Dec 162010
 

In the Hockey Statistical Analysis world Tomas Vokoun is an interesting case study because depending on how he gets evaluated he either shows up as an very good goalie or in some cases a true elite goalie in the NHL.  Most ways we evaluate goalies has to do with save percentages.  We either look at overall save percentage or even strength save percentage or even even strength game tied save percentage.  Under all of these scenarios Vokoun excels to various degrees.  A recent Behind the Net Hockey Blog post asked several hockey statistic analysts to discuss “elite goalies” and Tomas Vokoun’s name came up frequently.  What is dumbfounding to me is Vokoun’s record because his won-loss record (79-80-25) is notably worse over the past 3 seasons than his backups (32-22-8).  That can’t be a sign of an elite goalie, even if his backups have been relatively good (i.e. Craig Anderson).  One may postulate it is due to facing tougher competition as backup goalies often get the to play against weaker teams or one may postulate it is just due to bad luck.  Or maybe, he just isn’t a great goalie.

Since shots totals and shooting/save percentage is often affected by game score I’ll focus on 5v5 even strength game tied statistics to balance everything out.  Over the last 3 seasons (2007-08 to 2009-10) there are 35 goalies with 1500 or more 5v5 game tied minutes.  Of these goalies, Tomas Vokoun ranks 8th in 5v5 game tied save percentage which may not be elite, but still very good.  Jonas Hiller tops the list with a .942 save % with Vokoun at .933 and Chris Osgood trails the list with a .906 save %.  So, Vokoun looks pretty good.

But, Tomas Vokoun ranks just 23rd in goals against average which isn’t great and probably average at best.  Those who are in love with fenwick numbers will note that Vokoun has the second highest fenwick against of any goalies with 1500+ 5v5 tied minutes and he gives up so many goals because Florida gives up so many shots and scoring chances.  Of course, I believe that not all shots against are equal and shot totals can be influenced by style of play as much as talent.  If you don’t believe style of play affects shot totals and scoring chances, ask yourself why there are score effects on shot/corsi totals?  The answer is depending on the score, teams play differently.  But teams play differently when the score is tied as well.  Some teams play a defense first style, even when game is tied, and others play a more wide open offensive style.  Florida, without any true elite offensive stars, probably plays more of a defensive game which would naturally lead to more shots against, but not necessarily more quality scoring chances against.

So yes, Florida gives up a lot of shots, but how good is Tomas Vokoun’s competition really.  He does play in the weakest division in the NHL and yet he can’t produce a good won-loss record.  Just looking at Vokoun’s opposition, his opponents rank dead last in goals for per 20 minutes so compared to other goalies he is playing against relatively weak opponents offensively.  His oppositions GF% (goals for / goals for + against) is also fourth worst so overall so he plays against very weak opposition in terms of scoring goals and stopping goals.  For those who prefer Fenwick, his opposition has a FF% (fenwick for / fenwick for + against) of .499, good for 27th among the 35 goalies.  So his opposition isn’t good and his performance in goals against average isn’t good either.  That isn’t a good combination if you want to be considered an elite level goalie.

How about a direct comparison with his backups.  In 2007-08 his goals against average per 20 minutes was significantly worse than Craig Anderson’s (0.949 for Vokoun, 0.538 for Anderson) while Anderson’s opponents had a slightly better goals for per 20 minutes (0.678 vs 0.671).  In 2008-09 Vokoun had a much better season giving up 0.697 goals per 20 minutes compared to Anderson’s 0.896 though Anderson played against slightly better offensive competition.  In 2009-10 Vokoun had a much better goals against than Clemmensen (0.621 vs 1.058) but played against weaker competition as well (OppGF20 of .714 vs 0.743 for Clemmensen’s opponents).  Generally speaking Tomas Vokoun had a very weak 2007-08 season but much better 2008-09 and 2009-10 seasons even though he always seemed to play against weaker offensive opponents.

In terms of my Hockey Analysis Ratings, Tomas Vokoun ranked 16th out of 35 goalies in 2007-10 HARD and 18th in 2007-10 HARD+ rankings.  Middle of the pack.  The seasonal breakdown positioned him 35th of 38 in HARD+ for goalies with 500+ minutes in 2007-08, 19th of 35 in 2008-09, and 6th of 37 in 2009-10.  So far this season he is closer to the bottom again.

Is Tomas Vokoun an elite goalie, or even great goalie?  Probably not.  He just posts good save percentages because his team gives up a lot of shots, but not necessarily quality scoring chances, and he plays against weak offensive competition.

Do good teams Create Good Luck? (Updated)

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Dec 032010
 

(Updated to include 3 seasons of data as I now realize that more luck data was available)

The other day there was a post on the Behind the Net Blog which used betting odds to estimate how lucky a team was during the 2009-10 season.  In many ways it is quite an ingenious way to evaluate a teams luck and I recommend those who have not read it go take a look.  Last night I was watching, sadly, the Leafs-Oilers game and thinking about luck in a hockey game and whether a team has any control over the luck they experience.   It got me thinking, does a team which controls the flow of the play mean that team is more likely to have more ‘good luck’ stuff happen to them than ‘bad luck’ stuff.

I defined luck as being how many standard deviations their actual point totals were from their expected point totals as defined in the document referenced in the Behind the Net blog post and in an updated document with 4 years of data.  I have only included 3 seasons in this analysis since I have only been working with 3 seasons of data recently and I was too lazy to go back and calculate a fourth season right now.

The most used stat to indicate how well a team controls the play is corsi or fenwick percentage which is basically the number of shots a team directs at the goal divided by the number of shots that they and their opponents teams directed at the goal.  I’ll be using Fenwick % here which includes shots and missed shots but not blocked shots.  So how does Fenwick % correlate with luck?

The correlation is fairly low but a correlation exists.  Maybe good teams can generate their own luck.  Here is a table of a teams luck and fenwick% for 2009-10.

Team Luck Fen%
Chicago Blackhawks 0.777 0.578
Detroit Red Wings 0.395 0.541
Boston Bruins -0.534 0.536
Pittsburgh Penguins -0.156 0.530
Toronto Maple Leafs -1.282 0.528
New Jersey Devils 0.459 0.522
St. Louis Blues 0.186 0.519
Phoenix Coyotes 2.092 0.515
Nashville Predators 1.225 0.514
Calgary Flames -0.590 0.513
Washington Capitals 1.883 0.512
San Jose Sharks 1.020 0.512
Philadelphia Flyers -1.157 0.511
Ottawa Senators 0.083 0.508
Los Angeles Kings 1.040 0.498
Buffalo Sabres 0.302 0.496
Atlanta Thrashers -0.347 0.496
New York Rangers -0.753 0.495
Vancouver Canucks 0.471 0.495
Carolina Hurricanes -0.555 0.491
New York Islanders -0.201 0.490
Columbus Blue Jackets -0.855 0.488
Dallas Stars -0.212 0.480
Anaheim Ducks -0.087 0.467
Tampa Bay Lightning -0.604 0.466
Florida Panthers -0.726 0.465
Montreal Canadiens 0.052 0.464
Minnesota Wild -0.486 0.459
Colorado Avalanche 0.599 0.449
Edmonton Oilers -1.993 0.446

When I was looking through the table something caught my attention.  Of the bottom 15 teams in Fenwick%, only four teams had positive luck.  These were Buffalo, Vancouver, Montreal and Colorado.  Generally speaking, these four teams had good to very good goaltending.  Of the top 15 teams in Fenwick%, only five teams had negative luck.  These were Boston, Pittsburgh, Toronto, Calgary and Philadelphia.  Boston and Calgary had good to very good goaltending (especially once Boston switched mostly to Rask) but Philadelphia, Pittsburgh and Toronto had mediocre to poor goaltending.  That got me to wondering whether goaltending correlated with luck at all so I took a look at the correlation between 5v5 game tied shooting and save percentages with luck.

Like fenwick%, there is an indication of a small correlation between shooting percentage and luck and there is a bit more of a correlation with save percentage.  Next I looked at combining all three factors.  Initially I was going to look at combining all three through some sort of average but then decided to look at goals for percentage instead (goals for divided by goals for plus goals against) since that basically encompasses everything anyway and we find that combined we get a relatively strong correlation with luck.

Now we are getting into correlation that might actually mean something, but what does it all mean?  To be honest, I am not sure.  Regardless of what ‘skill’ we look at there does seem to be a small positive correlation between how good a team is and how good their luck is (as calculated from the betting lines).  Does this mean that a bad team and especially a team with bad goaltending opens itself up to more bad luck than good teams or teams with good goaltending, or does it mean that luck manifests itself mostly in bad goals against or does it simply mean that the people who bet on hockey games trend towards betting the underdog which would push their expected winning percentage up and good teams expected winning percentage down which would result in a poor estimation of luck?  I am not sure how you determine what the exact cause of the correlation is but if it is the latter I have a word of advice, always bet the favourite.

Aug 132010
 

Who is the best Shooter in the NHL?

If you were asked, who is the best shooter in the NHL you might answer Alexander Ovechkin since he has been the most prolific goal scorer since the lockout.  What Ovechkin also always does though is take far more shots than anyone else resulting in a shooting percentage that is for more ordinary.  This past season he was 50th in overall shooting percentage and in 2007-08 he was 46th and those are the only two times he cracked the top 50.  So is Ovechkin a great shooter, or simply great at finding opportunities to shoot?  And if Ovechkin isn’t the best shooter, who is?

Shooting percentage is a very common statistic which essentially is just goals scored divided by shots taken.  We all know and understand that.  Corsi numbers were initially conceived by former NHL goalie and current Buffalo Sabre goalie coach Jim Corsi as a method of evaluating goalie fatigue and has since become a frequently discussed statistic among hockey stat nuts, particularly those at Behind the Net.  Essentially what Corsi takes into account is shots directed at the net, not just shots on the net.  So, Corsi also takes into account missed shots (i.e. shots that go wide) and blocked shots (i.e. shots blocked by a defender).  Corsi numbers are often considered a good indicator of which team controls the play more (if you control the play you will get more shots and shot attempts than your opponent).  Corsi numbers were then revised by Matt Fenwick from the Battle of Alberta blog to not include blocked shots as it was found that including blocked shots in Corsi numbers correlation with winning percentage.  So it came to be that shots plus missed shots are generally referred to as Fenwick numbers and shots plus missed shots plus blocked shots are generally referred to as Corsi numbers.  That is the terminology I will use here.

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My thoughts on Corsi Numbers

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Jun 032010
 

I am planning that over the course of the summer and into next season I will get back into analyzing hockey statistics more in depth again.  Over the past couple of seasons Corsi numbers have become much more prevalent so I thought I would start off by discussing what they are and my thoughts on them.

Corsi numbers were originally created by former NHL goalie and now Buffalo Sabre goalie coach Jim Corsi.  David Staples recently had a good interview with Corsi which goes into his thought process behind developing Corsi numbers.  The interview is definitely worth a read but let me summarize.

In his role as the Sabre’s goalie coach, Corsi was attempting to evaluate the work load his goalies had in a game of play and found that simply shots against were not sufficient.  The goalie can relax whenever the puck is in the oppositions end, but whenever the play is in his own end he can’t relax, regardless of whether a shot was taken or not.  To get a better idea of his goalies workload he summed up shots, missed shots and blocked shots which should give a much better indication of a goalies overall work load.  A goalie needs a certain skill level to successfully save the majority of shots on goal, but a goalie also needs a certain fitness level (both mental and physical) to be able to play under a certain workload level within a single game and over the course of an 82 game season and this is why Corsi invented the Corsi numbers.

More recently others in the hockey community have extended Corsi numbers to evaluate a teams ability to control the play of a game (i.e. does a team play more in the oppositions zone vs their own) and evaluate individual players by looking at their Corsi numbers for and against while they are on the ice and comparing that to their teammates Corsi numbers.  Most notable are Gabe Desjardins of behindthenet.ca and Gabe and everyone else at the Behind the Net blog but there are others too.  Some people, most notably Matt Fenwick of the Battle of Alberta blog only use shots and missed shots and do not include blocked shots as Jim Corsi does resulting in what is typically called Fenwick numbers.  When used in this context Corsi and Fenwick numbers are calculated just as +/- is calculated which is to take the shots+missed shots+ blocked shots for his team and subtracting the shots+missed shots+ blocked shots numbers by the opposition while he is on the ice.

One of the benefits that many people believe that Corsi numbers provide is that since Corsi numbers include more events (i.e. shots+missed shots+blocked shots vs just shots or even just goals as in +/-) the statistical analysis will be far more accurate due to the larger ‘sample size.’

So what do I think of all this?  I do agree with Jim Corsi that using Corsi numbers as a way to evaluate a goalies workload is probably far more valuable than just using shots on goal.  Beyond that, I am pretty sure that Corsi numbers will give a pretty solid indication of a teams control of the play, for whatever that is worth.  I say for whatever that is worth because some teams, when they have the lead, will choose to play in a defensive shell allowing a lot of shots from the point, but not giving up all that many high quality, in close, shots or worse yet, shots on rebounds. Corsi numbers when the game is close (tied, or within one goal with significant time to play such that the team with the lead has not yet gone into ‘protect the lead’ mode) may give us a better indication of a teams capability to control the play, when they want to but even that may be flawed.  Also, a team with a strong set of forwards but a weak defense and goalie may control the play more than a team with a strong defense and top tier goalie but is that team really any better at winning games?

Much of the same arguments can be made when evaluating players.  Defensive minded players are not necessarily on the ice to control the play, they are on the ice to not allow goals against most typically by the oppositions top offensive forwards.  As mentioned above, one way to accomplish this is to go into a defensive shell and just not give up any quality scoring chances against.  A player can have a sub-par Corsi number, but be doing his job perfectly well.

I do believe that Corsi numbers have a use in evaluating a goalies work load and even in showing which teams are controlling the play, but in my opinion using it anywhere beyond that we are making too many assumptions about how important Corsi numbers are with respect to winning games.  Just ask the Washington Capitals how almost completely controlling the play worked for them against Montreal in round two of the playoffs. In the past I have used mostly goals for/against and shot quality (using shot type and distance as a proxy for quality) to evaluate players and while that has its own inherent flaws as well I will most likely continue to do so in the future.

How do Goalie Age Part III

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Apr 292010
 

If you have not yet read Part I and Part II of this series, you should probably do that now so you will better understand Part III.

In this part I wanted to take a look at individual goalies and see how they compare to the average. The following is a list of the 19 goalies I used to create the goalie performance by age average chart that you see in Part II and if you click on their names you will be shown a chart of their performance compared to the average performance.

Chris Osgood
Chris Tererri
Craig Billington
Curtis Joseph
Dominik Hasek
Ed Belfour
Glenn Healy
Jeff Hackett
Jocelyn Thibault
John Vanbiesbrouk
Kirk McLean
Martin Brodeur
Mike Richter
Mike Vernon
Olaf Kolzig
Patrick Roy
Ron Tugnutt
Sean Burke
Tom Barasso

I’ll leave it as an exercise to the reader to go through each of the charts and draw whatever conclusions you can but there appear to be two different charts. The first is a typical curved chart where a goalie improves early in his career and tails off later in his career. There are of course varying degrees of this curve from the extreme like Jeff Hackett to a more moderate curve like Ron Tugnutt. The other type of chart which is quite common is the one where the goalie enters the league at quite a high level and then tails off over time. Again there are varying degrees as to which this tail off occurs from the extreme in Jocelyn Thibault or Kirk McLean to a more casual drop of as with Mike Vernon.

It is generally believed that the two best goalies of the past 20-25 years are Patrick Roy and Martin Brodeur. Not as often mentioned is Dominik Hasek and I can only assume that is because he doens’t have the win totals or Stanley Cups of the other two partly because of the teams he played for and partly because he started his career late compared to Roy and Brodeur. But, in the prime of his career he was truly dominating and in my opinion was by far the best goalie in the NHL through the late 1990’s and into the current decade. So, lets take a look at how these three stack up against each other.

Clearly all three have been better than the average of the other 19 goalies, but I was actually a little surprised to see how little better Brodeur has been for much of his career. Four times in Brodeur’s career has he performed below the average of the other 19 goalies at the same age where neither Roy or Hasek ever performed below the group average at any age. The other conclusion one must draw from this chart is simply how good Hasek was, particularly late in his career. Roy had dominating years in his early to mid 20’s but from age 29 on clearly Hasek was the more dominant goalie. As for Brodeur, he has had a few excellent seasons but generally speaking has been a step below the Roy and Hasek at all ages. The only other goalie who could possibly be considered as a similar talent to these three goalies is Ed Belfour who you could argue had a career quite similar to that of Brodeur.

In part IV, which I’ll either post tomorrow or early next week, I’ll take a look at a few current goalies in the middle of their careers to see if we can gain any insight into what phase of their career they are in and what the future might hold for them.

Apr 282010
 

Earlier today I posted an article showing how a goalies save percentage varies by age. It was pointed out that one of the flaws in that analysis is that I didn’t account for the fact that over time the average NHL save percentage has varied, and has generally increased over time. In fact, the change from the 1980’s to the 1990’s is quite significant. As a result I decided it was important enough to take the next step and account for variations in league wide save percentages.

To accomplish this I took each goalies save percentage and divided it by the league wide save percentage for that year which essentially tells us how much a goalie was better or worse than his peers in that given year. Anything greater than 1 meant the goalie was better than the average goalie and anything less than 1 meant the goalie was not as good as the average goalie that year. I then performed the same analysis using this ratio number instead of straight save percentage.

How do Goalie Age

The end result is that a goalies peak years generally start sooner than seen under the straight save percentage analysis and the drop off in a goalies latter years is more pronounced as well. Generally speaking a goalie will have his best years between ages 22 and 34 after which the drop off is fairly pronounced. This isn’t true for all goalies though as the truly elite goalies such as Roy, Belfour, Hasek and Brodeur played above their peers well beyond age 34 but for the majority of goalies it is downhill once you get past your early 30’s.

Note: In the above chart I only included ages for which data was available for at least 3 goalies and I only included years where a goalie played at least 5 games. This was done so as to not skew the chart at the edges and the result is only ages 19-41 are shown though Barasso played at age 18 and Hasek played until age 43.

At look at the Importance of Goaltending

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Apr 152010
 

I have always believed that goaltending is by far the most important position in hockey and have claimed it can make or break any teams season. I have claimed that the main reason that the Leafs have failed to make the playoffs post lockout is because of bad goaltending. Many others have scoffed at this claim blaming everything from bad defense to bad offense (which is mostly not factually true) to poor coaching, to a combination of all of the above. I have seen others claim that goaltending would account for at most four or five games a year. So, I have undertaken a bit of a study to attempt to figure out how important goaltending really is and how many points in the standings poor goaltending can cost you or great goaltending can gain you.

Most goaltending studies I have seen, and done myself, have to do with comparing goalies from one team to the next. The problem with this is people can easily choose to dismiss the study with claims like ‘but team x has such a bad defense you cannot blame the goalie for that’ and to some extent there is some validity in this claim (though I do not believe it to be as much as many do). There have been other studies that attempt to factor out the defense issue by coming up with some sort of shot difficulty rating based on shot type and defense. I believe that this has some merit and improves the validity of the study but people will simply jump in and claim that not all shots from the same distance are equal and teams with bad defense will inherently give up more difficult shots so the shot quality analysis is still far from perfect. Again, there is some merit to this.

So, with all that in mind I set out to study goaltending in a way that eliminates the quality of a team’s defense in a way that most sane people cannot dispute: compare goalies who play on the same team. If we are comparing two goalies who play on the same team we immediately eliminate the ‘but he plays on a bad team’ argument because they are playing behind the same players.

I collected all the goalie statistics from the 5 regular seasons since the NHL lockout of the 2004-05 season. For each team and season I identified each team’s starting goalie (the goalie with the most starts) and then grouped all other goalies who played for that team in that season and merged their statistics into a combined backup goalie statistic. For example, this past season Jonas Hiller was the starter for the Anaheim Ducks and JS Giguere and Curtis McElhinney also played for the Ducks so Giguere and McElhinney’s stats were combined into a single team backup stat. The statistics I am interested in are save percentage and points earned for their team and the number of games started from which we can calculate points earned per start stat for the starter and the combined backup. I next subtracted the backups points per start from the starters points per start and the backups save percentage from the starters save percentage. Here is an example for the Anaheim goalies.

Goalie Starts W L OTL PTS Pts/Start SV%
Jonas Hiller 58 30 23 4 64 1.103 0.9182
JS Giguere 17 4 8 5 11 0.9000
C. McElhinney 7 5 1 2 10 0.9167
Backups 24 9 9 3 25 1.042 0.9055
Starter-Backup 0.0618 0.0128

So, from that table we see that Jonas Hiller had a save percentage 0.0618 higher than his backups and produced more points for his team in the standings at a rate of 0.0128 per start. Now since that last stat is pretty meaningless I prorated it to 82 games and over the course of 82 games Hiller would produce 5.07 additional points in the standings over his backups.

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