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Defenders effect on Save %

By on February 9th, 2012

It has been shown on numerous occasions that players can influence their own teams on-ice shooting percentage be that through their talents or their style of play.  An example is the PDO vs Luck article I posted the other day.  In that article there is a table that clearly shows that shooting percentage varies across players and that players who are given more ice time (presumably because they are better players) have higher shooting percentages.  The same was not true for on-ice save percentage though.  On-ice save percentages were not ‘stratified’ according to ice time. That study looked at forwards and I have since looked at defensemen and have also attempted to see if organizing players according to defensive zone starts percentages would allow for ‘stratification’ of on-ice shooting percentages but to no avail.  But I am stubborn and didn’t give up.

The next thing I chose to do is compare a players on-ice save percentage with the weighted average of the save percentages of all the goalies the player played with.  The weighted average is based on the number of shots against the goalie and the player were on the ice together for.  So, lets say for example Player A was on the ice for 100 shots against, 30 of those shots were when he was on the ice with Goalie A and 70 were when he was on the ice with Goalie B.  When Goalie A is not playing with Player A his save percentage is 91%.  When Goalie B is not playing with Player A his save percentage is 92%.  The weighted average of the two goalies is (91% * 30 + 92% * 70) /100 or 91.7%.  I then compare that goalie save percentage 91.7% to the players on-ice save percentage by dividing the players save percentage by the goalies save percentage.  So, for example, if Player A’s on-ice save percentage is 92% then I calculate 92% divided by 91.7% to get 100.33.  Any numbers above 100 indicate the player improved his goalies save percentage and any numbers below 100 indicate the player hurt the goalies save percentage.

In order to get an indication of whether the player could produce that much of an improvement due solely to luck I employed a binomial distribution estimation of the likelihood that the player would have an on-ice save percentage greater than the one he posted considering the goalies he played against.  The results of all of this are below.  Forwards first followed by defensemen and top 25 and bottom 25 for both.  The data I used was 4 year 2007-11 5v5 zone start adjusted data and only using players with 1250 shots against.

Forward Sv% Infl. Chance > Forward Sv% Infl. Chance >
TAYLOR PYATT 101.94% 0.54% MATT STAJAN 98.87% 90.41%
MANNY MALHOTRA 101.95% 1.00% DEREK ROY 98.98% 90.50%
ZACH PARISE 101.86% 1.08% DAVID BACKES 98.90% 90.86%
JEFF CARTER 101.61% 1.32% SAM GAGNER 98.83% 91.74%
LEE STEMPNIAK 101.70% 1.34% HENRIK ZETTERBERG 98.77% 92.60%
JORDAN STAAL 101.50% 2.45% SIDNEY CROSBY 98.83% 92.92%
TEEMU SELANNE 101.51% 2.95% SHANE DOAN 98.98% 93.52%
TRAVIS MOEN 101.30% 3.59% PATRICK KANE 98.76% 93.64%
CORY STILLMAN 101.34% 3.62% DAINIUS ZUBRUS 98.67% 93.73%
RADIM VRBATA 101.34% 4.54% RICK NASH 98.77% 94.30%
TRAVIS ZAJAC 101.22% 5.22% MARTIN HAVLAT 98.64% 94.72%
BRIAN GIONTA 101.11% 6.15% MARTIN ERAT 98.75% 95.04%
SAMUEL PAHLSSON 101.22% 6.30% DAVID BOOTH 98.61% 95.77%
RADEK DVORAK 101.08% 6.99% PAUL STASTNY 98.44% 96.62%
VALTTERI FILPPULA 101.28% 7.14% ANDREW LADD 98.42% 96.99%
JASON POMINVILLE 101.01% 7.72% MARK RECCHI 98.54% 97.07%
WOJTEK WOLSKI 101.07% 8.24% EVGENI MALKIN 98.48% 97.67%
MIKE KNUBLE 101.03% 8.40% ALEXANDER FROLOV 98.16% 97.93%
MARC SAVARD 101.05% 9.02% RYAN KESLER 98.29% 98.12%
CHRIS THORBURN 101.07% 10.39% THOMAS VANEK 98.41% 98.39%
CHRIS DRURY 100.98% 11.55% TODD WHITE 98.05% 98.45%
MICHAEL RYDER 100.88% 11.62% CHRIS KELLY 98.02% 98.63%
RENE BOURQUE 100.98% 11.81% KRISTIAN HUSELIUS 97.85% 99.39%
NICKLAS BACKSTROM 100.87% 12.22% BRANDON DUBINSKY 97.51% 99.89%
MIKKO KOIVU 100.84% 12.65% ILYA KOVALCHUK 97.65% 99.96%

 

Defenseman Sv% Infl. Chance > Defenseman Sv% Infl. Chance >
KENT HUSKINS 102.22% 0.26% AARON WARD 99.21% 81.98%
NICKLAS LIDSTROM 102.09% 0.31% JORDAN LEOPOLD 99.23% 83.79%
ROB SCUDERI 101.78% 0.52% KEVIN BIEKSA 99.13% 84.68%
SEAN O’DONNELL 101.55% 1.26% JAROSLAV SPACEK 99.25% 84.75%
BRYCE SALVADOR 101.87% 1.28% NICK BOYNTON 99.14% 85.31%
SHANE O’BRIEN 101.63% 1.52% DAN BOYLE 99.19% 85.70%
MIKE WEAVER 101.61% 2.15% STEPHANE ROBIDAS 99.13% 87.86%
ROSTISLAV KLESLA 101.60% 3.15% SHEA WEBER 99.22% 87.88%
TREVOR DALEY 101.23% 3.16% JOHN-MICHAEL LILES 98.98% 89.07%
BRYAN MCCABE 101.25% 3.30% LUBOMIR VISNOVSKY 99.04% 90.41%
TIM GLEASON 101.20% 3.55% DENNIS WIDEMAN 99.11% 91.36%
ROB BLAKE 101.48% 3.86% MARK STREIT 98.79% 91.57%
MARC-EDOUARD VLASIC 101.22% 3.95% BRENT SEABROOK 98.94% 92.21%
PAUL MARTIN 101.37% 4.29% SHAONE MORRISONN 98.80% 92.52%
MIKE LUNDIN 101.51% 4.97% SCOTT NIEDERMAYER 98.82% 93.29%
ANDREJ MESZAROS 101.09% 5.88% ANDREJ SEKERA 98.71% 94.38%
NICK SCHULTZ 101.00% 5.96% FILIP KUBA 98.63% 94.44%
KEITH YANDLE 101.00% 6.79% MARTIN SKOULA 98.61% 95.27%
ANDREI MARKOV 101.07% 7.22% DUNCAN KEITH 98.80% 95.91%
MATT GREENE 101.14% 7.30% BARRET JACKMAN 98.73% 95.96%
ROMAN HAMRLIK 100.81% 9.44% DAN GIRARDI 98.69% 97.15%
TONI LYDMAN 100.83% 10.05% ZBYNEK MICHALEK 98.74% 97.37%
DUSTIN BYFUGLIEN 100.98% 10.12% FEDOR TYUTIN 98.53% 97.74%
JAN HEJDA 100.89% 10.14% DAN HAMHUIS 98.63% 97.87%
CHRIS PRONGER 100.89% 10.72% JACK JOHNSON 97.80% 99.95%

There were a total of 172 forwards and 141 defensemen in the study.  What is interesting is that there were 15 defensemen (10.6% of them) that had their binomial chance of posting their on-ice save percentage at 5% or lower when we would expect 7 by chance.  That means there were more than twice as many really really good on-ice save percentages for defensemen than we would expect by chance alone.

For forwards, there were just 10 who had their binomial chance at 5% or lower which equates to 5.81% so not far off of what we would expect.  We had 10 we expected 8.6.  There were 19 forwards with binomial chance <10% when we should expect 17 by chance.  Not a huge difference.  Conversely, there were 14 forwards with binomial chance >95% or 8.1% compared to the expected 8.6 players and there were 25 forwards above 90% when we should expect 17.

It seems the really good defenders are defensemen and the players most apt to hurt their goalies save percentage are forwards.

That was a pure numbers analysis, what if we looked at the players themselves.  Looking at the list of forwards with better than expected on-ice save percentages we see a lot of third line players that primarily play defensive roles (Pyatt, Malhotra, Moen, Pahlsson, Drury, Staal, etc.).  The bottom 25 forwards contain a lot of more offensive oriented players (Kovalchuk, Huselius, Vanek, Frolov, Malkin, Recchi, Stastny, Booth, Havlat, Nash, Kane, Crosby, Roy, etc.).  There is actually only a 0.04% chance (one in 2500) that Kovakchuk’s on-ice save percentage was due to luck alone.

Much the same can be said for the defensemen.  The defensemen that are  best at improving on-ice save percentage are often defensemen we consider to be defensive defensemen (Huskins, Scuderi, O’Donnell, Salvadore, Weaver, Vlasic, Martin, etc.) or elite 2-way defensemen (Lidstrom, Blake, Yandle, Pronger, etc.) and the ones at the bottom of the list are more offensive specialists (J. Johnson, Keith, Kuba, Sekera, Wideman, Liles, Visnovsky, Boyle, Streit, etc.).  Yes, this is more evidence that Jack Johnson is a horrific defensive defenseman.

All things considered, there does seem to be some order in the list and order is the enemy of luck and the binomial analysis indicates that there may be more going on than one would expect purely from luck.  It seems that players can, to some degree, influence on-ice shooting percentage.  We can’t credit, or blame, the goalies all the time.

Continue reading Defenders effect on Save % →

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Thoughts on PDO and Luck

By on February 5th, 2012

One of my beefs in the analysis and evaluation of hockey players is the notion that PDO (on-ice shooting percentage plus on-ice save percentage) can be used as a proxy for luck.  A perfect example of how PDO is used as a proxy for luck is this article by Neil Greenberg about the Washington Capitals.

For example, when Alex Ovechkin has been on the ice during even strength this season, the team has a shooting percentage of 8.2 percent and has saved shots at a rate of .917. So that makes his PDO value 999 (.082+.917=.999), which is almost exactly the league average. In other words, Ovechkin has seen neither very good nor very bad “puck luck” this season.

What’s useful about this metric is that it’s “unstable,” and over a large-enough sample will regress to 1000. Why 1000? Because every shot that is a goal is a shot not saved, and vice versa.

My beef with such an analysis is the notion that for all players PDO regresses to 1000 and any players with PDO above 1000 are lucky  and any players with a PDO below 1000 are unlucky.  While I do believe luck can influence PDO over small sample sizes, not all players have a natural PDO level of 1000 and there are two reasons why.

1.  Not all players play in front of perfectly average goalies which will have a major impact on the save percentage portion of PDO.

2. Players can drive shooting percentages.

To show you what I mean on point 2, I took 4 years (2007-08 to 2010-11) of 5v5 zone start adjusted data and grouped forwards based on their ice time over those 4 years and then calculated the on-ice shooting and save percentages and PDO for each group.  Here is what I found.

TOI (minutes) SH% SV% PDO
<500 7.5% 90.9% 983.5
500-999 7.9% 91.2% 991.2
1000-1499 8.0% 91.2% 992.2
1500-1999 8.2% 91.2% 993.4
2000-2499 8.6% 91.1% 997.0
2500-2999 9.0% 91.2% 1001.9
3000-3499 9.3% 91.2% 1004.4
3500-4000 9.8% 90.8% 1006.1
4000+ 10.4% 90.8% 1012.4

PDO varies from 983.5 up to 1012.4 depending on the group’s ice time.  This is largely driven by shooting percentage which varies from 7.5% to 10.4% with the players with the lowest amount of ice time having the lowest on-ice shooting percentage and the players with the most ice time having the highest shooting percentage.  Order is the enemy of luck so seeing shooting percentages ordered this nicely tells me something other than luck is happening.  Driving on-ice shooting percentage is a skill.  This means more talented players can have a natural PDO (the PDO that they should regress to) above 1000 and less talented players can have a nautral PDO below 1000.  Factor in the goaltending and a player could have a natural PDO well above or well below 1000.

Now, this is not to say that luck isn’t a factor in a players PDO, especially over small sample sizes, it’s just we can’t estimate that luck by assuming every players natural “regress to” PDO is 1000.  Daniel Sedin has a PDO of 1043 this season (through Thursday February 2nd).  Is it fair to suggest he has been luck and should see his PDO regress to 1000?  When you consider his4-year PDO is 1035 (and his 3 year PDO is 1054) probably not.  His natural, “regress to” PDO is probably not that far off his current 1043 PDO.  Now if you are talking about Todd Bertuzzi this season it’s a different story.  Through Thursday he had a a PDO of 1056 while his 4-year PDO is 994 and he hasn’t had a PDO above 1000 in any of the previous 3 seasons.  It is probably fair to presume that Bertuzzi’s natural regress to PDO is much closer to 1000, maybe even below 1000 in which case it is fair to conclude that Bertuzzi has probably been quite lucky so far this season and is unlikely to continue at this pace the remainder of the season.

When used properly PDO can be an indication of luck but to do so we need to consider the context of a players PDO, not just assume all players PDO’s will necessarily regress to 1000.

 

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Zone Start Effects on Stats

By on February 1st, 2012

Over the past week or so I have talked about a simple and straight forward method for taking into account variations in zone starts.  The method is to simply ignore the 10 seconds following an offensive or defensive face off.  By adjusting for zone starts in this manner we can see a fairly significant impact on stats and today I’ll take a look at what gets impacted and how.

To do this I took a look at 3 year data using the 2008-09, 2009-10 and 2010-11 seasons.  Using 5v5 data for players with at least 1000 minutes of ice time I identified the 25 players who had the highest percentage of their face offs in the offensive zone and the 25 players who had the highest percentage of their face offs in the defensive zone.   I then compared their 5v5 zone start adjusted stats to their non-adjusted 5v5 stats.  The statististics I looked at are on-ice goals for percentage, on-ice fenwick for percentage, shooting percentage, opposition shooting percentage, goals for per 20 minutes, goals against per 20 minutes, fenwick for per 20 minutes and fenwick against per 20 minutes.  The changes are as follows:

Top 25 OZPct Top 25 DZPct
GF% -1.17% 2.58%
FF% -0.99% 2.32%
SH% 15.00% 12.40%
OppSh% 15.31% 11.86%
GF20 2.40% 7.00%
GA20 4.69% 2.12%
FF20 -8.28% -2.89%
FA20 -6.36% -6.93%

What is interesting is that there are relatively small differences in GF% and FF% but differences in shooting percentages are very large (note that 15% change is from, for example, 10% to 11.5%, not the actual difference in shooting percentages).  Goal and fenwick event rates are somewhere in the middle but while goal rates rise when we ignore the 10 seconds after an offensive/defensive zone  faceoff, fenwick rates drop.  This means that while a lot of shots are taken in the 10 seconds after the faceoff, very few of those shots end up as goals.  As I mentioned yesterday, the league-wide shooting 5v5 percentage in the 10 seconds after the faceoff is around 3% while it is almost 9% the rest of the time.

Let’s look at some specific examples.  Henrik Sedin gets a lot of offensive zone faceoffs and as a result 19.6% of his fenwick against events come within the 10 seconds after an offensive/defensive zone faceoff but only 8.0% of his on-ice goals do.  In real numbers, Henrik Sedin was on the ice for 2634 fenwick for events and 523 occurred within 10 seconds of an offensive/defensive zone faceoff.  He was also on the ice for 212 goals for while only 17 occurred within 10 seconds of an offensive/defensive zone faceoff.

Manny Malhotra is the opposite of Henrik Sedin and gets a lot of defensive zone faceoffs.  As a result, 17.3% of all his fenwick events against occur within the 10 seconds after an offensive/defensive zone faceoff, but only 4% of his on-ice goals against do.  In real numbers, Malhotra was on the ice for 1710 fenwick events against at 5v5 over the past 3 seasons, but 296 came within 10 seconds of an offensive/defensive zone face off.  He was also on the ice for 75 goals against, but only 3 came within 10 seconds of an offensive/defensive zone faceoff.

What does this all mean?  It means that if you are doing a corsi/fenwick/shot/shooting percentage based analysis accounting for zone starts is really important because it can have significant impacts on these stats (less so for ratios though).  The impact on goals is much less significant but probably not something we would want to ignore depending on the analysis.  May as well use the 10 second zone start adjusted data for all player analysis.

 

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Stats.hockeyanalysis.com updated

By on January 31st, 2012

Just wanted to let you know that I have finally updated stats.hockeyanalysis.com to include 2011-12 data though I have not yet included multi-year data that includes 2011-12.

I have also included in this updated zone start adjusted data which adjusts for zone starts by not considering the 10 seconds following an offensive/defensive zone faceoff.  I have included both 5v5 and 5v5 zone start adjusted data and the 5v5 close, 5v5 tied, 5v5 up 1, 5v5 up 2+, 5v5 down 1 and 5v5 down 2 data are zone start adjusted.  It doesn’t make any sense to zone start adjust PP and PK so the 5v4 and 4v5 data is not zone start adjusted.

As always, if you have any issues or questions with anything at stats.hockeyanalysis.com let me know.

As an interesting aside on zone starts, I have noticed that zone starts affect shots/fenwick/corsi somewhat significantly but do not affect goal data much.  I thought this was strange at first but then the explanation became clear when I looked at shooting percentages.

Situation SH%
All 5v5 7.91%
ZS Adjusted 5v5 8.89%
10 seconds after Ozone faceoff 3.04%

Shots within 10 seconds of a faceoff don’t go in nearly as frequently as shots at any other time.  The reason for this is probably that the majority of these shots likely come from the point after an offensive faceoff win.  Also, the goalie is perfectly set and ready for the shot and the defending team has their players in optimal defending positions and are usually fully rested.

So, what does this mean?  It means you can actually probably pretty much ignore zone starts if you are looking at goal data.  Zone starts have very little influence on the rate at which goals are scored.

 

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Corsi vs Shooting %: Gomez vs Cammalleri

By on January 28th, 2012

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.

 

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Evaluating the Leafs Defensemen

By on January 26th, 2012

With the re-signing of John-Michael Liles the Leafs now have an abundance of defensemen signed under control for a number of years, many with big dollar contracts too.  We all have our varying opinions on the relative values of each of these defensemen but I thought it would be an appropriate time to take a closer look at them statistically.

Offensively

2011-12 HARO+ 2010-11 HARO+ 2010-12 HARO+ 2011-12 FenHARO+ 2010-11 FenHARO+ 2010-12 FenHARO+
JOHN-MICHAEL LILES 1.23 1.03 1.11 0.96 0.99 1.00
CODY FRANSON 1.20 1.06 1.10 1.05 1.05 1.03
LUKE SCHENN 1.10 1.08 1.08 0.85 1.02 0.99
DION PHANEUF 1.01 1.08 1.05 1.00 0.99 1.03
CARL GUNNARSSON 1.05 1.00 1.02 1.04 0.92 0.94
MIKE KOMISAREK 1.10 0.96 1.00 1.02 0.90 0.86
KEITH AULIE 0.90 1.02 0.99 0.78 0.86 0.89
JAKE GARDINER 1.18 0.94

The above list are my own offensive ratings (goal based and fenwick based) for 5v5 zone start adjusted (10 seconds) situations sorted by their year and a half (2010-12) HARO+ ratings.

The list generally fits with what we might expect though the one surprise is probably Luke Schenn being rated so highly offensively.  I had a debate with a few people last week where I suggested that Schenn is as good offensively as Phaneuf and got ridiculed for making that statement but the numbers do in fact support that.  The above are based on ‘on-ice’ numbers but individual stats make Schenn look good too.  This season Phaneuf has 2 even strength goals and 13 even strength points while Schenn has 1 even strength goal and 11 even strength points but Phaneuf has played more than 30% more even strength minutes than Schenn.  Last season Phaneuf had 5 goals and 17 points at even strength versus Schenn’s 5 goals and 21 points in 20% more minutes.  Combined Schenn has 6 goals and 32 points in 2237 ES minutes while Phaneuf has 7 goals and 30 points in 2207 ES minutes.  That’s awfully close offensive production if you ask me.  The difference in their overall totals is solely due to Phaneuf’s PP minutes and Schenn’s lack of them.

Getting back to the rest of the team, it is no surprise to see Liles and Franson at the top of the list.  They are known to be more offensive specialists and the stats bear that out.  The reverse is true for Komisarek and Aulie who are viewed as more defensive defensemen and that is the role they are assigned.  They simply do not produce much offense.  We only have half a season of Jake Gardiner, but so far so good.  While his fenwick offensive numbers aren’t crazy good, his HARO+ rating is very very good.  I think Gardiner is someone we can be cautiously optimistic will develop into a very good (maybe Liles-like) offensive defenseman.

For interest sake, here are the players raw offensive numbers for the last 2 seasons combined sorted by GF20.

2010-12 GF20 2010-12 FF20
LUKE SCHENN 0.88 12.62
CODY FRANSON 0.88 13.22
JOHN-MICHAEL LILES 0.87 12.66
DION PHANEUF 0.85 13.01
CARL GUNNARSSON 0.83 11.96
MIKE KOMISAREK 0.81 11.00
KEITH AULIE 0.81 11.41

Gardiner’s GF20 is 0.96 and FF20 is 12.62 so far this season.

Defensively

This is the defensive equivalent of the above offensive rating chart.

2011-12 HARD+ 2010-11 HARD+ 2010-12 HARD+ 2011-12 FenHARD+ 2010-11 FenHARD+ 2010-12 FenHARD+
CODY FRANSON 0.77 1.39 1.15 1.02 0.98 1.00
KEITH AULIE 0.71 1.22 1.05 0.89 0.87 0.85
DION PHANEUF 0.87 1.07 1.00 1.04 0.94 0.97
CARL GUNNARSSON 1.04 0.86 0.95 1.00 0.94 0.99
LUKE SCHENN 0.83 0.88 0.88 0.89 0.93 0.90
JOHN-MICHAEL LILES 0.85 0.86 0.87 1.00 1.00 0.99
MIKE KOMISAREK 0.78 0.74 0.76 0.90 0.93 0.95
JAKE GARDINER 0.94 0.97

There are definitely some surprises in the above list and there are probably some small sample size issues going on.  Franson looked awesome defensively last season but terrible this season when considering their goal based HARO+ numbers.  The same is true for Aulie, and to some extent Phaneuf while the reverse is true for Gunnarsson.  For each of them their Fenwick numbers are a little more consistent.

All-in all though, Franson looks like he could be a more than respectable defenseman defensively.  His fenwick ratings are pretty solid and his 2-year goal ratings are very good.  On the other side of the spectrum, Komisarek looks awful, regardless of whether you consider goal ratings or fenwick ratings.  This is not good for a guy who doesn’t produce offense either.  Luke Schenn’s defensive numbers are a little better than Komisarek’s but still not great, but at least he is producing offensively.

Again, for interest sake, here are each defenseman’s 2-year raw defensive numbers.

2010-12 GA20 2010-12 FA20
CODY FRANSON 0.67 13.06
KEITH AULIE 0.73 15.36
DION PHANEUF 0.78 13.48
CARL GUNNARSSON 0.83 13.30
LUKE SCHENN 0.88 14.51
JOHN-MICHAEL LILES 0.92 13.05
MIKE KOMISAREK 1.02 13.75

Gardiner’s GA20 is 0.80 and FA20 is 13.83 so far this season.

Contract Status and Moving Forward

Phaneuf and Komisrek are signed for 2 more seasons at $6.5M and $4.5M cap hits respectively.  Liles and Schenn are signed for 4 more seasons each at $3.875M and $3.6M cap hits respectively.  Carl Gunnarsson is signed for another season at $1.325M when he becomes an RFA and will be due a substantial raise.  Cody Franson is set to become an RFA this summer and will deserve a sizeable raise from his current $800K salary.  Jake Gardiner has 2 years left on his entry level deal with a $1.1M cap hit and Keith Aulie is an RFA this summer.  The Leafs also have Korbinian Holzer, Jesse Blacker and others in the farm system ready to make a push for a roster spot on the Leafs in the next year or two.

The Leafs salary cap hit for their defensemen next season will be $21M plus whatever Cody Franson gets on a new contract which quite likely will be around the $1.5-2.5M range.  That would bring their expenditures on defensemen to $23M which actually isn’t all that ridiculous if the salary cap is $65+M.  That said, if they are looking to free up salary to spend on forwards and/or are looking to open up a roster spot for their young defensemen there are a few options.

The first option is to trade (if possible) Mike Komisarek.  He provides no real value to this team but then he will probably provide no value to any team so trading him might be difficult.  He also has a limited no trade clause limiting the number of potential trade partners as well.  He would be a perfect candidate to have his contract buried in the AHL (in actual dollars he’ll earn $3.5M in each of the next 2 seasons and coincidentally Jeff Finger’s buried $3.5M contract expires this summer) but he has a no movement clause which means he cannot be demoted.  The only option to get his contract off the books is via trade.

Another option is to trade Luke Schenn.  He provides some value to the Leafs with his offensive ability but that is not an area where the Leafs are lacking (most of their defensemen have offensive capabilities).  His poor defensive numbers make him expendable in my opinion and being young and on a reasonably priced long term contract he should have a lot of value on the trade market.  He could feasibly be used in a package to land the Leafs the big two-way forward they desperately need.

The other options are trading either Franson or Gunnarsson.  Neither would save the team as much cap space as either Komisarek or Schenn but both would have good value on the trade market.  That said, I would not be a proponent of this as I think they both provide good value to the Leafs, and are likely to provide good value for many years.  Gunnarsson has developed into a solid all-purpose defenseman and I think Franson has that ability too.

 

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Importance of Quality of Competition/Teammates

By on January 25th, 2012

Whenever I get into a statistical debate over which player might be better than another the inevitable argument that comes up is “yeah, but player A plays against tougher competition and gets tougher assignments” which is a valid argument to make.  But how valid?  The other day I looked at a simple, straight forward method for accounting for zone start differences (which can be significant) and today I thought I’d take a look at quality of teammates and quality of competition.

Whenever I browse through my stats.hockeyanalysis.com site or in my own database I have always been curious about the general lack of variation in the quality of competition and to a lesser extent quality of teammate stats (especially over multiple seasons of data) and I thought it would be worth while taking a look at it more closely.

My stats site has a number of metrics that we can look at but let me define a few.

  • GF20 – Goals For per 20 minutes of ice time.
  • GA20 – Goals Against per 20 minutes of ice time.
  • TMGF20 – Weighted average (by ice time played with) of teammates GF20
  • TMGA20 – Weighted average (by ice time played with) of teammates GA20
  • OppGF20 – Weighted average (by ice time played against) of opponents GF20
  • OppGA20 – Weighted average (by ice time played against) of opponents GA20

I also have the same stats for fenwick as well identified with an F instead of a G in the above abbreviations.

So, let’s take a look at a players offensive capabilities.  Things that would affect a players GF20 are the players own offensive talents, the offensive talents of his teammates and the defensive talents of his opponents.  We know that not all players have the same talent level, but what about the talent levels of his teammates and his opposition?  What is the variation among them?

The above table shows the mean goal production (GF20) in blue along with lines representing + and – one standard deviation.  Also included is TMGF20 in green and OPPGA20 in red and their + and – standard deviation lines.  I have included data for one, two, three and 4 seasons of data and skaters with a minimum of 400 minutes of 5v5 ice time average per season.

As you can see, there is very very little variation in quality of opposition, almost to the point we can almost  ignore it.  The variation in quality of teammate is significant and cannot be ignored and while it seems to get reduced over time, it’s impact cannot be ignored even when using 4 years of data.

Here is the same chart except using fenwick stats instead of goal stats.

We see pretty much the same thing when we look at fenwick data as we do goal data.  There is very little variation in quality of opposition, but significant variation in quality of teammate.  What about on the defensive side of things?

Once again, the quality of opposition has very little variation across a group of players almost to the point that it can be ignored.

All of this tells us that when comparing/evaluating players, the quality of competition a  player faces varies very little from player to player and we should be really careful when we use arguments such as “Player A faces tougher quality of competition” because in the grand scheme of things, the quality of competition probably only has a very minor influence on Player A’s on-ice stats.  And if you think about it, this probably makes sense.  If you have a great offensive player, the theory is your opponents will want to match up their great defensive players against him.  But, at the same time you are trying to match up your great offensive player against their weakest defensive players.  When at home, you get the line matching advantage, while on the road your opponent does.  When all is said and done everything more or less evens out.

 

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Adjusting for Zone Starts

By on January 23rd, 2012

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.

 

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State of Brian Burke’s Leafs

By on January 18th, 2012

Brian Burke joined the Leafs in November of 2008.  When he joined the Leafs he insisted he has no interest in a 5 year rebuild and expected he could make the team competitive much sooner.  Let’s evaluate how Burke has done in his tenure as GM of the Maple Leafs.

2007-08 2011-12
GAA 3.08 (27th) 3.03 (27th)
SV% 89.3 (29th) 90.1 (24th)
GFA 2.74 (11th) 2.98 (6th)
PP 17.8% (15th) 20.6% (4th)
PK 78.0% (30th) 74.4% (30th)
Points 83 (12th in east) 89 (projected, 9th)

Their overall offense is slightly better but their defense is the same sad defense we had prior to Burke.  They are in the playoff hunt this season, but they are a dismal 13-15-3 in their last 32 games and showing little signs that if they can somehow squeak into the playoffs they can threaten to win a round.  They have just 7 wins against teams currently in the playoffs and the only playoff team they have defeated since December 5th is the Detroit Red Wings.

Up until recently I have been a supporter of Brian Burke but to be perfectly honest he is growing weary on me.  Yes, the team is younger, but no, it is not very much better.  Yes, there is greater prospect depth, but I am doubtful any of them have the potential to become game changers in the NHL (i.e. dominant core players).  He seems to think he has one of the best coaches in the NHL and gave him a contract extension but he also has talked recently about how he thinks his team is a playoff team and is only a player or two away from seriously challenging to be a top team that can make a lengthy playoff run.  He loves to talk about how the Phaneuf trade changed the franchise around, but since the Phaneuf trade the Leafs are just 72-62-19 or the equivalent of an 87 point team.  In the 6 seasons post lockout 87 points would get you 11th, 12th, 11th, 11th, 10th, and 10th.  I don’t know about you, but I am not satisfied with a 10th-12th place team, or even a 9th place team.

Other Leaf fans like to talk about how young this team and the rebuilding process isn’t complete (despite Burke insisting he had no interest in a 5 year rebuild) but lets look at their ages and experience.  I have included the top 18 skaters in total ice time this season and top 2 goalies.

Player Age GP
Dion Phaneuf 26 515
Carl Gunnarsson 25 155
Phil Kessel 24 419
Joffrey Lupul 28 494
Jake Gardiner 21 38
John-Michael Liles 31 557
Luke Schenn 22 275
Nikolai Kulemin 25 278
Mikhail Grabovski 27 284
Tyler Bozak 25 155
Tim Connolly 30 660
Clarke MacArthur 26 328
David Steckel 29 351
Cody Franson 24 171
Matt Frattin 24 38
Joey Crabb 28 111
Mike Komisarek 29 492
Matthew Lombardi 29 473
James Reimer 23 55
Jonas Gustavsson 27 88

Only Gardiner, Schenn and Reimer are under age 24.  The majority of the team is aged 24-26 with a few players in their late 20′s and Liles topping out at 31.  There are 12 players with 250+ games experience and 7 with 400+ games experience and only 4 players (both goalies, Frattin and Gardiner) have fewer than 100 games experience.  This isn’t a team filled with rookies with little or no experience, it is a young team but with a fair bit of NHL experience with the majority of players in their prime years or just entering their prime years.  Am I really expected to buy into the fact that this mediocre team of 24-29 year olds will suddenly become a great team of 26-31 year olds 2 years from now?  I am not so certain.

Furthering that challenge is that Grabovski, Liles and Gustavsson are UFA’s after this season and after next season Connolly, Lupul, Lombardi, MacArthur, Armstrong, Bozak, and Steckel are UFA’s.  That is 7 of your top 13 skaters in terms of ice time becoming UFA’s over the next 2 summers plus a handful of others.  This doesn’t appear to be a core of players that can win now and a good chunk of the core could walk away as free agents should they choose to.

All this begs the question, where do the Leafs go from here?  Do they stick with this core, re-signing the UFA’s and hope for the best, or do they admit that this completely revamped (from 3 years ago) team is only marginally better, still can’t keep the puck out of their own net, and may in fact need another significant overhaul?  And if it is the latter, should we leave that up to Burke?  To be fair, it is probably too early to pull the plug on this current Leaf team but from my perspective if Burke insists the problem is not the coach and the mediocrity continues, I am not sure how much longer we Leaf fans should wait.

 

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History of Poor Defensive Teams making Playoffs

By on January 15th, 2012

Not sure what led me to look into this but I took a look at poor defensive teams making the playoffs in the eastern conference.  Over the past 3 seasons there have been just 6 teams to make the playoffs in the eastern conference with goals against averages greater than 2.80.  They are:

  • Tampa Bay Lightning (2010-11):  2.80
  • Ottawa Senators (2009-10): 2.80
  • Pittsburgh Penguins (2009-10):  2.82
  • Montreal Canadiens (2008-09): 2.88
  • Washington Capitals (2008-09):  2.89
  • Ottawa Senators (2007-08): 2.92

Over the past 4 seasons there have been a total of 26 teams with gaa’s above 2.80 and just 6 of those made the playoffs (37.5%).  There have been 18 teams with gaa’s above 2.90 and only one team (the 2007-08 Senators) made the playoffs (5.6%).

What is interesting is that right now there are currently 4 teams in eastern conference playoff spots with goals against averages above 2.80.

  • Washington Capitals: 2.85
  • Philadelphia Flyers:  2.90
  • Toronto Maple Leafs:  3.03
  • Ottawa Senators:  3.07

There are actually only 6 teams in the eastern conference with GAA’s under 2.80 so at least 2 of them over 2.80 would have to be in the playoffs.  Those under 2.80 are the Bruins, Rangers, Penguins, Canadiens , Panthers and Devils.  If history is any indication that means Pittsburgh should be able to climb back into the playoff picture and who knows, maybe there is hope for the Canadiens (wouldn’t bet on it though).  But regardless, it appears there will be a few teams making the playoffs in the eastern conference with gaa’s above 2.80, and maybe even one or two above 3.00.  The only eastern teams to make the playoffs with a gaa above 3.00 post lockout are the Tampa Bay Lightning in 2006-07 (3.11 gaa) and 2005-06 (3.07), Carolina Hurricanes in 2005-06 (3.11) and Philadelphia Flyers in 2005-06 (3.04) but offense was significantly higher in those seasons.  Particularly in 2005-06 when only 5 teams had sub 3.00 gaa’s in the east, all making the playoffs.

I should also point out that of the teams that made the playoffs with a GAA above 2.80 in the past 4 seasons, both Ottawa teams missed the playoffs the following season, Tampa is certain to do so this season and Montreal squeeked into the playoffs in 2009-10 with just 88 points, the lowest point total for a playoff team post lockout.  Bad defensive teams don’t generally see much success and should they achieve some it is seemingly not a positive predictor of future success.