One of the biggest omissions in my player rankings is making adjustments for zone start differences.  We know that Manny Malhotra has a significant bias towards starting his shifts in the defensive zone and that his teammates Daniel and Henrik Sedin have a significant bias towards starting their shifts in the offensive zone.  The result is Malhotra will unfairly be penalized for giving up more shots and goals against simply because he starts more often in the defensive zone and the Sedins have a huge advantage in generating shots and goals because of how often they start their shifts in the offensive zone.  The question is, how much of an effect does it have and how do we adjust for it?

Over the past couple of weeks I have been pondering these questions and I thought of two potential solutions to the problem.  The first solution is to find some sort of adjustment factor based on zone start statistics.  I briefly pondered a few ideas but wondered if a uniform adjustment factor can be fairly applied to all players who have varying skills and talents.  I decided that I would take a look at my second idea first.

My second adjustment idea is really a simple idea and really isn’t an adjustment at all.  The idea is to just ignore any play that occurs during some stretch of time after an offensive/defensive zone face off.  After some length of time, any advantage (or disadvantage) one might get from starting in the offensive (or defensive) zone would be nullified.  Worst case scenario is we have to eliminate ~45 seconds after every offensive or defensive zone face off which would essentially nullify the whole shift.

So, with that in mind I took a look at 3 year (2008-09, 2009-10 and 2010-11) 5v5 statistics and did a comparison of four different lengths of time to ignore after an offensive/defensive zone faceoff – 0, 10, 20 and 30 seconds.  To evaluate what is going on I looked at each players fenwick for and against per 20 minutes and calculated the correlation between each time after faceoff adjustment.  Here is what I found:

 FenF/20 FenA/20 5v5 vs F10 0.8639 0.8451 F10 vs F20 0.9882 0.9866 F20 vs F30 0.9870 0.9883 5v5 vs F20 0.8718 0.8368

5v5 is no zone start adjustment, F10 is ignoring 10 seconds after an offensive/defensive zone faceoff, f20 is ignoring 20 seconds after and f30 is ignoring 30 seconds after.  The numbers are r^2 for fenwick for per 20 minutes and fenwick against per 20 minutes.

As you can see, there is a somewhat sizeable difference between 5v5 and the F10 adjustment but there is very little difference between the F10 and F20 or F20 and F30 and there isn’t really any difference between 5v5 vs F10 and 5v5 vs F20.  All of this tells me that any advantage (or disadvantage) a player gains because of their zone stars occurs during the first 10 seconds after an offensive or defensive face off.  After that, only the players talent matters and there is no benefit to removing more data from our analysis.

Wanting to confirm this works for a single season of data I decide to take a look at Manny Malhotra and Henrik Sedin’s stats from last season.

 Malhotra FenA/20 Sedin FenF/20 5v5 14.16 15.39 F10 12.49 13.31 F20 12.44 13.66 F30 12.24 13.71

This confirms what we witnessed with the correlations using 3 years of data.  By ignoring the first 10 seconds after an offensive/defensive zone faceoff we can eliminate any benefit/penalty a player may get because of his zone starts.  When I finally get around to updating my stats site I intend to include F10 data as well and I think this is a simple enough solution to abandon any attempts at any other zone start adjustment technique.

There is a post over at Backhand Shelf today that lists 10 backup goalies that have out performed their #1 counterparts.  It is an interesting read but it may be a perfect example of how simple statistics don’t tell the whole story.

The first pair of goalies on the list are the Bruins Tukka Rask vs Tim Thomas.

Backup: Tuukka Rask (10-4-1, 1.59 GAA, .945 SV%)
Starter: Tim Thomas (17-7-0, 1.99 GAA, .938 SV%)

Now both goalies have exceptionally good numbers but on the surface you would probably conclude that Rask has superior numbers to Thomas and on the surface you would be correct.  But dig a little deeper and things may look a little different.

A few days ago I was wading through some statistics and made an interesting observation about the Bruins handling of these two goalies.  Specifically, Tukka Rask gets far easier starts than Tim Thomas.

 Rask Thomas Opp. Record 265-257-72 511-418-123 Opp. Points % 1.013 1.088 Opp. Points/82gms 83.1 89.2 Opp GFA 2.55 2.77 Opp Sh% 8.66% 9.29%

Thomas’s opponents have a better record, have a better goal scoring rate and have a better shooting percentage than Rask’s and generally speaking it isn’t very close.  Boston has the highest goals per game average in the NHL.  The next 6 teams are Philadelphia, Vancouver, Detroit, Toronto, Chicago and Ottawa.  Of Rask’s 14 starts he has 2 starts (14.3%) against those six teams, one against Toronto and one against Detroit.  Thomas has 25 starts, and 9 starts (36%) against those six teams (3 vs Toronto, 2 vs Ottawa, 2 vs Philadelphia, 1 vs Chicago and 1 vs Vancouver).

When you take Rask and Thomas’s individual numbers on the surface it appears that Rask has out performed Thomas but when you dig deeper and look at the quality of opposition it is far less clear that Rask has outperformed Thomas and in fact it may be the other way around.

(On a side note, the combined record of all of Boston’s opponents is just 776-675-195, the equivalent of an 87 point team so it seems they have had a fairly easy schedule thus far. )

This will be the final part of my unplanned 3-part series on who is good and who is not on the current Leafs team.  The first was about the penalty kill and the second was defensively.  Today we look at the players offensively.

The Defensemen

 Player Name GFA FenF20 Ozone% DION PHANEUF 2.4 15.22 60.2% KEITH AULIE 2.25 14.24 61.9% JOHN-MICHAEL LILES 2.79 13.85 46.1% CARL GUNNARSSON 2.1 13.42 56.1% CODY FRANSON 2.37 13.2 47.2% JAKE GARDINER 2.25 12.79 54.3% LUKE SCHENN 2.49 12 47.3% MIKE KOMISAREK 2.82 10.95 40.8%

Included in the table above are goals for average (goals for per 60 min.), fenwick for per 20 minutes and offensive zone faceoff percentage which gives an indication which players start most frequently in the offensive zone.  There really isn’t too much exciting going on here.  For the most part the defensemen’s FenF20 is driven by their Ozone%.  The r^2 between FenF20 and Ozone% is 0.60 so there is a pretty tight correlation.  The only deviation is Liles who generates more offense than his Ozone% indicates he should.  The r^2 is 0.80 if we don’t include Liles.  So offensively, it seems Liles is the only defenseman who is able to drive the play significantly more than any of the others.  Looks like he might be worth keeping around.  Let’s get his name on a contract extension.

The Forwards

 Player Name GF20 FenF20 Ozone% PHIL KESSEL 3.36 14.91 51.3% MIKHAIL GRABOVSKI 2.79 14.67 57.6% JOFFREY LUPUL 3.6 14.5 50.2% NAZEM KADRI 3.54 14.13 48.1% MIKE BROWN 1.2 14.13 49.5% TYLER BOZAK 2.97 13.97 49.3% NIKOLAI KULEMIN 2.46 13.68 53.4% DAVID STECKEL 1.26 13.21 48.6% CLARKE MACARTHUR 2.97 12.92 56.3% MATT FRATTIN 2.07 12.64 49.5% TIM CONNOLLY 2.43 12.63 46.1% MATTHEW LOMBARDI 2.46 11.94 54.1% PHILIPPE DUPUIS 0 11.65 50.0% JOEY CRABB 2.34 11.57 56.1% JOE COLBORNE 3.33 11.29 50.0% JAY ROSEHILL 0 10.6 43.1% COLBY ARMSTRONG 0.87 10.41 51.3% COLTON ORR 3.21 9.65 22.2%

Unlike the defensemen there is very little correlation between the Ozone% and FenF20 (r^2=0.0395) which means there is no rhyme or reason to where these guys are starting on the ice.  Joey Crabb can’t seem to drive offense and yet has an Ozone% of 56.1%.  The best offensive line of Kessel-Lupul-Bozak start about 50% of the time in the defensive zone while our supposed defensive specialist Philippe Dupuis starts half the time in the offensive zone.  What’s that all about coach?  Aside from those oddities it is kind of what we’d expect.  Offense is driven by the Kessel and Grabovski lines.  Generally speaking, there aren’t too many surprises in regards to how the Leafs are performing offensively.  The only surprise might be Mike Brown rating so highly.  This is pretty abnormal for him so probably just small sample size issues going on.

Yesterday I took a look at the Leafs players on the PK to see who has seen good result and who has seen bad results when they have been on the ice.  Today I do the same thing but look at 5v5 situations from the defensive side of things to see if there is any consistency between 5v5 and the PK.

The Goalies

 Player Name GAA SV% JAMES REIMER 1.41 94.6% JONAS GUSTAVSSON 2.58 91.3% BEN SCRIVENS 2.82 90.6%

Interestingly, this is the exact opposite as we saw on the PK where Reimer had the worst save percentage and Scrivens had the highest.  We should have more confidence in these numbers so it is quite possible that Reimer’s poor results are primarily luck driven.  The question is, how much can he improve it?  Last year on the PK Reimer had an 85.6% save percentage which while is much better than this seasons 77.3% still is not good.  He ranked 34th of 40 goalies last season on the PK while he was 6th of 48 at 5v5.  Last year Reimer had a 93.3% 5v5 save percentage so he is actually better this season at 5v5.  Is it sustainable?  Time will tell.

The Defensemen

 Player Name GAA FenA20 DION PHANEUF 2.40 12.11 CARL GUNNARSSON 2.25 12.24 CODY FRANSON 2.37 12.51 KEITH AULIE 4.50 12.74 MIKE KOMISAREK 2.34 13.38 JOHN-MICHAEL LILES 2.55 13.77 JAKE GARDINER 1.86 14.82 LUKE SCHENN 2.19 16.63

For those regular readers, I believe players can drive shooting percentages (especially) and suppress oppositions shooting percentages (less so) but we are below the threshold of where small sample size issues outweigh the benefits of doing a goal analysis over a fenwick/corsi analysis.  So, when ranking players defensively we should focus on fenwick (for now).

Ughhh.  While Schenn’s GAA isn’t the worst (it’s actually pretty good relative to his teammates) his fenwick against is awful.  Significantly worse than his teammates.  While Schenn has had a slight bias towards defensive zone faceoffs it isn’t enough so to justify this difference in fenwick against.  Liles, Franson and Komisarek had a higher percentage of defensive zone faceoffs and had better results.  Taking it to a league level, of the 166 defensemen with 250 minutes of 5v5 ice time this season Schenn ranks second last in fenwick against per 20 minutes.  Only Derek Morris of Phoenix is worse.  In the summer I wrote an article about how poor Schenn is defensively and there isn’t a lot in the numbers above to change my opinion any.

Phaneuf, Gunnarsson and Gardiner were the primary offensive zone players which explains in part why Phaneuf and Gunnarsson lead the list but also show that Gardiner still struggles defensively as is often the case with a rookie.  Hopefully, unlike Schenn, he’ll improve with experience.

The Forwards

 Player Name GA20 FenA20 MIKE BROWN 1.62 10.36 COLBY ARMSTRONG 2.61 10.7 DAVID STECKEL 2.13 11.02 NAZEM KADRI 3.54 11.78 CLARKE MACARTHUR 2.76 11.79 PHILIPPE DUPUIS 0.81 11.83 JAY ROSEHILL 1.41 12.02 MIKHAIL GRABOVSKI 1.68 12.24 MATTHEW LOMBARDI 4.29 12.97 MATT FRATTIN 1.5 13.2 JOEY CRABB 2.64 13.32 TIM CONNOLLY 1.62 13.53 NIKOLAI KULEMIN 1.98 13.68 JOE COLBORNE 2.79 14.43 PHIL KESSEL 2.46 15.47 JOFFREY LUPUL 2.82 16.47 TYLER BOZAK 2.82 16.57 COLTON ORR 0 20.38

Kessel, Lupul, Bozak – score a lot of goals, give up a lot of goals.  The three of them have very high fenwick against relative to their teammates.  This isn’t unsual for offensive players (high risk, high reward), but the best players in the league find a way to accomplish both offense and defense (i.e. Datsyuk).  The second line of Grabovski, MacArthur and Kulemin seem much more defensively responsible, but surprisingly they have a higher percentage of offensive zone starts than the Kessel line so they should have better numbers, but maybe not to the extent they do.  Brown, Steckel and Dupuis do seem like pretty solid defensive players 5v5.  Those fenwick against numbers for those three are quite good relative to the rest of the team, and the rest of the league.

Overall the Leafs are a decent enough defensive team at 5v5.  Especially once you look past the Kessel-Lupul-Bozak line up front and Schenn (and to a lesser extent Gardiner) on defense.  For some strange reason though, that hasn’t translated very well to the PK.  Why they suck so bad on the PK is pretty dumbfounding.

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.

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.

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.

There has been an interesting discussion of on-ice shooting percentage at Tyler Dellow’s mc79hockey.com.  I have argued that we need to look at on-ice shooting percentage as a talent, and not something that just happens randomly while others have largely dismissed it.  One person in particular is Gabe Desjardin’s who has a followup post on his blog largely dismissing its importance.

In his blog post Gabe first discusses Gaborik’s value just considering his on-ice shooting percentage.

So are these totals 75% skill then?  Let’s do a quick check on how many goals that skill would be worth: 1000 on-ice shots/season * 2.5% above mean * 75% = 18.75 goals above average.  Double that to get to an approximate replacement level of 37.5 goals or just over six wins.  The current price for one win on the free agent market is roughly \$3M, so we’d estimate Gaborik’s offensive value at more than \$18M.

Gabe doesn’t believe any one player could be worth \$18M based just on shooting percentage so he tries to shoot a hole in that by looking at 2yr vs 2yr regression.

Needless to say, anytime you come up with a metric that says a player should get paid \$18M, you have to go back and check your math.  I did that by splitting the last four years into two two year periods (2007-08/2008-09 vs 2009-10/2010-11) and comparing on-ice shooting percentage among players who had 1000+ on-ice shots in each period.  I found that player on-ice shooting regressed 80% to the mean from the first set to the second, which puts Gaborik’s apparent talent closer to \$5M.

Ok, so we have Gaborik’s value down to \$5M.  That still seems pretty large to me but Gabe dismisses that further by suggesting some of that \$5M has to be attributed to his linemates, arena bias, and strangely, a players opponents (particularly at home).

This is a key point, of course, and one that may not come through when we talk about team-level effects or try to figure out the value of individual top six forwards: when a #1 line plays against a #4 line, their shooting percentage goes up relative to when they’re playing power-vs-power.

Here is the thing.  The whole reason I participate in these debates is to suggest that players do in fact have the ability to drive or suppress shooting percentage and thus we must consider shooting percentage, in addition to corsi, when evaluating players.  So, it amazes me when I am debating someone that is trying to minimize the ability to drive/suppress shooting percentage that they bring up such observations that a players shooting percentage will go up when they are playing weaker players (i.e. the fourth line) who I presume can’t suppress shooting percentage as well as the stronger players.   There is clearly something wrong with the logic there.  Players don’t have the ability to suppress shooting percentage, but fourth liners are worse at suppressing shooting percentage than first liners??

The other thing I want to discuss is Gabe’s calculation that shooting percentage regresses 80% to the mean based on his 2yr vs 2yr calculation.  I won’t dispute his math because it is probably true, but I will suggest that I don’t believe that a league-wide observation can be applied to individual players.  There are a number of factors that influence a players on-ice shooting percentage.

1.  The quality of his linemates.

2.  The quality of opposition.

3.  Style of play (i.e.  aggressive offensive game vs defensive style).

4.  Score effects

Team building generally revolves around a small number of players.  Pittsburgh has Crosby, Malkin and Staal as their core forwards, everyone else is pretty much interchangeable.  Pretty much every team is like this.  A lot of those interchangeable parts move from team to team or even line to line on the same team or get asked to play different roles on the same team.  An injury to Kunitz and Pascal Dupuis gets bumped from the third line to playing with Crosby.  For these mostly interchangeable parts there can be a lot of variation in who they play with, the team they play on, and who they play against, and the roles they are asked to play.  All these factors are at play when Gabe calculates his 80% regression to the mean.  The good players who have well-defined roles don’t see near the same variation in their on-ice shooting percentages.  Look at the Crosby’s and Gaborik’s.  They are consistently at the top of the list.  Look at the Moen’s and Marchant’s and Pahlsson’s, they are consistently near the bottom of the list.  The players we perceive as good offensive players are at the top of the list.  The players we perceive as weak offensive players, or defensive minded players, are at the bottom of the list.  That’s a talent that we must consider.

Yesterday I posted my 100% statistics based predictions for the eastern conference, today here are the predictions for the western conference.  See the eastern conference predictions for more details on how these predictions are calculated but generally speaking I use my player rating system and combine them with my estimates for ice time for every player to come up with a predicted goals for and against for every team.  I haven’t converted the goal differentials to won-loss records because I actually think looking at the predicted goals for and against and goal differential provides better insight into the strengths and weaknesses of each team.

 Predicted Last Season Team GF GA GF-GA GF GA GF-GA Chicago 235.6 205.0 30.6 252 220 32 Vancouver 238.7 213.0 25.8 258 180 78 San Jose 228.3 208.2 20.1 243 208 35 St. Louis 233.3 217.9 15.4 236 228 8 Calgary 234.1 223.2 11.0 241 230 11 Detroit 241.7 233.9 7.8 257 237 20 Los Angeles 216.6 211.5 5.0 209 196 13 Nashville 214.0 210.3 3.7 213 190 23 Anaheim 237.2 234.7 2.5 235 233 2 Dallas 221.7 221.6 0.1 222 226 -4 Phoenix 210.5 217.9 -7.4 226 220 6 Minnesota 210.7 230.1 -19.4 203 228 -25 Columbus 216.9 239.4 -22.5 210 250 -40 Colorado 205.4 239.5 -34.1 221 287 -66 Edmonton 204.9 252.0 -47.1 191 260 -69

As I mentioned in the eastern conference predictions, while I think the above standings seem for the most part reasonable I think there will be more spread in the goals for column.  The top offensive teams will probably end up scoring 20+ goals more than is predicted above.  Last season Vancouver had 258 goals to lead the conference and that was a low total for a conference leader.  The prior 2 seasons the leader had 268 and 289 goals scored.

As far as surprises go, seeing St. Louis fourth and Calgary fifth were definitely surprises but then Calgary’s goal differential is predicted to be the same as last season and the Blues goal differential only rises moderately from +8 to +15.4 based mostly by reducing the goals against.  These teams weren’t that far from making the playoffs either so while a little surprising on the surface, might not be all that unreasonable of a prediction.  Los Angeles being predicted to score only 5 more goals than they give up is a surprise too.

 Team GF Team GA Detroit 241.7 Chicago 205.0 Vancouver 238.7 San Jose 208.2 Anaheim 237.2 Nashville 210.3 Chicago 235.6 Los Angeles 211.5 Calgary 234.1 Vancouver 213.0 St. Louis 233.3 St. Louis 217.9 San Jose 228.3 Phoenix 217.9 Dallas 221.7 Dallas 221.6 Columbus 216.9 Calgary 223.2 Los Angeles 216.6 Minnesota 230.1 Nashville 214.0 Detroit 233.9 Minnesota 210.7 Anaheim 234.7 Phoenix 210.5 Columbus 239.4 Colorado 205.4 Colorado 239.5 Edmonton 204.9 Edmonton 252.0

It looks like it could be another tough year for fans in Edmonton and Colorado as they are predicted to be the bottom 2 teams in goals scored as well as be the bottom 2 teams in goals allowed.  I am sure the fans in Washington are smiling since they have Colorado’s first round pick which they acquired in the Varlamov trade.  Based on the predictions above, I’d say there is a more than decent chance it is a top 5 pick overall.

The final interesting thing is that these predictions predict the eastern conference to have a better goal differential than the western conference.  This is a change from recent seasons when the west has generally been the better conference.  Not sure if this will become reality or not but it is worth watching.  There were a number of quality players that moved from the west to the east this summer (Brad Richards, Ilya Bryzgalov, Brian Campbell, Christian Ehrhoff, Robyn Regehr, Tomas Fleishmann, Scottie Upshall, Steve Sullivan, Matthew Lombardi, Joel Ward, etc.) which probably weren’t fully offset by the players going west (Carter, Richards, Wisniewski, etc.).  Whether the shift is enough to make the east as good or better than the west we’ll have to wait and see.

A week or two ago I presented a prediction of the eastern conference using a purely statistics based analysis.  There were a number of limitations with the process which I outlined at the beginning of the post but I have fixed some of those so this is version 2.0 of the prediction algorithm.  Let me summarize the process.

1.  I took each teams current rosters and estimated the amount of even strength, power play and shorthanded ice time each player on the roster would play.  For veteran players, the estimates were loosely based on previous years ice time which should give us a pretty accurate number for the majority of the players, serious injuries aside.
2. I then combined the ice time data with my 3-year 5v5close, 5v4 power play and 4v5 shorthanded HARO+ and HARD+ ratings.  I used 3-year ratings because I think they more reliably reflect each players true abilities where as one year, and even two year, ratings have significant margins of error associated with them.
3. For rookies and other relatively un-established players I had to take guestimates at their ratings and their ice times.  Most rookies or players with little NHL experience to develop ratings with I guestimated them to be below average players, except for players who are premiere prospects in which case I rated them more like an average player.   It is actually somewhat rare for rookies to perform significantly above average, especially defensively.
4. Unlike my previous ratings, I did make adjustments for strength of schedule.
5. Also, unlike my previous ratings, I did make adjustments for teams that might get more or less than an average number of power play or penalty kill opportunities.  To do this I used each teams total power play and short handed situations over the past 2 seasons and compared them to the league average.  For teams which more powerplays than the average team had their power play goal production increased and those with less had their power play goal production decreased accordingly.  The same was done for the penalty kill.  Of course, if a team changes their playing style to take or draw more or fewer penalties than in the previous 2 seasons the reliability of the predictions will be degraded somewhat.

As with the previous post, I haven’t converted goals for/against into points in the standings but this gives you an indication of how the numbers seem to view the teams talent levels.  So, with that said, here are your eastern conference predictions.

 Predicted Last Season Team GF GA GF-GA GF GA GF-GA Boston 227.1 203.5 23.5 244 189 55 Pittsburgh 242.0 219.9 22.1 228 196 32 Buffalo 235.4 217.7 17.6 240 228 12 Washington 236.3 218.9 17.3 219 191 28 Philadelphia 239.3 222.1 17.2 256 216 40 Toronto 245.3 235.6 9.6 213 245 -32 Tampa Bay 233.6 224.4 9.3 241 234 7 NY Rangers 217.5 210.8 6.8 224 195 29 Montreal 226.5 225.6 0.9 213 206 7 Carolina 227.5 231.0 -3.5 231 234 -3 Florida 211.4 216.6 -5.2 191 222 -31 New Jersey 202.3 210.1 -7.8 171 207 -36 NY Islanders 227.4 240.5 -13.1 225 258 -33 Winnipeg 210.3 235.5 -25.2 218 262 -44 Ottawa 189.5 251.8 -62.3 190 245 -55

Before getting into some team specific observations, a first observation worth noting is that the goals for and against predictions seem to be more compressed than what typically occurs in the NHL standings.  The predicted goals for totals range from a high of 245 to a low of 189.  The low of 189 is perfectly reasonable (the lows from the previous 3 seasons are 171, 196 and 190) but the high of 245 is well below the high totals of previous years.  Last season the Canucks scored a high of 258 goals, the previous season the Capitals led with 313 followed by the Canucks with 268 and in 2008-09 the Red Wings led with 289 goals.  I am not sure if this is evidence of increased parity or whether it is a flaw within the ratings system and/or the prediction algorithm.

 Team GF Team GA Toronto 245.3 Boston 203.5 Pittsburgh 242.0 New Jersey 210.1 Philadelphia 239.3 NY Rangers 210.8 Washington 236.3 Florida 216.6 Buffalo 235.4 Buffalo 217.7 Tampa Bay 233.6 Washington 218.9 Carolina 227.5 Pittsburgh 219.9 NY Islanders 227.4 Philadelphia 222.1 Boston 227.1 Tampa Bay 224.4 Montreal 226.5 Montreal 225.6 NY Rangers 217.5 Carolina 231.0 Florida 211.4 Winnipeg 235.5 Winnipeg 210.3 Toronto 235.6 New Jersey 202.3 NY Islanders 240.5 Ottawa 189.5 Ottawa 251.8

The teams with the largest predicted improvements in goal differential are the Leafs (42 points), the Devils (28), Panthers (26), Islanders (20), and Jets (19) while the teams predicted to fall back the most in terms of goal differential are Boston (-31), Philadelphia (-23) and the Rangers (-22).   The predicted top 6 scoring teams in the east are Toronto, Pittsburgh, Philadelphia, Washington, Buffalo and Tampa while the lowest scoring teams are predicted to be Ottawa, New Jersey, Winnipeg and Florida.  The teams with the predicted worst defense are Ottawa, Islanders, Toronto, Winnipeg and Carolina and the predicted best defensive teams are Boston, New Jersey, NY Rangers, Florida and Buffalo.  While there are a couple of surprises in there, most of those seem quite reasonable.  Now for some team specific observations.

Washington Capitals – The Capitals played a different game last season from the previous two seasons.  In 2008-09 they scored 268 goals but gave up 240, in 2009-10 they scored 313 and gave up 227.  Last season they improved significantly defensively giving up just 191 goals but their offense also suffered as they scored just 219.  The predictions are predicting the offense will come back next season but will cost them a little defensively.  Mathematically speaking it makes sense, but in reality it is difficult to say whether they will change their playing style back to a more offensive game or not at the cost of defense.  We’ll have to wait and see.

Toronto Maple Leafs – One of the biggest surprises in these predictions is the offense of the Maple Leafs.  They are predicted to score the most goals of any team, eastern or western conference.  A big reason for this is both Joffrey Lupul (who played just 28 games with the Leafs) and Tim Connolly have very good HARO+ ratings as do many of the returning Leaf forwards including Kessel, Kulemin, Grabovski, and MacArthur.  Even projected third line players Armstrong and Bozak have solid HARO+ ratings.  If the ratings are true, scoring goals shouldn’t be a problem for the Leafs and in fact the late season surge last year was predominantly a result of increased goal production and not solely due to the play of James Reimer.  The Leafs problematic defensive ability is still an issue for the Leafs though.

New York Rangers – It is difficult to fathom how a team that added Brad Richards will see their goal production drop from 224 to about 217.  This is a little dumbfounding, but the Rangers did lose 16 goals from Frolov and Prospal and the algorithm is certainly not predicting another 21 goals from Brian Boyle (his previous career high was 4) so it is certainly possible that Richards won’t dramatically increase the Rangers offensive output.  We’ll see.

Philadelphia Flyers – Unless some of the younger players really step up their games it is difficult to see them being as good a team as the Flyers from last season.  They are predicted to score 16 fewer goals but give up 6 more (despite Bryzgalov).

New Jersey – The Devils will be a dramatically better team this year, but they still may not be a very good one.  They have some highly talented forwards (Parise, Zajac, Kovalchuk) but they depth is weak and they will produce very little offense from the back end and who knows what Brodeur has left in the tank.

Florida Panthers – They brought a lot of players in this past off season and they should have an improved team but like the Devils it might be a stretch for them to make the playoffs.

Tomorrow we’ll take a look at the predictions for the western conference standings.