Jan 282012
 

I have been having a discussion as to whether shot quality exists over at Pension Plan Puppets and more precisely whether certain players can drive a teams shooting percentage while they are on the ice.  As part of the discussion I brought up the on-ice shooting percentage differences between Scott Gomez and Michael Cammalleri and decided that it would be useful to present that comparison as a post here.

First off, let me define shot quality as how I see it.  Shot quality is an ability for players to systematically drive (or suppress) shooting percentages when they are on the ice.  To me it doesn’t matter whether they can drive shooting percentages because they can get more shots from better shooting locations, or are better shooters, or are better playmakers setting up  changes with the goalie out of position.  Those are interesting things to investigate, but investigating them isn’t necessary to show shot quality exists.  Shot quality, in my mind, is all about a players being able to drive (or suppress) shooting percentage when they are on the ice, regardless of how.

In the past I have used examples such as Henrik Sedin vs Travis Moen and some comments I got were “but those are extreme cases” which is an interesting comment because in essence they person making that argument is admitting that shot quality exists but only in extreme cases.  So, I decided that it might be useful to take a look at two players who generally speaking play similar roles.  Scott Gomez and Michael Cammalleri.  Both Gomez and Cammalleri are top six forwards generally thought of as more offensive players.  What is also interesting is they over the past 4 1/2 seasons they both have switched teams and they have both spent a couple years playing on the same team, sometimes on the same line.    Let’s take a look at their 5v5 on-ice shooting percentages over the past 4 1/2 seasons.

 Sh% Gomez Cammalleri Difference
2007-08 7.09 8.15 1.06
2008-09 6.15 9.25 3.10
2009-10 7.89 9.66 1.77
2010-11 4.50 7.07 2.57
2011-12 7.96 8.11 0.15

In each and every season Cammalleri has had a higher shooting percentage, sometimes much higher.  Only this season have they been close in their on-ice shooting percentages.  If that isn’t a systematic ability by Cammalleri and his linemates to get a higher shooting percentage than Gomez and his linemates, I don’t know what is.  They can do it every singles season.

Now, let’s take a look at their offensive fenwick rates.  Here are their fenwick for per 20 minutes of 5v5 ice time rates.

 FF20 Gomez Cammalleri Difference
2007-08 15.86 14.3 -1.56
2008-09 16.76 15.38 -1.38
2009-10 14.21 13.4 -0.81
2010-11 16.4 14 -2.4
2011-12 16.8 12.06 -4.74

Well now, that tells us a different story.  Gomez and his line mates take far more shots than Cammalleri and his line mates, and they do it every single season.  Gomez and his line mates seem to have a much better skill at taking shots, but Cammalleri and his line mates seem to have a much better skill at capitalizing on shots.  The question now is, which skill results in more goals.  Here are their 5v5 goals for per 20 minute stats.

 GF20 Gomez Cammalleri Difference
2007-08 0.792 0.801 0.009
2008-09 0.757 1.020 0.263
2009-10 0.837 0.927 0.090
2010-11 0.534 0.713 0.179
2011-12 0.854 0.756 -0.098

Now that is interesting.  Cammalleri and his line mates have out produced Gomez and his line mates every year until this season.  Based on this one example, being able to drive shooting percentage resulted in more goals being scored than being able to drive shots.  If you were down by a goal in the third period, who would you rather have on the ice, Gomez and his line mates or Cammalleri and his line mates?

And the above is a perfect example of why I don’t like pure corsi/fenwick based evaluation of players.  If you just look at corsi/fenwick, Gomez looks like a very good player (see here and here), and Cammalleri does not.  But, if you look at goals, over the past 2 seasons 54.1% of all goals scored while Cammalleri was on the ice were for the Canadiens while just 47.2% of all goals scored while Gomez was on the ice were for the Canadiens.  Who is the better player, and who would I rather have on my team?  Cammalleri by a country mile.

Let’s take it one step further and how they played when they were on the ice together and when they were apart over the past 2 seasons.

Together Cammalleri Gomez
GF% 54.8% 53.9% 45.4%
Corsi% 52.3% 47.9% 51.6%

Wow, that is dramatic.  When they play together can an drive shots (corsi) and goals.  When Cammalleri is not playing without Gomez he can drive goals, but not shots (corsi) and when Gomez is playing without Cammalleri he can drive shots (corsi) but not goals.  Again, who would you rather have on your team?  For me, I’ll take the guy who can drive goals thank you very much.

And that my friends, is a perfect example of when a corsi based analysis will fail.

 

Dec 092011
 

Gabe Desjardins of Arctic Ice Hockey asks the question about whether a player can influence his teammates shooting percentage.  To answer this question he took a look at the Pittsburgh Penguins shooting percentages with and without Mario Lemieux.  The conclusion:

I’d posit that Lemieux’s playmaking contribution is about as large as we’re going to consistently find – something on the order of 7-8% – and we can use it to bound the impact that a player can truly have on the quality of his teammates’ scoring chances.

Since I have the numbers handy I figured I’d take a look at some more recent examples but instead of looking at straight shooting percentage I looked at corsi shooting percentage (since I had corsi data more available).  Corsi shooting percentage is simply goals for divided by corsi for.  I’d consider Joe Thornton one of the premiere playmaking centers in the league today so let’s take a look at how some players performed while playing with, and without, him.

CSH% With Thornton CSH% Without Boost
Marleau 4.93% 3.88% 27.02%
Setogutchi 5.09% 3.80% 33.82%
Heatley 5.59% 4.32% 29.50%

I included the past 4 years with Marleau, 3 years with Setogutchi and 2 years with Heatley.  That would indicate Thornton has an approximately 30% boost in corsi shooting percentage to his teammates.  Certainly far more than the 8% Gabe predicted as the upper bound.

Now, let’s take a look at another great player, Sidney Crosby.

CSH% With CSH% Without Boost
Malkin 7.30% 5.15% 41.89%
Dupuis 5.73% 4.06% 40.95%
Fleury 5.75% 4.22% 36.15%

All players are using 4 years of data.  I included Fleury in the list because it provides a good proxy of the Penguins shooting percentages when Crosby is on the ice vs when he is not.  This would seem to indicate that Crosby is worth a nearly 40% boost in his teams shooting percentage.  That’s significantly more than even Thornton and a massive amount more than Gabe’s estimated upper bound.  Maybe we should revise the upper bound to be 40%, not 8%.

For interest sake, here how much Crosby influenced his teammates corsi rates.

Boost in CF20
Malkin 21.15%
Dupuis 18.15%
Fleury 15.59%

While a ~20% boost is significant, it is at best only half the boost he provided to corsi shooting percentage.  Driving shooting percentage is a more significant reason why Crosby is so good offensively than driving corsi events.

 

Update:  Eric over at Broad St. Hockey has an interesting post looking at individual shooting percentages as opposed to on-ice shooting percentages as I did above.  Four of the players he looked at are H. Sedin, Crosby, Thornton and Datsyuk and for each he looked at a number of teammates with at least 30 shots with and without.  Taking it a step further I think it is necessary to average across players to get a better idea of what is happening.  If you do that, this is what you get:

With Without Boost
Sedin 11.19% 6.81% 64.28%
Crosby 9.14% 7.55% 21.17%
Thornton 9.69% 7.27% 33.19%
Datsyuk 9.36% 6.98% 34.09%

Wow, that might make Sedin the best playmaker in the league, by a significant margin.  Crosby doesn’t look quite as good as my “on-ice” analysis but that is because much of the reason why Crosby improves his linemates on-ice shooting percentage is because he is such a great shooter himself.

The point still stands, without considering shooting percentages we aren’t getting anywhere close to having a complete analysis of a players impact on the game.

 

Nov 222011
 

I hate to keep beating the “Shooting Percentage Matters” drum but it really dumbfounds me why so many people choose to ignore it, or believe it is only a small part of the game and not worth considering and instead focus their attention on corsi/fenwick, and corsi/fenwick derived stats as their primary evaluation too.

It dumbfounds me that people don’t think players have an ability to control shooting percentage yet we all seem to agree that shooting percentage is affected by game score.  Rob Vollman wrote the following in a comment thread at arctic ice hockey.

<blockqote>The score can affect the stats because teams behave differently when chasing or protecting a lead…</blockquote)

He isn’t specifically referring to shooting percentage, but shooting percentage varies based on game score and I think most people accept that.  So, while people freely accept that teams can play differently depending on score, they seemingly choose not to believe that players can play different depending on their role, or skillset.  Or rather, it isn’t that they don’t believe players can play differently (for example they realize there are defensive specialists) they just choose not to accept that a players style of play (in addition to their talents, which often dictates their style of play) will affect their stats, including shooting percentage.  An example, which I brought up at The Puck Stops Here is Marian Gaborik vs Chris Drury.  Both Gaborik and Drury played the past 2 seasons on the NY Rangers but Gaborik played an offensive role and Drury generally played a more defensive/3rd line role.  As a result, here are their offensive stats at 5v5 over the past 2 seasons.

Gaborik Drury Gaborik’s Edge
Team Fenwick For per 20min WOI 13.8 12.8 +8%
Team Sh% For WOI 10.26% 6.18% +66%
Team Goals For per 20 min WOI 1.031 .575 +79%

Shooting percentage took what was a slight edge for Gaborik in terms of offensive fenwick for and turned it into a huge advantage in goals for.  Part of that is Gaborik and his line mates better skill level and part of it is their aggressive offensive style of play, but regardless of why, we need to take shooting percentage into account or else we will undervalue Gaborik at the offensive end of the rink and over value Drury.

It isn’t just Gaborik and Drury whose offense is significantly impacted by shooting percentage.  It happens all the time.  I took a look at all players that had 2000 5v5 even strength on-ice offensive fenwick events over the past 4 seasons.  From there I calculated their expected on-ice goals scored based on their ice time using league-wide average  on-ice fenwick for per 20 minutes (FF20) and league-wide average fenwick shooting percentage (FSH%).

I next calculated an expected goals based on the league-wide FF20 and the players FSH% as well as an expected goals based on the players FF20 and the league-wide average FSH%.  When we compare these expected goals to the expected goals based solely on the league-wide average we can get an idea of whether a players on-ice goal production is driven mostly by FF20 or FSH% or some combination of the two.

The following players had their on-ice 5v5 goal production influenced the most positively or most negatively due to their on-ice 5v5 FSH%.

Player Name %Increase from FSH%
MARIAN GABORIK 40.6%
SIDNEY CROSBY 36.3%
ALEX TANGUAY 33.1%
HENRIK SEDIN 32.8%
BOBBY RYAN 32.5%
EVGENI MALKIN 31.9%
DANIEL SEDIN 31.6%
ILYA KOVALCHUK 30.6%
NATHAN HORTON 29.6%
J.P. DUMONT 29.4%
GREGORY CAMPBELL -12.4%
RYAN CALLAHAN -13.9%
RADEK DVORAK -15.6%
CHRIS DRURY -16.8%
SEAN BERGENHEIM -19.4%
SCOTT GOMEZ -19.7%
MARTIN HANZAL -21.5%
MIKE GRIER -21.5%
DANIEL WINNIK -24.5%
TRAVIS MOEN -32.1%

And the following players had their on-ice 5v5 goal production influenced the most positively or most negatively due to their on-ice 5v5 FF20.

Player Name %Increase from FF20
HENRIK ZETTERBERG 24.7%
ALEX OVECHKIN 21.7%
PAVEL DATSYUK 20.6%
TOMAS HOLMSTROM 19.9%
NICKLAS BACKSTROM 19.8%
ERIC STAAL 19.7%
RYANE CLOWE 18.8%
ALEXANDER SEMIN 18.3%
SCOTT GOMEZ 18.0%
ZACH PARISE 17.9%
MARTY REASONER -6.5%
ANDREW COGLIANO -6.5%
ANTTI MIETTINEN -6.7%
KYLE BRODZIAK -7.3%
CHRIS KELLY -8.6%
ILYA KOVALCHUK -9.8%
JAY MCCLEMENT -10.4%
MICHAL HANDZUS -14.4%
JOHN MADDEN -14.5%
TRAVIS MOEN -15.6%

Some interesting notes:

  1.  The range in the influence of FSH% is significantly larger than the range of influence of FF20 indicating that shooting percentage is more important than shot generation in terms of scoring goals.
  2. The FSH% list is not random.  The list is stratified.  Offensive players at the top, non-offensive players at the bottom (plus Scott Gomez who gets offensive minutes, but sucks).  What you see above is not luck.  There is order to the list, not randomness.
  3. Speaking of Gomez, he sucks at on-ice FSH%, but has a very good FF20, though that is partly due to offensive zone start bias.
  4. Ilya Kovalchuk is the anti-Gomez.  He has a great FSH%, but is horrible at helping his team generate shots.
  5. The standard deviation of the FSH% influence is 14.5% while it is 8.3% for FF20 influence so it seems FSH% has a much greater influence on scoring goals than FF20.  This is not inconsistent with some of my observations in the past or observations of others.

So, what does all this mean?  Shooting percentage matters, and matters a lot and thus drawing conclusions based solely on a corsi analysis is flawed.  It isn’t that generating shots and opportunities isn’t important, but that being great at it doesn’t mean you are a great player (Gomez) and being bad at it doesn’t make you a bad player (Kovalchuk).  For this reason I really cringe when I see people making conclusions about players based on a corsi analysis.  A corsi analysis will only tell you how good he is at one aspect of the game, but is not very good at telling you the players overall value to his team.  My goal is, and always will be, to try and evaluate a players overall value and this is why I really dislike corsi analysis.  It completely ignores a significant, maybe the most significant, aspect of the game.  Furthermore, I believe that offensive ability and defensive ability should be evaluated separately, which many who do corsi analysis don’t do or only partially or subjectively do.

I really don’t know how many different ways I can show that shooting percentage matters a lot but there are still a lot of people who believe players can’t drive or suppress shooting percentage or believe that shooting percentage is a small part of the game that is dwarfed by the randomness/luck associated with it (which is only true if sample size is not sufficiently large).  The fact is corsi analysis alone will never give you a reliable (enough to make multi-million contract offers) evaluation of a players overall ability and effectiveness.  Shooting percentage matters, and matters a lot.  Ignore at your peril.

 

Oct 272011
 

There has been a fair bit of discussion going on regarding shot quality the past few weeks among the hockey stats nuts.  It started with this article about defense independent goalie rating (DIGR) in the wall street journal and several others have chimed in on the discussion so it is my turn.

Gabe Desjardins has a post today talking about his hatred of shot quality and how it really isn’t a significant factor and is dominated by luck and randomness.  Now, generally speaking when others use the shot quality they are mostly talking about thinks like shot distance/location, shot type, whether it was on a rebound, etc.  because that is all data that is relatively easily available or easily calculated.  When I talk shot quality I mean the overall difficulty of the shot including factors that aren’t measurable such as the circumstances (i.e. 2 on 1, one timer on a cross ice pass, goalie getting screened, etc.).  Unfortunately my definition means that shot quality isn’t easily calculated but more on that later.

In Gabe’s hatred post he dismisses pretty much everything related to shot quality in one get to the point paragraph.

 

Alan’s initial observation – the likelihood of a shot going in vs a shooter’s distance from the net – is a good one.  As are adjustments for shot type and rebounds.  But it turned out there wasn’t much else there.  Why?  The indispensable JLikens explained why – he put an upper bound on what we could hope to learn from “shot quality” and showed that save percentage was dominated by luck.  The similarly indispensable Vic Ferrari coined the stat “PDO” – simply the sum of shooting percentage and save percentage – and showed that it was almost entirely luck.  Vic also showed that individual shooting percentage also regressed very heavily toward a player’s career averages.  An exhaustive search of players whose shooting percentage vastly exceeded their expected shooting percentage given where they shot from turned up one winner: Ilya Kovalchuk…Who proceeded to shoot horribly for the worst-shooting team in recent memory last season.

So, what Gabe is suggesting is that players have little or no ability to generate goals aside from their ability to generate shots.  Those who follow me know that I disagree.  The problem with a lot of shot quality and shooting percentage studies is that sample sizes aren’t sufficient to draw conclusions at a high confidence level.  Ilya Kovalchuk may be the only one that we can say is a better shooter than the average NHLer with a high degree of confidence, but it doesn’t mean he is the only one who is an above average shooter.  It’s just that we can’t say that about the others at a statistically significant degree of confidence.

Part of the problem is that goals are very rare events.  A 30 goal scorer is a pretty good player but 30 events is an extremely small sample size to draw any conclusions over.  Making matters worse, of the hundreds of players in the NHL only a small portion of them reach the 30 goal plateau.  The majority would be in the 10-30 goal range and I don’t care how you do your study, you won’t be able to say much of anything at a high confidence level about a 15 goal scorer.

The thing is though, just because you cannot say something at a high confidence level doesn’t mean it doesn’t exist.  What we need to do is find ways of increasing the sample size to increase our confidence levels.  One way I have done that is to use 4 years of day and instead of using individual shooting percentage I use on-ice shooting percentage (this is useful in identifying players who might be good passers and have the ability to improve their linemates shooting percentage).  Just take the list of forwards sorted by on-ice 5v5 shooting percentage over the past 4 seasons.  The top of that list is dominated by players we know to be good offensive players and the bottom of the list is dominated by third line defensive role players.  If shooting percentage were indeed random we would expect some Moen and Pahlsson types to be intermingled with the Sedin’s and Crosby’s, but generally speaking they are not.

A year ago Tom Awad did a series of posts at Hockey Prospectus on “What Makes Good Players Good.”  In the first post of that series he grouped forwards according to their even strength ice time.  Coaches are going to play the good players more than the not so good players so this seems like a pretty legitimate way of stratifying the players.  Tom came up with four tiers with the first tier of players being identified as the good players.  The first tier of players contained 83 players.  It will be much easier to draw conclusions at a high confidence level about a group of 83 players than we can about single players.  Tom’s conclusions are the following:

The unmistakable conclusions from this table? Outshooting, out-qualitying and out-finishing all contribute to why Good Players dominate their opponents. Shot Quality only represents a small fraction of this advantage; outshooting and outfinishing are the largest contributors to good players’ +/-. This means that judging players uniquely by Corsi or Delta will be flawed: some good players are good puck controllers but poor finishers (Ryan Clowe, Scott Gomez), while others are good finishers but poor puck controllers (Ilya Kovalchuk, Nathan Horton). Needless to say, some will excel at both (Alexander Ovechkin, Daniel Sedin, Corey Perry). This is not to bash Corsi and Delta: puck possession remains a fundamental skill for winning hockey games. It’s just not the only skill.

In that paragraph “shot quality” and “out-qualitying” is used to reference a shot quality model that incorporates things like shot location, out-finishing is essentially shooting percentage, and outshooting is self-explanatory.  Tom’s conclusion is that the ability to generate shots from more difficult locations is a minor factor in being a better player but both being able to take more shots and being able to capitalize on those shots is of far greater importance.

In the final table in his post he identifies the variation in +/- due to the three factors.  This is a very telling table because it tells it gives us an indication of how much each factors into scoring goals.  The following is the difference in +/- between the top tier of players and the bottom tier of players:

  • +/- due to Finishing:  0.42
  • +/- due to shot quality:  0.08
  • +/- due to out shooting:  0.30

In percentages, finishing ability accounted for 52.5% of the difference, out shooting 37.5% of the difference and shot quality 10% of the difference.  Just because we can’t identify individual player shooting ability at a high confidence level doesn’t mean it doesn’t exist.

If we use the above as a guide, it is fair to suggest that scoring goals is ~40% shot generation and ~60% the ability to capitalize on those shots (either through shot location or better shooting percentages from those locations).  Shooting percentage matters and matters a lot.  It’s just a talent that is difficult to identify.

A while back I showed that goal rates are better than corsi rates in evaluating players.  In that study I showed that with just 1 season of data goal for rates will predict future goal for rates just as good as fenwick for rates can predict future goal for rates and with 2 years of data goal for rates significantly surpass fenwick for rates in terms of predictability.  I also showed that defensively, fenwick against rates are very poor predictors of future goal against rates (to the point of uselessness) while goals against rates were far better predictors of future goal against rates, even at the single season level.

The Conclusion:  There simply is no reliable way of evaluating a player statistically at even a marginally high confidence level using just a single year of data.  Our choices are either performing a Corsi analysis and doing a good job at predicting 40% of the game or performing a goal based analysis and doing a poor job at predicting 100% of the game.  Either way we end up with a fairly unreliable player evaluation.  Using more data won’t improve a corsi based analysis because sample sizes aren’t the problem, but using more data can significantly improve a goal based analysis.  This is why I cringe when I see people performing a corsi based evaluation of players.  It’s just not, and never will be, a good way of evaluating players.

 

Aug 212011
 

I have just updated my stats site (stats.hockeyanalysis.com) to include a number of new features.  The added features are:

1.  I have added a new situation – 5v5close.  5v5close is when the game is tied or within 1 goal in the first and second period or tied in the third period.  This is what I would call normal play where teams are more or less (depending on talent or game play/coaching style) equally interested in  playing offense or defense.  When teams get a larger lead or lead late in the game teams adjust their style of play to either protect that lead or go all out to score a goal to catch up.  It is probably better to use this than 5v5tied and maybe better than 5v5 (all 5v5 game score situations).

2.  I have included zone start data in the form of OZOF%, DZOF% and NZOF%.  OZOF% is the percentage of face offs taken in the offensive zone when the player is on the ice and DZOF% and NZOF% are the same for defensive zone and neutral zone faceoffs.  When we look at these by situation we can get an idea of how a players use gets changed by game score.  For example, last year Manny Malholtra had 38.8% of his 5v5 face offs in the defensive zone (29.1% offensive zone and 32.1% neutral zone) but when the Canucks were up by a goal his defensive zone faceoffs rose to 41.6% and when the Canucks were up by 2 goals they rose to 48.4%.

3.  I have once again put up with/against statistics for each player.  I had this data up a few years ago but when I re-designed my website I removed it but it is back.  Each player page (i.e. the Malhotra one linked to above) has a set of links at the top of the page to with/against statistics for each season (and multi-seasons) for 5v5 and 5v5 close situations for both goal and corsi data.  Each page shows how the player played with each teammate as well as how they played when they were not playing together as well as how the player performed against each opponent and how well the player and the opponent performed when not playing together.  These tables can give you an indication of which players are playing together and which players play well together as well as who a player plays against the most.  As an example, take a look at Manny Malhotra 5v5 goal with/against data for this past season and you will see he played the most with Raffi Torres (even more than with Roberto Luongo!) but it seems both players had better on ice results when apart.

4.  If you hadn’t noticed yet, a while back I added on ice shooting percentage (Sh%) and on ice opposition shooting percentage (OppSh%, subtract from to get on ice save %) which can be found with the goal data (but not with corsi, fenwick and shot data).

All totaled, there is well over 10 gigabytes of html, php and data base files of statistics (90% of which is in the with/against tables) so be warned, if you really wanted to you could spend days looking at it all.

Jul 192011
 

An interesting statistical debate sprung up today started by Tom Benjamin who wrote about his skepticism of the Corsi statistic.  In it Tom comments on the fact that Ryan Kesler and Ryan Clowe ranked so highly in corsi in response to Greg Ballentine’s posts at The Puck Stops Here.

Greg’s examples, it seems to me, make a good case against the Corsi statistic. First, both the Kesler and Clowe stories tell us how much influence context has – neither Kesler nor Clowe could have done it playing on a different team or even playing in a different role on the same team. In other words, this is not really an individual statistic.

Of course, this got some in the Corsi crowd up in arms and states that Corsi can’t be used on its own without considering its context.  Gabe Desjardin’s comments on Tom’s post with the following:

Corsi, like any other statistic, needs to be understood in the context of other factors. Sneering at it because, like any other simple statistic, it doesn’t provide a unified measure of a player’s complete value doesn’t contribute anything to the larger discussion of hockey analysis.

Ok, so I am glad we have that cleared up.  Corsi is just a stat without meaning unless you consider the context.  Oh good, now it is on par with nearly every other stat in hockey.  Unfortunately people actually use corsi to actually draw conclusions about players.

Here is the thing that really irks me about some in the Corsi crowd.  They just assume that shot quality doesn’t exist.  An anonymous commenter using the name ‘Name’ writes the following:

Even the basis of corsi, that shot quality always evens out, so we just have to measure shot quantity, is inherently flawed. The strategies and styles some teams play lead to giving up a greater number of shots, but reducing quality ones, whereas some teams strive to block or prevent every single shot, no matter where it comes from. Therefore some players, just by playing on a certain time, will inherently be on the ice for more shots against. It doesn’t mean they’re giving up more quality scoring chances, or making lots of defensive mistakes, or failing to control play offensively.

To this comment Gabe comes back with his favourite response to any challenge put his way:

Name these teams and players. Thanks.

Now that is a fair response, unfortunately he ignores anyone who actually names these players.  The reality is shot quality doesn’t even out.  Some players drive shot quality and some players suppress it.  Some players have a significantly different +/- than corsi +/- year in and year out.  I gave an example the other day in Brendan Morrison.  Here are some other names for Gabe to consider.

Some guys who can drive shooting percentage: Sidney Crosby, Marian Gaborik, Nathan Horton, Bobby Ryan, Martin St. Louis.   Henrik and Daniel Sedin.  Alex Tanguay.  Jason Spezza.

Some guys who can suppress shooting percentage:  Marco Sturm, Travis Moen, Tyler Kennedy, Taylor Pyatt, Shawn Thornton, Chris Drury, Jeff Carter. Torrey Mitchell. Kamil Kreps.

No one in the second group had an opposition shooting percentage above 7.6% in any single season over the past 4 years.  Only a handful of times over the past 4 years has any of the players in the first list had an on ice shooting percentage below 9% and only Bobby Ryan’s 23 game 2007-08 season was below 7.6%.

Now, you’ll probably notice that the first group are all first line players who are expected to produce offense while the second group are mostly third line players asked to shut down the opposition.  It’s difficult to suggest it was just luck that this is how things panned out.  No, some combination of talent and style of play will affect your on ice shooting and opposition shooting percentages.  And again it needs to be stated that shooting percentage is much more highly correlated with scoring goals than corsi.

Jeff Carter is an especially interesting case in that you can in no way argue that he has played in front of especially good goaltending that would drive down his shooting percentage against and yet he has a really slow shooting percentage and yet has one of the highest corsi against of any forward over the past 4 seasons (20.1 corsi events against per 20 minutes over the past 4 seasons ranks 300 of 310) but his goals against (0.753 per 20 minutes)  is decidedly average or even slightly better than average (ranks 139 of 310).

So Gabe, those are some players for you to consider.  I look forward to your response.

Jul 162011
 

Last night after news came out that Brendan Morrison had re-signed with the Calgary Flames, Kent Wilson tweeted the following:

Morrison back in Calgary. Check out his corsi tied rating fellow stats nerds: http://bit.ly/q1ywUj

The link is to the Calgary Flames 5v5 game tied corsi ratings which show Morrison had a 0.452 corsi rating (Corsi For %) which was dead last on the Flames.  The problem with jumping to the conclusion that Morrison is bad is two fold:

1.  Corsi generally speaking isn’t good at evaluating players.

2.  One year of 5v5 game tied data is not enough to evaluate players, even with corsi.

Lets take a look at Brendan Morrison over the past 4 years and I’ll show you exactly what I mean.  First lets look just at 5v5 any game score situations.

Season(s) CorF% GF%
2010-11 0.484 0.562
2009-10 0.514 0.627
2008-09 0.498 0.569
2007-08 0.430 0.500
2007-11 (4yr) 0.491 0.577

In each and every year the goals for percentage is significantly higher than his corsi for percentage.  His corsi ratings make Morrison look mediocre at best but his goal ratings make him appear to be quite good.  This isn’t a fluke.  It is occurring systematically, every single season, over 4 seasons in which Morrison played for 5 different teams (Vancouver, Anaheim, Dallas, Washington, Calgary).

Now what about 5v5 game tied situations.  Morrison’s 4 year game tied corsi for percentage is 0.482, his 4 year game tied goal for percentage is 0.592 (which ranks 28th of  217 among forwards with at least 1000 5v5 game tied minutes over the past 4 seasons).

Personally, I’d rather have good goal ratings than good corsi ratings.  Morrison is a good signing by the Flames.

Jul 122011
 

Over the past couple of weeks I have had several comment discussions regarding some of my recent posts on player evaluation and Norris and Hart trophy candidates which centered around which is a better method for evaluating players:  corsi vs goal based evaluation.  A lot of people, maybe the majority of those within the advanced hockey stat community, seem to prefer corsi based analysis while I prefer goal based analysis and I hope to explain why with this post.  I have explained much of this previously but hopefully this post will put it all into one simple easy to understand package.

There are two main objectives for a player when the coach puts him on the ice:  1.  Help his team score a goal.  2.  Help his team stop the opposing team from scoring a goal.  Depending on the situation and the player the coach may prioritize one of those over the other.  For example, a defensive player may be tasked primarily with shutting down an opposing teams offensive players and scoring a goal is really a very minor objective.  Late in a game when a team is down a goal the opposite is true and the primary objective, if not sole objective, is to score a goal.

I think we can all agree on the previous paragraph.  Goals are what matter in hockey so right there we have the #1 reason why goals should be used in player evaluation.  The problem is, goals are a relatively rare event and thus ‘luck’ can have a serious impact on our player analysis results due to the small sample size that goals provide.  This brought on the concept of corsi which is nothing more than shot attempts and is used as a proxy for scoring chances.  The benefit of corsi is that shot attempts occur about 10 times often as goals which gives us a larger sample size to evaluate players.

Continue reading »

Jun 012011
 

There seems to be some confusion, or lack of clarity, about my post on corsi vs shooting percentage vs shooting rate the other day so let me clear it up in as straight forward a way as I can.

“Hawerchuk” over at BehindTheNetHockey.com writes the following:

“I’m not totally sure what he’s getting at. People use Fenwick because it’s persistent, and PDO because it’s not. Over the course of a single season, observed shooting and save percentage drive results, but they are not persistent.”

Dirk Hoag over at OnTheForecheck.com writes:

“Here’s an example of when NOT to use correlation as a tool in statistical analysis (when the variables in question are linked by definition). David makes a bad blunder here, by looking at scoring leaders, seeing a bunch of high shooting percentages, and concluding that shooting percentage is the true “talent”. The problem is that shooting percentage swings wildly from season to season, whereas shooting rates are much more consistent.”

The great advantage of corsi/fenwick has over goals as an evaluator of talent is the greater sample size associated with it.  The greater the sample size the more confidence we can have in any results we conclude from it and the less chance that ‘luck’ messes things up.  Year over year shooting percentage fluctuates a lot, but that doesn’t necessarily mean that it isn’t a talent or doesn’t have persistence, it could mean that the sample size of one year is too small.  The four year shooting percentage leader board seems to identify all the top offensive players so it can’t be completely random.  So what happens if we increase the sample size?  Here are correlations of fenwick shooting percentages while on ice in 5v5 even strength situations for forwards:

Year(s) vs Year(s) Corrolation
200708 vs 200809 0.249
200809 vs 200910 0.268
200910 vs 201011 0.281
200709 vs 200911 (2yr) 0.497

As you can see, there isn’t a lot of persistence year over year but for 2 years over 2 years we are starting to see some persistence.  Still not to the level of corsi/fenwick, but certainly not non-existant either, and the greater correlation with scoring goals makes fenwick shooting percentage on par with fenwick as a predictor of future goal scoring performance when we have 2 seasons of data as I pointed out in my last post.

For the record, year over year correlation for fenwick for rate is approximately 0.60 depending on years used  and 2 year vs 2 year correlation is 0.66.

But as I pointed out in my previous post, you would probably never use shooting percentage as a predictor because you may as well use goal rate instead which has the same sample size limitations as shooting percentage but also factors in fenwick rate.  Year over year correlation of GF20 (goals for per 20 minutes) is approximately 0.45 depending on years used and the 2 year vs 2 year correlation is 0.619 so GF20 has persistence and has a 100% correlation with itself making it as reliable (or more) a predictor of future goal scoring rates as fenwick rate with just one year of data and a better predictor when using 2 years of data.  Let me repost the pertinent table of correlations:

Year(s) vs Year(s) FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.396 0.386
200809 vs 200910 0.434 0.468
200910 vs 201011 0.516 0.491
Average 0.449 0.448
200709 vs 200911 (2yr) 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.479 0.527

The conclusion is, when dealing with less than a years worth of data, fenwick/corsi is probably the better metric to identify talent and predict future performance, but anything greater than a year goals for rate is the better metric and for one years worth of data they are about on par with each other.

Note:  This is only true for forwards.  The same observations are not true about defensemen where we see very little persistence or predictability in any of these metricts, I presume because the majority of them don’t drive offense to any significant degree.

May 302011
 

The general consensus among advanced hockey statistic analyzers and is that corsi/fenwick stats are the best statistic for measuring player and team talent levels.  For those of you who are not aware of corsi and fenwick let me give you a quick definition.  Corsi numbers are the number of shots directed at the goal and include shots, missed shots and blocked shots.  Fenwick numbers are the same except it does not included blocked shots (just shots and missed shots).  I generally look at fenwick and will do that here but fenwick and corsi are very highly correlated to the results would be similar if I used corsi.

The belief by many that support corsi and fenwick is that by looking at fenwick +/- or fenwick ratio (i.e. fenwick for /(fenwick for + fenwick against)) is an indication of which team is controlling the play and the team that controls the play more will, over time, score the most goals and thus win the most games.  There is some good evidence to support this, and controlling the play does go a long way to controlling the score board.  The problem I have with many corsi/fenwick enthusiasts is that they often dismiss the influence that ability to drive or suppress shooting percentage plays in the equation.  Many dismiss it outright, others feel it has so little impact it isn’t worth considering except when considering outliers or special cases.  In this article I am going to take an in depth look at the two and their influence on scoring goals on an individual level.

I have taken that last 4 seasons of 5v5 even strength data and pulled out all the forwards that have played at minimum 2000 minutes of 5v5 ice time over the past 4 seasons.  There were a total 310 forwards matching that criteria and for those players I calculated the fenwick shooting percentage (goals / fenwick for), fenwick for rate (FenF20 – fenwick for per 20 minutes of ice time) and goal scoring rate (gf20 – goal for per 20 minutes ice time) while the player was on the ice. What we find is shooting percentage is more correlated with goal production than fenwick rate.

Shooting % vs GF20 R^2 = 0.8272
FenF20 vs GF20 R^2 = 0.4657
Shooting % vs FenF20 R^2 = 0.1049

As you can see, shooting percentage is much more highly correlated with goal scoring rate than fenwick rate is which would seem to indicate that being able to drive shooting percentage is more important for scoring goals than taking a lot of shots.

Here is a list of the top 20 and bottom 10 players in fenwick shooting percentage and fenwick rate.

Rank Player FenSh% Player FenF20
1 MARIAN GABORIK 8.07 HENRIK ZETTERBERG 16.7
2 SIDNEY CROSBY 7.83 ALEX OVECHKIN 16.3
3 ALEX TANGUAY 7.64 PAVEL DATSYUK 16.15
4 HENRIK SEDIN 7.63 TOMAS HOLMSTROM 16.05
5 BOBBY RYAN 7.60 NICKLAS BACKSTROM 16.05
6 STEVE DOWNIE 7.58 ERIC STAAL 16.04
7 EVGENI MALKIN 7.57 RYANE CLOWE 15.91
8 DANIEL SEDIN 7.55 ALEXANDER SEMIN 15.85
9 ILYA KOVALCHUK 7.49 SCOTT GOMEZ 15.8
10 NATHAN HORTON 7.44 ZACH PARISE 15.8
11 J.P. DUMONT 7.43 ALEXEI PONIKAROVSKY 15.79
12 JASON SPEZZA 7.39 JOHAN FRANZEN 15.78
13 PAUL STASTNY 7.36 JIRI HUDLER 15.74
14 PAVOL DEMITRA 7.33 DAN CLEARY 15.71
15 DANY HEATLEY 7.30 SIDNEY CROSBY 15.71
16 RYAN MALONE 7.29 JUSTIN WILLIAMS 15.68
17 JONATHAN TOEWS 7.28 CHRIS KUNITZ 15.61
18 THOMAS VANEK 7.24 MIKHAIL GRABOVSKI 15.56
19 SERGEI KOSTITSYN 7.24 JOE PAVELSKI 15.43
20 DREW STAFFORD 7.24 MIKAEL SAMUELSSON 15.39
301 BLAIR BETTS 4.20 CHUCK KOBASEW 11.34
302 ERIC NYSTROM 4.12 TRAVIS MOEN 11.31
303 SAMUEL PAHLSSON 4.10 IAN LAPERRIERE 11.23
304 SHAWN THORNTON 3.99 ERIC NYSTROM 11.21
305 TRAVIS MOEN 3.89 ROB NIEDERMAYER 10.94
306 TODD MARCHANT 3.88 TODD MARCHANT 10.91
307 NATE THOMPSON 3.75 SAMUEL PAHLSSON 10.87
308 FREDRIK SJOSTROM 3.70 JERRED SMITHSON 10.76
309 CRAIG ADAMS 3.52 JAY PANDOLFO 10.74
310 STEPHANE VEILLEUX 3.49 BLAIR BETTS 10.67

For both lists, the players are the top of the list are for the most part considered top offensive players and the players at the bottom of the list are not even close to being considered quality offensive players.  So, it seems that both shooting percentage and fenwick do a reasonable job at identifying offensively talented players.  That said, the FenF20 list includes 7 players (Zetterberg, Datsyuk, Holmstrom, Franzen, Hudler, Cleary and Samuelsson) who have played mostly or fully with the Detroit Red Wings and it seems unlikely to me that 7 of the top 20 offensive players are Red Wing players.  Furthermore, the fenwick list also includes guys like Ponikarovsky, Samuelsson, Hudler, Cleary, Williams, etc. who would probably be considered secondary offensive players at best.  From just this cursory overview it seems to confirm what we saw with the correlations – Shooting Percentage is a better indicator of offensive talent than Fenwick For rates.

It is actually no surprise that the Red Wings dominate the fenwick rate leader board because the Red Wings organizational philosophy is all about puck control.

“It’s funny because our game looks at numbers just like other games,” says Red Wings general manager Ken Holland, “but as much value as we assign to puck possession and how essential it is to winning, we really don’t have a numerical value for it that everyone can agree on. Remember when [A's general manager] Billy Beane started emphasizing on-base percentage in baseball? It wasn’t just a curious number; it changed the game. It redefined the type of player you wanted on your team. It’s coming in hockey; we just have to figure out how.”

This got the pro-corsi crowd riled up a bit as they said “Umm, yeah, we have that stat and it is called corsi” and were a bit bewildered at why NHL GMs didn’t make that recognition.  But anyway, what the above shows is that an organization that focuses on puck control dominates the corsi for statistic so I guess what that shows is that corsi/fenwick probably is a good measure of puck control.  But, as we have seen, fenwick (i.e. puck control) doesn’t automatically translate into goals scored.  There are no Red Wing players among the top 20 in fenwick shooting percentage and Datsyuk is the only Red Wing player in the top 20 in goals for per 20 minutes so while they take a lot of shots (or at least shot attempts), they aren’t the best at converting them into goals.

For me, and I am sure many others, the above is enough to conclude that shooting percentage matters a lot in scoring goals, but for the staunch corsi supporters they will argue that corsi is more persistent from season to season and thus is a better predictor of future performance.  So which is the better predictor of future performance?  The following table shows the correlation between shooting percentage and fenwick rate with the following seasons goal scoring rate.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20
200708 vs 200809 0.253 0.396
200809 vs 200910 0.327 0.434
200910 vs 201011 0.317 0.516
Average 0.299 0.449
200709 vs 200911 (2yr) 0.479 0.498
200709 vs 200910 (2yr vs 1yr) 0.375 0.479

Note:  For the above season(s) vs season(s) correlation calculations, only players with at least 500 5v5 even strength minutes in each of the four seasons are included.  This way the same players are included in all season(s) vs season(s) correlation calculations.

As you can see, when dealing with a single season of data the correlation with GF20 is much better for fenwick rate than for fenwick shooting percentage.  The gap closes when using 2 seasons as the predictor of a single season and is almost gone when using 2 seasons to predict the following 2 seasons.  It seems that the benefit of using corsi over shooting percentage diminishes to near zero when we have multiple seasons of data and though I haven’t tested it shooting percentage probably has an edge in player evaluation with 3 years of data.

Of course, you would never want to use shooting percentage as a predictor of future goal scoring rate when you could simply use past goal scoring rate as the predictor.  Past goal scoring rate has the same ‘small sample size’ limitations as shooting percentage (both use goals scored as it sample size limitation) but scoring rate combines the prediction benefits of shooting percentage and fenwick rate.  The table below is the same as above but I have added in GF20 as a predictor.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.253 0.396 0.386
200809 vs 200910 0.327 0.434 0.468
200910 vs 201011 0.317 0.516 0.491
Average 0.299 0.449 0.448
200709 vs 200911 (2yr) 0.479 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.375 0.479 0.527

The above table tells you everything you need to know.  When looking at single seasons both GF20 and FenF20 perform similarly at predicting next seasons GF20 with fenwick shooting percentage well behind but when we have 2 years of data as the starting point, GF20 is the clear leader.  This means, when we have at least a full seasons worth of data (or approximately 500 minutes ice time), goal scoring rates are as good or better than corsi rates as a predictor of future performance and beyond a years worth of data the benefits increase.  When dealing with less than a full season of data, corsi/fenwick may still be the preferred stat when evaluating offensive performance.

So what about the defensive side of things?

Year(s) vs Year(s) FenA20 to GA20 GA20 to GA20
200708 vs 200809 0.265 0.557
200809 vs 200910 0.030 0.360
200910 vs 201011 0.120 0.470
Average 0.138 0.462
200709 vs 200911 (2yr) -0.037 0.371
200709 vs 200910 (2yr vs 1yr) 0.000 0.316

Defensively, fenwick against rate is very poorly correlated with future goals against rate and it gets worse, to the point of complete uselessness, when we consider more seasons.  Past goals against rate is a far better predictor of future goals against rate.

Where it gets interest is unlike offense correlation drops when you consider more seasons which seems a bit strange.  My guess is the reason we are seeing this is because I am just looking at forwards and defense is more driven by goaltending and defensemen and as more time passes the greater the difference are in goalie and defensemen teammates.  Furthermore, forward ice time is largely driven by offensive ability (and not defensive ability) so many of the quality defensive forwards may be removed from the study because of the 500 minute per season minimum I am using (i.e. the group of players used in this study are biased towards those that aren’t focusing on defense).  Further analysis is necessary to show either of these as true though but the conclusion to draw from the above table that, for forwards at least, goals against rates are by far the better indicator of defensive ability.

In summary, it should be clear that we cannot simply ignore the impact of a players ability to drive or suppress shooting percentage in the individual player performance evaluation and so long as you have a full year of data (or > 500 or more minutes ice time) the preferred stat for individual player performance evaluation should be goal scoring rate.  Corsi/fenwick likely only provide a benefit to individual performance evaluation when dealing with less than a full year of data.