Goalie Research: Wins Added

[Hockey Outsider]

In hockey, wins are the only thing that matter. However, it doesn’t follow that the goalie with the most wins was the best or most valuable netminder. It’s unfair and illogical to compare the W/L/T record of a goalie on an expansion team, to that of a goalie on a Stanley Cup contender.

I created a statistic called “Wins Added”. It looks at how many games a goalie won, in excess of the number of games they were expected to win on the strength of their team’s offense and defense. I’ll save the details for the next post, and I’ll show the results here.

Note: due to data limitations these numbers are from 1987-88 to 2006-07 only. Also note that when I say "wins", I'm referring to wins plus half of ties, overtime losses, and shootout losses.

Career wins added - 1988 to present

Goalie|Games|Wins Added
Patrick Roy | 934 | +67
Dominik Hasek | 694 | +63
Martin Brodeur | 891 | +53
Roberto Luongo | 417 | +33
Curtis Joseph | 913 | +32
Ed Belfour | 963 | +27
Daren Puppa | 429 | +20
Manny Legace | 242 | +19
Marty Turco | 320 | +19
Jean-Sebastien Giguere | 353 | +18

A few quick comments:
- Roy is, if anything, underrated by stats junkies. His 90.8% save percentage from 1988-1992, compared to the league average of 88.3% during that span, is nearly Hasek-like. Yes, Hasek is the better goalie, but don’t be fooled by Roy’s save percentage – it only looks low because of the era.
- I’m surprised Belfour wasn’t in 4th place, but his poor showing in 2006, and his years of floating in San Jose and late in his Chicago tenure, hurt his career (cumulative) ranking.
- Puppa is here on the strength of two seasons: 1990 and 1996. Based on my work I estimate that the ’90 Sabres would have been 8th in conference without Puppa’ goaltending (they finished 2nd with a division title); the ’96 Lightning squeaked into the playoffs thanks to Puppa, even though an average goalie would have led them to 11th place.

Peak wins added (best five years)

Goalie|Wins Added
Dominik Hasek | +7.6
Roberto Luongo | +6.9
Patrick Roy | +6.7
Martin Brodeur | +5.4
Tom Barrasso | +5.4
Mike Vernon | +5.2
Curtis Joseph | +5.1
Daren Puppa | +4.8
Ed Belfour | +4.8
JS Giguere | +4.2

- Luongo helped the Canucks earn the division title in 2007; had they replaced him with an average goalie, Vancouver would have been 23rd in the league. Even in 2006 (when he took a ridiculous amount of flak solely because he played on a terrible team), he brought the Panthers to within 7 points of a playoff spot. Replacing him with an average goalie, Florida would have 27th in the league.
- Brodeur was an average regular season goalie from 1999-2004. (He was excellent before and after that six-year stretch, and was always great in the playoffs). He won 270 games during that span, but a statistically average goalie that had his ice time on those excellent New Jersey teams, was expected to win 268.9 games. Again, Brodeur was great during the other two-thirds of his career, but people became blinded by his wins during that period without considering the context.

An obvious limitation to this formula is that this is for the regular season only. I was able to break down the data to account for mid-season trades, but I wasn't able to separate team-level data between starters and backups. (So Hasek's wins added are understated if he faced more shots per game than his backups).

However, an advantage over stats like save percentage and GAA is that clutch play should (theoretically) be reflected in the win/loss totals, so timing matters. Also, puckhandling should also (theoretically) be reflected in the win/loss totals so a major strength of excellent puckhandling goalies, is not ignored.

Generally, goalies add fewer wins to their teams than most people probably expect. From 1988 onwards, Patrick Roy won 566 games, but a statistically average goalie playing for his Canadiens and Avs would have been expected to win 502 games. Only 86 goalies have been able to add 5 wins (10 points) to their teams in a single season.

Click to read more...

 

[Hockey Outsider]

How the Formula Works

* Warning this is very technical and boring

Step 1: calculate expected win percentage for a given team-year

Calculation: There is a very strong correlation (about 94%) between the Pythagorean formula, and actual win percentage. Thus, my estimate for how many games a goalie would have won is based on that formula. To calculate it, you need the team’s goals for per game [A], and the team’s shots against per game . To calculate what an average goalie would have done you need to look at the league average save percentage [C]. Plug it in to this formula: A^2 / [(A^2) + [(B^2) * (1-C)]]^2. That shows the expected win percentage based on how a goalie with a league-average save percentage would have performed, given the team’s level of offense and defense.

Example: In 1998, Dominik Hasek played for the Buffalo Sabres. His team scored an average of 2.52 goals per game [A], and allowed 30.6 shots against . The league average save percentage was 90.6% [C]. Plugging these numbers into the formula, a statistically average goalie is expected to win 43.4% of their decisions.

Step 2: calculate the number of games the goalie was expected to win

Calculation: take expected win percentage from step 1 and multiply it by minutes played, divided by sixty.

Example: In 1998, Hasek was expected to win 43.4% of his decisions. He played 4,220 / 60 = 70.3 games so he was expected to win 30.7 games.

Step 3: compare expected wins to actual wins

Calculation: subtract expected wins from actual wins

Example: in 1998, Hasek won 39.5 games but a statistically average goalie only would have won 30.7 games, had they played 70.3 games for the 1998 Sabres. Therefore Hasek singlehandedly won 8.8 games for the Sabres. (That might not sound like a lot, but, to put that into perspective, Buffalo finished 6th in the conference with 89 points. If Hasek was replaced with an average goalie, Buffalo would have lost the ~18 points that Hasek singlehandedly earned them, and they would have fallen to 10th in the conference, well out of the playoffs.

Step 4: adjust to an 82-game schedule

Calculation: take result of Step 3 * 82 games/ length of NHL schedule

Example: in 1998 the schedule was 82 games so no adjustments are necessary

 

[Bear of Bad News]

Very nice work! I'll add some more salient observations when I'm not at work - I didn't hear back from you a few months ago, so I was afraid that you'd tabled things.

 

[MountainHawk]

This is awesome work.

 

[pitseleh]

Awesome work as usual Hockey Outsider. The work you put into your analyses is always appreciated.

 

[xeric716x]

that was a pretty cool read

 

[foame]

sweet stats

 

[Dennis Bonvie]

Career wins added - 1988 to present

Goalie|Games|Wins Added
Patrick Roy | 934 | +67
Dominik Hasek | 694 | +63
Martin Brodeur | 891 | +53
Roberto Luongo | 417 | +33
Curtis Joseph | 913 | +32
Ed Belfour | 963 | +27
Daren Puppa | 429 | +20
Manny Legace | 242 | +19
Marty Turco | 320 | +19
Jean-Sebastien Giguere | 353 | +18

Peak wins added (best five years)

Goalie|Wins Added
Dominik Hasek | +7.6
Roberto Luongo | +6.9
Patrick Roy | +6.7
Martin Brodeur | +5.4
Tom Barrasso | +5.4
Mike Vernon | +5.2
Curtis Joseph | +5.1
Daren Puppa | +4.8
Ed Belfour | +4.8
JS Giguere | +4.2

- Luongo helped the Canucks earn the division title in 2007; had they replaced him with an average goalie, Vancouver would have been 23rd in the league. Even in 2006 (when he took a ridiculous amount of flak solely because he played on a terrible team), he brought the Panthers to within 7 points of a playoff spot. Replacing him with an average goalie, Florida would have 27th in the league.
- Brodeur was an average regular season goalie from 1999-2004. (He was excellent before and after that six-year stretch, and was always great in the playoffs). He won 270 games during that span, but a statistically average goalie that had his ice time on those excellent New Jersey teams, was expected to win 268.9 games. Again, Brodeur was great during the other two-thirds of his career, but people became blinded by his wins during that period without considering the context.

An obvious limitation to this formula is that this is for the regular season only. I was able to break down the data to account for mid-season trades, but I wasn't able to separate team-level data between starters and backups. (So Hasek's wins added are understated if he faced more shots per game than his backups).

However, an advantage over stats like save percentage and GAA is that clutch play should (theoretically) be reflected in the win/loss totals, so timing matters. Also, puckhandling should also (theoretically) be reflected in the win/loss totals so a major strength of excellent puckhandling goalies, is not ignored.

Generally, goalies add fewer wins to their teams than most people probably expect. From 1988 onwards, Patrick Roy won 566 games, but a statistically average goalie playing for his Canadiens and Avs would have been expected to win 502 games. Only 86 goalies have been able to add 5 wins (10 points) to their teams in a single season.

And yet Brodeur was awarded 2 Vezina Trophies in those years. Hence "The Brodeur Controversy"

 

[nik jr]

always very interesting. thanks, HO.

 

[WarriorOfGandhi]

Roy

 

[reckoning]

Career wins added - 1988 to present

Goalie|Games|Wins Added
Patrick Roy | 934 | +67
Dominik Hasek | 694 | +63
Martin Brodeur | 891 | +53
Roberto Luongo | 417 | +33
Curtis Joseph | 913 | +32
Ed Belfour | 963 | +27
Daren Puppa | 429 | +20
Manny Legace | 242 | +19
Marty Turco | 320 | +19
Jean-Sebastien Giguere | 353 | +18

I love the fact that you factored in the team's offence into the analysis, as it seems to always be ignored when mentioning a goalies win totals. Your chart shows Brodeur's worth better than just save percentage. Yes, he played behind a great defensive team for most of those years, but it was also a team that usually gave little offensive support. He didn't have much room to make any errors. I'd be interested to see how Grant Fuhr's numbers for Edmonton's dynasty years compare.

Interesting work. The only suggested improvement would be to do it on a game-by-game basis, but that would take forever to tabulate.

What was the highest single-season mark you found?

 

[seventieslord]

Good stuff, but i wouldn't say Brodeur was always good in the playoffs. In 2000 and 2001 the Devils carried him to the finals.

 

[overpass]

This is good stuff, Hockey Outsider. It looks like a very sound method. It puts the available goalie stats together to create one number about as well as possible, I think.

The Wins Added method seems to give all the credit for a team outperforming their goals for and against to the goalie. I don't think this is an ideal model, as forwards can score timely goals and the defence can tighten up with a lead. However, it's probably better than ignoring the issue entirely, and it's been handled clearly and consistently here.

I have one suggestion, if you are interested in adding more complexity in the search for more accuracy. Try adjusting the goalie's save% based on the number of times his team was shorthanded vs the league average. SH Sv% has been considerably lower than EV Sv% over the past few years, and I expect it's always been that way. I'm not sure if historical data is available on EV Sv% vs SH Sv%, but there might be possibilities there in any case.

How the Formula Works

* Warning this is very technical and boring

Step 1: calculate expected win percentage for a given team-year

Calculation: There is a very strong correlation (about 94%) between the Pythagorean formula, and actual win percentage. Thus, my estimate for how many games a goalie would have won is based on that formula. To calculate it, you need the team’s goals for per game [A], and the team’s shots against per game . To calculate what an average goalie would have done you need to look at the league average save percentage [C]. Plug it in to this formula: A^2 / [(A^2) + [(B^2) * (1-C)]]^2. That shows the expected win percentage based on how a goalie with a league-average save percentage would have performed, given the team’s level of offense and defense.

Warning: more technical and boring stuff
I think you could get better results by using a different exponent for the Pythagorean formula from 2. I know in baseball they usually use 1.8 for precise analysis. See this piece here on more accurate use of the Pythagorean formula in baseball

I ran the numbers for the NHL from 1968-99 (when they added overtime loss points) and I got an exponent of 2.04 as the best fit - not significantly different from 2. However, the best exponent to use varies with the goals per game. Use the formula (total goals per game)^0.375 to get the best exponent for a given level of scoring.

This kind of precision is really unnecessary 99% of the time, but for this kind of work I think it might be a little more accurate, especially for low-scoring and high-scoring years.

 

[Canadiens1958]

Balance

How the Formula Works

* Warning this is very technical and boring

Step 1: calculate expected win percentage for a given team-year

Calculation: There is a very strong correlation (about 94%) between the Pythagorean formula, and actual win percentage. Thus, my estimate for how many games a goalie would have won is based on that formula. To calculate it, you need the team’s goals for per game [A], and the team’s shots against per game . To calculate what an average goalie would have done you need to look at the league average save percentage [C]. Plug it in to this formula: A^2 / [(A^2) + [(B^2) * (1-C)]]^2. That shows the expected win percentage based on how a goalie with a league-average save percentage would have performed, given the team’s level of offense and defense.

Example: In 1998, Dominik Hasek played for the Buffalo Sabres. His team scored an average of 2.52 goals per game [A], and allowed 30.6 shots against . The league average save percentage was 90.6% [C]. Plugging these numbers into the formula, a statistically average goalie is expected to win 43.4% of their decisions.

Step 2: calculate the number of games the goalie was expected to win

Calculation: take expected win percentage from step 1 and multiply it by minutes played, divided by sixty.

Example: In 1998, Hasek was expected to win 43.4% of his decisions. He played 4,220 / 60 = 70.3 games so he was expected to win 30.7 games.

Step 3: compare expected wins to actual wins

Calculation: subtract expected wins from actual wins

Example: in 1998, Hasek won 39.5 games but a statistically average goalie only would have won 30.7 games, had they played 70.3 games for the 1998 Sabres. Therefore Hasek singlehandedly won 8.8 games for the Sabres. (That might not sound like a lot, but, to put that into perspective, Buffalo finished 6th in the conference with 89 points. If Hasek was replaced with an average goalie, Buffalo would have lost the ~18 points that Hasek singlehandedly earned them, and they would have fallen to 10th in the conference, well out of the playoffs.

Step 4: adjust to an 82-game schedule

Calculation: take result of Step 3 * 82 games/ length of NHL schedule

Example: in 1998 the schedule was 82 games so no adjustments are necessary

Impressive amount of work and interesting BUT I seriously doubt that your numbers will balance.

Your Hasek example seems to be derived from the 1997-98 season. Actual data below.

GP W L T PTS
Buffalo Sabres 82 36 29 17 89

Dominik Hasek 72 33 23 13 79
Steve Shields 16 3 6 4 10

Martin Biron was not a factor.

You then do the various calculations and adjustments, reaching the conclusion that Dominik Hasek added 8.8 wins to the team and with an average goalie the team would have had 18 fewer points, finishing 10th and out of the play-offs.

Somewhat bogus since you do not answer the obvious questions. Specifically:

1.) To who did the points go to? You just dropped 18 points from the final standings
BUT those 18 points have to go somewhere for the standings to balance. You do
not seem to have a ready justification for the disappearance of these points.


2.) Statistical fairness dictates that your study apply the same analysis to all the
teams and the goalies for the 1997-98 season before your claim that a 10th place
finish is the only result possible.

Kindly provide the data as outlined above.

Thank you.

 

[Zine]

1.) To who did the points go to? You just dropped 18 points from the final standings
BUT those 18 points have to go somewhere for the standings to balance. You do
not seem to have a ready justification for the disappearance of these points.


2.) Statistical fairness dictates that your study apply the same analysis to all the
teams and the goalies for the 1997-98 season before your claim that a 10th place
finish is the only result possible.

I don't believe the author is claiming Buffalo would've finished 10th. He (or she) appears to be using it solely as a hypothetical to help illustrate the importance of 8.8 extra victories a season.

 

[Canadiens1958]

Beyond the Obvious.

I don't believe the author is claiming Buffalo would've finished 10th. He (or she) appears to be using it solely as a hypothetical to help illustrate the importance of 8.8 extra victories a season.

Unfortunately for your point or the author's the numbers have to balance. They cannot be pulled out of the sky to make a point or look good on paper. You do not need such complex calculations to deduce that 8.8 more wins a season would be important.

Suggest reading up on Chaos Theory.

 

[MadArcand]

I wonder how Sean Burke would fare using this comparison...

 

[Bear of Bad News]

1.) To who did the points go to? You just dropped 18 points from the final standings
BUT those 18 points have to go somewhere for the standings to balance. You do
not seem to have a ready justification for the disappearance of these points.


2.) Statistical fairness dictates that your study apply the same analysis to all the
teams and the goalies for the 1997-98 season before your claim that a 10th place
finish is the only result possible.

Forgive me for being blunt, but I don't see any point in sugar-coating this. Anyone who would suggest "reading up on Chaos Theory" at this point in the discussion clearly doesn't understand what they're talking about. Now to answer your questions.

(1) Hockey Outsider's examples in this area are first-order approximations to multiple-dimensional problems, similar to a derivative or a differential. The fact that you've taken something mentioned in an essentially parenthetical commentary and latched onto it like a lemur may be interesting from a sociological perspective.

(2) Just because Hockey Outsider didn't report on every goaltender on every team in that season, it doesn't mean that he hasn't done it. In fact, he clearly has done it, since he has posted the career leaders which include the season you are questioning. Furthermore, see the answer to question (1) above.

Your points amount to essentially standing up in the back of a differential equations lecture and shouting down the speaker, saying over and over again that ODEs should be abolished because they cannot solve a generalized non-linear equation. ODEs are excellent work - in fact, it's pretty much considered a mature field - but if you try to get it to do things they're not ready to do, then you're going to lose a lot of interesting work. Might as well drop calculus from the curriculum, too.

I don't think that Hockey Outsider would claim that his work is the be-all and end-all of the subject; we keep learning more and more about how to properly evaluate goaltenders and this is a (large) step in the process. But MAKE VALID CRITIQUES, please.

Perhaps you've seen an Ashton Kutcher vehicle, or read a Crichton book, or you might have even read Gleick's excellent introduction on the topic. I'd advise you to keep learning on the subject of chaos theory and dynamical systems - as someone who's taught a college-level course in the topic, I can assure that it's a fascinating topic that will be worth your while and will reward you for the efforts you put into it. But it will also keep you from saying things such as these.

 

[Big#D]

Calculation: There is a very strong correlation between the Pythagorean formula, and actual win percentage. Thus, my estimate for how many games a goalie would have won is based on that formula. To calculate it, you need the team’s goals for per game [A], and the team’s shots against per game . To calculate what an average goalie would have done you need to look at the league average save percentage [C]. Plug it in to this formula: A^2 / [(A^2) + [(B^2) * (1-C)]]^2. That shows the expected win percentage based on how a goalie with a league-average save percentage would have performed, given the team’s level of offense and defense.

HO, I have a couple questions for you regarding your assertion that Brodeur was "average" between 1999 and 2004. I don't want to appear to be a homer Devils fan. It's just that as one, I know more about Brodeur than I do any of the other goalies you have analyzed. So on to the questions:

1) Would a goalie who plays "average" on a great team and has awesome stats (SV% and GAA as well as wins) be measured similarly to a goalie who plays "average" on a bad team and has crappy stats? For instance a team like Detroit has won and competed for the Presidents Trophy consistently since the mid-1990's. Would a goalie on that team need to have great stats (SV% > .910 and/or GAA <2.00) to be considered average and have a wins added of +/- 0? Likewise with a bad team (the LA Kings come to mind but I don't want to assume all their years were bad).

2) For a goalie like Brodeur, the period that you mention is one in which the team had some of its best offensive years, leading or coming close to leading the league in GF at the beginning of the period. What affect would that have on Brodeur, given that he was fairly consistent in his play over the years?

3) It appears that your formula focusses on save percentage in comparison to wins when you break down the essential components of the formula. Save percentage has been Brodeur's weakest statistic over almost his entire career when compared to other "elite" goalies. Would this be the greatest contribution to Brodeur being "average" from 1999 to 2004?

4) The New Jersey Devils / CAA arena have been notorious over the years of having low shot totals for and against during games. Given my question in number 3, would this have had a significant affect on Brodeur's rating during his career had his shots against totals increased in numbers to the average (thus inflating his SV%), while not affecting the total games he won during the year?

Thanks in advance if you answer these questions. I thought your analysis was very interesting. Good work.

 

[overpass]

4) The New Jersey Devils / CAA arena have been notorious over the years of having low shot totals for and against during games. Given my question in number 3, would this have had a significant affect on Brodeur's rating during his career had his shots against totals increased in numbers to the average (thus inflating his SV%), while not affecting the total games he won during the year?

Hockey Outsider may want to wish to answer this also, but I'll throw some numbers out there for this.

First, I looked at the home shots against and road shots against for all NHL teams from 2001 to 2008. The numbers are from ESPN, and this source only has them for 01-08, otherwise I would have prefered to look at Brodeur's full career.

On average, teams allow fewer shots at home than they do on the road. I looked at the shots each team faced on the road and then discounted them by the constant home-ice advantage to create an "expected shots faced at home" number.

All numbers in the table are home numbers only. SA is average shots allowed per game, and XSA is expected shots allowed at home based on road shots allowed and home-ice advantage.

Teams that allowed fewer shots at home than expected

Team |SA |XSA| %
Dallas |23.2 |25.4 |91.3%
Minnesota |26.8 |29.0| 92.1%
New Jersey |24.2 |25.7| 94.0%
Chicago |26.6 |28.2 |94.6%
Vancouver |25.8 |27.3 |94.6%

New Jersey did indeed face fewer shots at home than on the road, although there was an even stronger effect in Dallas and in Minnesota.

Let's assume that New Jersey scorekeepers were stingy in recording shots on Brodeur. What if he actually faced the "expected" number of shots based on road shots faced, instead of the records that we have?

Year| Player |Actual Sv%| Adj Sv%
2000-01 |Martin Brodeur |0.906 |0.908
2001-02 |Martin Brodeur |0.906 |0.908
2002-03 |Martin Brodeur |0.914 |0.917
2003-04 |Martin Brodeur |0.917| 0.920
2005-06 |Martin Brodeur |0.911 |0.913
2006-07 |Martin Brodeur |0.922 |0.926
2007-08 |Martin Brodeur |0.920 |0.920

If the stat recorders in New Jersey have been overly stingy in recording shots against Brodeur, it has indeed given him a lower Save % than he should have had. I expect it would add up over a career. However, it's not a huge difference in any one year, and it wouldn't make him look like the best goalie from 1999-2004.

Also, when evaluating Brodeur, remember that he played for a team that consistently had the fewest time shorthanded in the league. Goalies generally have lower save% when shorthanded, so this was to his benefit.

 

[Big#D]

Hockey Outsider may want to wish to answer this also, but I'll throw some numbers out there for this.

If the stat recorders in New Jersey have been overly stingy in recording shots against Brodeur, it has indeed given him a lower Save % than he should have had. I expect it would add up over a career. However, it's not a huge difference in any one year, and it wouldn't make him look like the best goalie from 1999-2004.

Also, when evaluating Brodeur, remember that he played for a team that consistently had the fewest time shorthanded in the league. Goalies generally have lower save% when shorthanded, so this was to his benefit.

Hi. Thanks for the analysis. I wasn't sure the extent to which New Jersey's notoriety was true. All I know is that opposing teams' fans always seem to complain about the number of shots recorded when the came to New Jersey (esp. when they were at the CAA). It looks like it wouldn't make that much difference anyways. Good point that you made about times shorthanded. That probably also contributed to his SV%.

 

[Bear of Bad News]

Another bit of evidence - in Alan Ryder's paper (which I highly recommend for those interested in these sorts of things), he calculates New Jersey opponents' shot quality as 91.5% (for 2002-03, the year he was using for the paper), the lowest in the NHL for that season and quite a bit lower than any other team.

In those terms, the average shot Brodeur faced was 8.5% easier to stop than an average shot faced by a typical NHL goaltender, and his SQNSV (shot-quality neutral save percentage) for that year is 1 - (1 - 0.914) / 0.915, or 90.6%, instead of his actual save percentage of 91.4%.

Here's Alan's research site - http://hockeyanalytics.com/

 

[seventieslord]

Goalies generally have lower save% when shorthanded, so this was to his benefit.

And it was to his benefit before that time, and it has been to his benefit since that time.

On the Brodeur boosters side, however, they can always say he makes a lot of poke checks which don't show up as shots on goal. They can also say that his puckhandling skills result in fewer shots against. This is probably true but it is also difficult to quantify. I would attempt to guess exactly how many instances of puckhandling resulted in a shot being prevented, per game, then multiply that by games played, and look at his save percentage over the years and determine how many of those prevented shots would have been allowed had he not prevented them. It wouldn't be exact, though. And the kicker is really in the original number used, the number of actual shots he is preventing on a nightly basis. You would also need to subtract the number of times an average goalie does this per game so as to not over-reward Brodeur, because if he's preventing 7 shots per game and an average goalie prevents 4, he's really preventing 3 more than an average goalie, not 7. The rest of the formula would write itself. This is a metric that would obviously help Brodeur but I question how significant it could really be in attempting to prove anything significant, i.e. that he is better than Roy or Hasek, arguments that get blown out of the water any time a detailed analysis is done.

There is also the whole "timely save" argument that doesn't hold a lot of water with me. You either make more saves on a regular basis or you don't. No goalie should be saying "OK, i'm not trying that hard on these shots, but later on if the game is close I'm really going to shut the door!" A goalie's real duty is to stop the puck as often as possible.

I think whatever way you slice it, Brodeur is the 3rd best goalie of his generation. He's far behind Roy and Hasek, and every other goalie is far behind him. After all the analysis and discussion on the HOH top-100, that's exactly the conclusion we logically came to. Slotting him in among older goalies gets tricky, though. I personally have him behind Plante, Sawchuk, Dryden, and Hall, but ahead of Brimsek, Broda, Parent, Tretiak, Benedict, Hainsworth, and Durnan.

 

[pitseleh]

Also, when evaluating Brodeur, remember that he played for a team that consistently had the fewest time shorthanded in the league. Goalies generally have lower save% when shorthanded, so this was to his benefit.

In 06/07, I remember reading that Luongo actually had a higher save percentage at ES, PP, and SH than Brodeur, but when you combined the data, because of the discrepancy in times shorthanded Brodeur's save percentage ended up being higher. It'd be interesting to see how the numbers work out if controlled for the number of shots in each scenario.

 

[seventieslord]

How the Formula Works

* Warning this is very technical and boring

Step 1: calculate expected win percentage for a given team-year

Calculation: There is a very strong correlation (about 94%) between the Pythagorean formula, and actual win percentage. Thus, my estimate for how many games a goalie would have won is based on that formula. To calculate it, you need the team’s goals for per game [A], and the team’s shots against per game . To calculate what an average goalie would have done you need to look at the league average save percentage [C]. Plug it in to this formula: A^2 / [(A^2) + [(B^2) * (1-C)]]^2. That shows the expected win percentage based on how a goalie with a league-average save percentage would have performed, given the team’s level of offense and defense.

Example: In 1998, Dominik Hasek played for the Buffalo Sabres. His team scored an average of 2.52 goals per game [A], and allowed 30.6 shots against . The league average save percentage was 90.6% [C]. Plugging these numbers into the formula, a statistically average goalie is expected to win 43.4% of their decisions.

Step 2: calculate the number of games the goalie was expected to win

Calculation: take expected win percentage from step 1 and multiply it by minutes played, divided by sixty.

Example: In 1998, Hasek was expected to win 43.4% of his decisions. He played 4,220 / 60 = 70.3 games so he was expected to win 30.7 games.

Step 3: compare expected wins to actual wins

Calculation: subtract expected wins from actual wins

Example: in 1998, Hasek won 39.5 games but a statistically average goalie only would have won 30.7 games, had they played 70.3 games for the 1998 Sabres. Therefore Hasek singlehandedly won 8.8 games for the Sabres. (That might not sound like a lot, but, to put that into perspective, Buffalo finished 6th in the conference with 89 points. If Hasek was replaced with an average goalie, Buffalo would have lost the ~18 points that Hasek singlehandedly earned them, and they would have fallen to 10th in the conference, well out of the playoffs.

Step 4: adjust to an 82-game schedule

Calculation: take result of Step 3 * 82 games/ length of NHL schedule

Example: in 1998 the schedule was 82 games so no adjustments are necessary

I'm not sure I fully agree with two parts of this formula.

1) I believe that we should be looking at how many actual decisions a goalie had, not how many minutes. For example, in the Hasek example, he played 70.3 games based on his minutes, but adding up his decisions gets you 69 games. His wins per game should be based on wins divided by total decisions, not wins per minutes/60.

2) Ties should count. They're not wins, but they're being treated as being just as bad as losses, if I understand this correctly. A goalie on a team with low offensive numbers and low goals against is going to find themselves at a disadvantage in this formula because they are more likely to have more ties. These ties are being counted as "a game that they didn't win" for the purposes of this exercise.

Please correct me if I'm wrong.