but nothing that seemed more deep than a casual defense of the bell curve.
In short the idea seems to be that a small percentage of elite performers including outliers have a disproportionately large impact on the average result. The rest fall below the mathematical average of the group.
The other end of the scale, negative performances have the same impact and no they don't balance out.
They show that a bell curve doesn't match the data without massaging the data first. the data aligns much more closely to a Paretian http://www.vigorinnovation.com/from-winning-to-the-long-tail or power curve.
I suppose one could say that the outliers at both ends are the data. Eliminating or normalizing them distorts the data.
So when examining data for a season say goals scored or save % one must look at the outliers for explanation since they are most responsible for the data. Adjusting them to make the curve smoother or bell-like is wrong. That just ignores and distorts the most meaningful data.
It appears to support the 80-20 rule. 20% of sales people are responsible for 80% of the sales. Within that group the same rule applies. Furthermore this appears to apply within a team, season or career.
Similarly a small percentage of the group is responsible for most of the negative stats.
So a season with a few really bad goalies would greatly impact the overall data just as a season with a few really good goalies.
Normalizing this data creates the false impression that the group as a whole were better or worse. It would also serve to lessen the performance of the good goalies or enhance the performance of the bad goalies.
I did see a link to using Excel to calculate power curves which may interest those of you who've amassed the raw data.
The first two paragraphs-
"We revisit a long-held assumption in human resource management, organizational behavior, and industrial and organizational psychology that individual performance follows a Gaussian (normal) distribution. We conducted 5 studies involving 198 samples including 633,263 researchers, entertainers, politicians, and amateur and professional athletes. Results are remarkably consistent across industries, types of jobs, types of performance measures, and time frames and indicate that individual performance is not normally distributed—instead, it follows a Paretian (power law) distribution. Assuming normality of individual performance can lead to misspecified theories and misleading practices. Thus, our results have implications for all theories and applications that directly or indirectly address the performance of individual workers including performance measurement and management, utility analysis in preemployment testing and training and development, personnel selection, leadership, and the prediction of performance, among others.
Research and practice in organizational behavior and human resource management (OBHRM), industrial and organizational (I-O) psychology, and other fields including strategic management and entrepreneurship ultimately build upon, directly or indirectly, the output of the individual worker. In fact, a central goal of OBHRM is to understand and predict the performance of individual workers. There is a long-held assumption in OBHRM that individual performance clusters around a mean and then fans out into symmetrical tails. That is, individual performance is assumed to follow a normal distribution (Hull, 1928; Schmidt & Hunter, 1983; Tiffin, 1947). When performance data do not conform to the normal distribution, then the conclusion is that the error “must†lie within the sample not the population. Subsequent adjustments are made (e.g., dropping outliers) in order to make the sample “better reflect†the “true†underlying normal curve. Gaussian distributions are in stark contrast to Paretian or power law distributions, which are typified by unstable means, infinite variance, and a greater proportion of extreme events. Figure 1 shows a Paretian distribution overlaid with a normal curve."
Doctor No
Registered User
I wouldn't expect anything in the NHL to reasonably follow a bell curve - since for anything you're measuring a hockey ability by, the sample set of National Hockey League players represents the far right end of a bell curve.
For instance, suppose that the NHL-average save percentage is 91%. There's a hell of a lot more people out there who could post save percentages four standard deviations beneath that than there are who could post save percentages four standard deviations above that.
For instance, suppose that the NHL-average save percentage is 91%. There's a hell of a lot more people out there who could post save percentages four standard deviations beneath that than there are who could post save percentages four standard deviations above that.
Dalton
Registered User
The results of the study suggest that the data doesn't fit a Bell curve at all. No matter what end you're talking about. The data fits a power curve.
The authors reveal the software they use and say it works with excel.
"Results reported in Table 1 show that the Paretian distribution yielded a superior fit than the Gaussian distribution in every one of the 54 scientific fields. Recall that a larger chi-square value indicates worse fit and, thus, can be considered an index of badness of fit. As Table 1 shows, the average misfit for the Paretian distribution was 23,888 whereas the misfit of the normal distribution was larger than forty-four trillion (i.e., 44,199,201,241,681)—a difference in favor of the Paretian distribution in the order of 1:1.9 billion. Figure 2a displays a histogram of the empirically observed performance distribution of researchers. To interpret these results further, consider the field of Agriculture (see Table 1). A normal distribution and a sample size of 25,006 would lead to approximately 35 scholars with more than 9.5 publications (three standard deviations above the mean). In contrast, our data include 460 scholars with 10 or more publications. In other words, the normal distribution underestimates the number of extreme events and does not describe the actual distribution well."
The authors reveal the software they use and say it works with excel.
"Results reported in Table 1 show that the Paretian distribution yielded a superior fit than the Gaussian distribution in every one of the 54 scientific fields. Recall that a larger chi-square value indicates worse fit and, thus, can be considered an index of badness of fit. As Table 1 shows, the average misfit for the Paretian distribution was 23,888 whereas the misfit of the normal distribution was larger than forty-four trillion (i.e., 44,199,201,241,681)—a difference in favor of the Paretian distribution in the order of 1:1.9 billion. Figure 2a displays a histogram of the empirically observed performance distribution of researchers. To interpret these results further, consider the field of Agriculture (see Table 1). A normal distribution and a sample size of 25,006 would lead to approximately 35 scholars with more than 9.5 publications (three standard deviations above the mean). In contrast, our data include 460 scholars with 10 or more publications. In other words, the normal distribution underestimates the number of extreme events and does not describe the actual distribution well."
Hockey Outsider
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I debated whether to compare the numbers to the regular season or playoff average. I used the playoff average because save percentages usually increase in the playoffs (teams are generally more disciplined and conservative, and arguably some of the weaker goalies don't qualify). Thus a 91.5% save percentage might be good in the regular season but merely average in the playoffs.
Nice to see Kirk McLean up there!
Thanks! McLean benefits because substantially all of his playoff career coincided with his peak (88% of his career playoff games occured between 1992 and 1995). Contrast that with, say, Tom Barrasso, who played a lot of games before and after his peak. Still, a very impressive showing.
Irbe is tough to evaluate. Brutal numbers in San Jose (86.7% adjusted save percentage), but a strong showing in Carolina (91.4%). Part of that was Irbe improving with age, but it's also partly due to the Hurricanes being better defensively than the Sharks.
Hockey Outsider
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Here's a chart showing the distribution of adjusted save percentages. I'm only using goalies with 300+ minutes; that threshold is arbitrary, but there needs to be some kind of arbitrary threshold or else we'd be looking at statistically meaningless results for goalies who faced only a few dozen shots.
The largest single category (90% to 91%) straddles the mean and features nearly 25% of all observations. Although there are more observations above the mean than below, the distribution appears to follow roughly a bell curve.
If the average save percentage was 90.5%, but most goalies were actually stopping say 85% but Patrick Roy and a few other superstars were stopping 95%, than another distribution (and therefore another method to evaluate goalies) might be more meaningful. Let me know if I've misunderstood your post.
Just curious... Have you been able to also make some kind of adjustment for shots against when playing penalty killing? I would think that you (or others), even if not providing it here, may have thought about it? If you (or someone else) have looked, are there any particular things/patterns that stands out?
Hockey Outsider
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I haven't made an adjustment for penalty killing. (For those who are unfamiliar, what we mean is that a goalie who plays on a (un)disciplined team would face a disproportionately low (high) percentage of powerplay shots, which are significantly more likely to result in a goal).
All the data I used was taken from hockey-reference.com because it's well formatted and easy to use. That website doesn't have ES/PP/SH shot and save breakdowns. I know that data exists on NHL.com back to 1998, but it would be very time consuming to merge the data from those two websites. I'm not sure if that type of data even exists prior to 1998.
In summary, although I think it would be valuable to take that information into account, but I'm not sure if it exists prior to 1998, and I'm not willing to spend the countless hours it would take to merge and clean the data after 1998.
Doctor No
Registered User
Ultimately, it's not going to be a normal distribution, but (something) reasonably approximating the right-end tail of one. There are thousands of goaltenders out there who could post an NHL save percentage six standard deviations lower than the NHL average, and on the other hand, there are none (because none would get that opportunity).
The other poster is suggesting that it follows a power distributions, and he's probably right (I've written and spoke enough on the topic to know that power distributions handle outliers far more reasonably). I don't think that it matters too much in this context.
I can understand that. It was just that I remember someone doing it for the regular season, and that it might have been you. I haven't looked into the playoffs at all myself, and have no idea how much that kind of adjustment would change stats. For example, Philadelphia used to take many penalty minutes during the regular season, and if that meant they played more PK than most other teams, it may even further push their goalies up (Lindbergh. Perhaps Parent?) (But this is to me completely hypothetical as I don't even know if they played more PK during the playoffs than other teams.)
I think you have a few duplicates in your third table, the one showing Stanley Cup winners. I noticed it when counting the occurences of Patrick Roy's name, but also Fuhr occurs twice. Roy anyhow looks impressive! Before I started spending time here, I suspected he might have been somewhat overrated, because I've seen him make many mistakes too (especially when handling the puck behind/around the net). But in threads like this one he (or his stats) lives up to his high reputation.
seventieslord
Student Of The Game
Since we don't know the PPOA for the teams in the playoffs before a certain year, this would have to be estimated using team PIM. But we do have enough information available to draw reasonable conclusions: The fighting majors earned by the teams in the regular season, the number of PIMs they had, and the number of PPOAs those PIMs resulted in. You would get results that are pretty close, I think.
If anyone really wants to do the work, they can scour the HSP game-by-game to find exactly how many PPs each team faced.
You are right that facing a higher percentage of your shots from the PP is going to drag down your sv%. Adjusting for actual shot quality is probably more important than just situational adjustments, but particularly for pre-lockout seasons where that data doesn't exist I think situational adjustments get us closer to the truth than further away.
Hockey Outsider
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Thanks for the explanation - I have a better understand of that now.
Yes, I looked into it with my recent post about 2009-2012 regular season stats. I think shot situations are worth taking into account, though of course there's a trade-off. In this case, I decided that the additional information that this adjustment would produce is not worth the dozens of hours it would take me to accumulate, organization and analyze the data. If anyone is interested in doing this, I can send them what I've worked on thus far.
Thanks, that's a good catch. I updated the table.
Had you asked me five years ago, I would have said that Hasek was the greatest goalie of all-time by a sizable margin. Although I still have Hasek first, I now rank Roy a very close second. Relative to their peers, Hasek was only marginally better at stopping the puck than Roy (though I do recognize that Hasek played against a better peer group).
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