Feb 282012

I was somewhat disappointed in the lack of Maple Leaf moves made during yesterday’s trade deadline.  Not because Burke didn’t trade for anyone who can help us make the playoffs this year (though I though a trade for Ben Bishop would have been a useful gamble both short and long term) but because he didn’t do more to set up this team for the long term.  From my perspective this team is unlikely to make the playoffs this season and even if they do there is little chance they will win a round.  Their defense and goaltending is bad at best and physical defense oriented teams have the ability to shut down the Leafs offense.  The Leafs overall have a dreadful record against playoff teams.  So, Burke had an opportunity to take a team that had very little to be optimistic about this season and set it up for a better future.


The way the team is currently set up we have the following group of forwards:


The six players I have listed in bold are the only six players that I believe have a chance to be on this team playing the same roles they are currently in 2-3 seasons when the Leafs really should plan on having a competitive team.  And even with those six players Brown and Steckel are not core players and may or may not be kept around and Frattin still has to prove himself.  Let me address some of the guys I haven’t listed in bold.

Bozak – I kind of like Bozak, but lets be honest, he is not a first line center and he especially isn’t a first line center between Kessel and Lupul.  Neither Kessel nor Lupul are quality defensive players nor exceptionally big and putting a small and weak defensive center between them that doesn’t have elite level offensive skills is just not a recipe for success.  It’s possible that Bozak could be a second line center with the right wingers but if we are giving the second line role to Grabovski he doesn’t fit there.  He isn’t good enough defensively to be a third line center so I have a hard time to see where he fits in long term.

MacArthur – MacArthur is the left wing version of Bozak.  He has some nice offensive skills but really isn’t much more than a 50 point one-dimensional player.  He isn’t big, doesn’t play big, isn’t anything to write home about defensively.  He is a nice place holder until we get something better but nothing more.  I don’t see MacArthur playing a significant role on a Stanley Cup contender.  He just doesn’t bring enough of a complete package to play a significant role on a contending team.

Kulemin – Being realistic and setting last seasons career year aside, Kulemin is probably a defensively responsible 15 goal, 35 point player.  While he is no doubt a useful 2-way player he is probably more suited for a 2-way third line role.

Connolly – I like Connolly and I think on the right team with some big wingers he can be a quality second line center but he is really just a place holder on this team and probably doesn’t have a long term role in Toronto.

Lombardi – Lombardi was just acquired as a way to get Cody Franson and has no long term role in Toronto.

Armstrong – If Armstrong could stay healthy I think he can be a useful player.  Unfortunately he cannot stay healthy.

So, looking forward I think this could be the lineup when we are ready to compete including the holes we are looking to fill.


Needed are a first line center, preferably one with size and a defensive presence.  Two second line wingers, preferably at least one with good size to offset Grabovski’s smallish stature.  A 2-way third line center who can kill penalties.  At least one fourth line role player.

The plan, I presume now, is that at some point Colborne can be plugged into the first line role and Kadri and newly acquired Carter Ashton can be plugged into the second line winger roles.

The question I have is, why couldn’t we have started this process right now?  Speculation is that first round draft picks were offered for both MacArthur and Kulemin.  Would it really have hurt this team that much to have flipped MacArthur for a first round pick and seen what Kadri could do on the second line for the remainder of the season?  Kadri hopefully has a long term role with the Leafs, I am not certain MacArthur does.  With the Leafs ever get another chance to flip MacArthur for a first round pick?  I am not so sure.  The price for players was extremely high yesterday (see Gaustad, Paul) and I don’t think you get a first round pick for MacArthur in the summer.  Same goes for Kulemin, though I do think Kulemin could have a long term role with the Leafs, just not necessarily as a regular top six forward.  Wouldn’t it be nice going into this summers draft with two or more first round picks and the opportunities that provides either in drafting quality prospects for the future or packaging them in a trade?  Wouldn’t it be nice going into this summer with some kind of indication as to whether Kadri is ready for a regular role the NHL?  I can argue the same for Bozak.  If Bozak could have been flipped for a draft pick or a prospect, wouldn’t it have been nice to see if Colborne is ready for regular NHL duty?

I realize it is tough for Burke to admit to fans that this is more or less a lost season from this point on, but I don’t really think fans would be unhappy had he traded away Bozak and/or MacArthur and/or Kulemin and/or others for first round picks and prospects and said “we thought that we had a playoff team that could possibly even scare someone in the first round but it has become clear that the group was not coming together as expected so we decided to make some changes and give some of our quality prospects a chance to show they will be ready for the NHL next season.”  Some fans might be angry that they have on some level ‘given up’ on the season but I think the majority of them will see that as being far more realistic than what Burke actually said: “we still believe in this team.”

So, as far as I am concerned, yesterday was a lost opportunity to trade away some players that may not (or probably do not) have a long term future with the Leafs (MacArthur in particular) while the prices were as high as they may ever be for those players.  It is also a lost opportunity to give some NHL ready prospects such as Kadri and Colborne an opportunity to show their abilities at the NHL level.  Instead have to watch a bunch of guys who won’t be with the Leafs when they are ready to be serious contenders fail to make the playoffs and generally continue to disappoint with their underwhelming performance.  Mediocrity without optimism is not fun to watch and lets be clear, this team is mediocre and there aren’t many players on the team we can be optimistic will be better a year or two from now.  They are what they are and there isn’t much hope this group will get any better.


Feb 232012

The Columbus Blue Jackets have traded Jeff Carter to the Los Angeles Kings for Jack Johnson and a first round pick.  When Johnson signed his current 7 year, $30.5M contract I wrote how I thought the Kings would regret the contract.  Now I think the Blue Jackets will.

Just how bad is Jack Johnson?  Well, lets take a look at the Kings defensemen’s goals against per 20 minutes of zone start adjusted 5v5 close ice time over the past 2 3/4 seasons.

Defenseman 11-12 GA20 Defenseman 10-11 GA20 Defenseman 09-10 GA20
Martinez 0.471 Martinez 0.465 Harrold 0.418
Voynov 0.473 Greene 0.580 Greene 0.451
Doughty 0.495 Drewiske 0.635 Doughty 0.499
Mitchell 0.504 Harrold 0.715 Scuderi 0.517
Scuderi 0.557 Mitchell 0.764 O’Donnell 0.543
Greene 0.571 Doughty 0.785 Drewiske 0.682
Johnson 0.727 Scuderi 0.831 Jones 0.755
Johnson 1.054 Johnson 0.906

Not only is Jack Johnson dead last in all three seasons, he is last by a sizeable margin.  Of the 7 Kings defensemen this year the spread between #1 Martinez and #6 Greene is smaller than the spread between #6 Greene and #7 Johnson.  The previous two seasons look no better.

But what is even more scary are Johnson’s offensive numbers.  Yeah, Johnson may be a question mark defensively but his offense helps offset some of that.  Well, so we thought.  Here are the goals for per 20 minutes of ice time numbers for Kings defensemen.

Defenseman 11-12 GF20 Defenseman 10-11 GF20 Defenseman 09-10 GF20
Voynov 1.014 Harrold 1.072 Scuderi 0.791
Martinez 0.848 Mitchell 1.030 Doughty 0.774
Greene 0.694 Doughty 0.999 O’Donnell 0.744
Doughty 0.637 Martinez 0.802 Drewiske 0.744
Mitchell 0.612 Drewiske 0.714 Greene 0.708
Scuderi 0.590 Scuderi 0.712 Johnson 0.647
Johnson 0.485 Greene 0.709 Jones 0.519
Johnson 0.671 Harrold 0.314

Dead last in 2 of the three seasons and only ahead of Randy Jones and Peter Harrold in 2009-10.  Certainly not something to write home about.  So, if Johnson isn’t helping his team score goals and isn’t helping his team limit goals against, he has to have a pretty terrible goals for percentage (goals for divided by goals for + goals against).  Let’s take a look.

Player Name GF% Player Name GF% Player Name GF%
Voynov 0.682 Martinez 0.633 Greene 0.611
Martinez 0.643 Harrold 0.600 Doughty 0.608
Doughty 0.562 Mitchell 0.574 Scuderi 0.605
Greene 0.548 Doughty 0.560 O’Donnell 0.578
Mitchell 0.548 Greene 0.550 Drewiske 0.522
Scuderi 0.514 Drewiske 0.529 Harrold 0.429
Johnson 0.400 Scuderi 0.462 Johnson 0.417
Johnson 0.389 Jones 0.407

It’s pretty sad when every other defenseman on your team has a goals for percentage above 50% and you sit at 40%.

It seems that Jack Johnson is a major drag on his team, especially defensively, but offensively as well.  The only redeeming quality of Jack Johnson on the ice is that he seems to be a good PP specialist.  I have often called Jack Johnson the $4.3M/yr version of Marc-Andre Bergeron but that might be unfair to Bergeron.  Getting rid of Johnson is addition by subtraction plus you are getting Jeff Carter for a mere first round pick which is a steal.

The Kings win this trade by a country mile while the Blue Jackets have set back their franchise years by the whole Carter fiasco which has seen them trade Jakub Voracek, Sean Couturier and Nick Cousins (3rd round pick in 2011) for a horrid Jack Johnson and a mid first round pick.  Pretty sad for any Blue Jacket fan.


Feb 222012

Looking at this chart, I think only Lightning fans can sympathize with the torture that Leaf fans have suffered through with regards to their goaltending, but at least the Lightning have made the playoffs a few times and even had some success.

Update:  For interest sake, here are the post lockout shooting percentages and PDO (shooting percentage + save percentage).



Downie trade good for Stastny/Duchene?

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Feb 212012

Steve Downie was traded from the Tampa Bay Lightning to the Colorado Avalanche today for Kyle Quincey (who was later shipped to Detroit).  I featured Downie in a post I wrote on the weekend about mixing toughness with skill and how having a big, physical winger can make a skilled center more productive, especially a smaller skilled center.  Downie did this with Stamkos, St. Louis and to a lesser extent with Lecavalier.  The beneficiary of Downie’s toughness in Colorado will be either Paul Stastny or Matt Duchene.

Until last years trade deadline Stastny played with another big, physical winger named Chris Stewart.  To see how Stastny might benefit from Downie it might be prudent to see how Stastny played with and without Stewart.

GF20 With GF20 Without
2010-11 1.158 0.781
2009-10 1.139 0.951
2009-11 (2yr) 1.145 0.854

That is a 34% boost in on-ice offensive production with Stewart than without.  This season Stastny’s on-ice GF20 is a fairly low 0.723 so there is definitely a lot of room to see a significant increase in those numbers.  It’ll be interesting to see how they perform the remainder of the season but if you are in a hockey pool, you may want to take a chance and trade for Stastny or Duchene.


Feb 212012

There was a twitter conversation between Gabe Desjardins and David Staples last night in which Gabe suggested that Daniel Sedin’s heavy offensive zone start bias resulted in an additional 7-9 points that he would not have gotten if his zone starts were more evenly split between offensive and defensive zone.  When I saw this I immediately though that seemed like a really high number so I decided to take a look though the play by play sheets and see how many of Daniel Sedin’s even strength points came from a faceoff in the offensive zone.  Of all of Daniel Sedin’s points so far, here are the only ones that might at all be attributed to an offensive zone faceoff.

Date Opppnent Type Time After Faceoff
Oct. 15 Edmonton Assist 8 seconds
Oct. 20 Nashville Goal 11 seconds
Oct. 29 Washington Assist 19 seconds
Nov. 29 Columbus Goal 8 seconds
Dec. 6 Colorado Goal 24 seconds
Jan. 31 Chicago Goal 29 seconds
Feb. 18 Toronto Assist 40 seconds

Every other point that Daniel Sedin got was either on the PP, after a faceoff in another zone or after a line change during the play or after the opponent had possession of the puck.  Even the points above we don’t know if the opposition had control of the puck between the faceoff and the goal, especially for the plays 19 seconds or longer after the faceoff (a lot can happen in 19 seconds) and the goal vs Colorado was during 4 on 4 play as well.  But for the sake of argument, let’s say we can directly tie all 7 of those points to being a result of offensive zone face offs.  Also, for the sake of easy math, let’s assume his OZone% is 70% (it’s actually closer to 80%).  So, on 70% OZone starts he scored 7 goals.  If we reduce his Ozone% to 50% you’d naturally think you’d lose an equivalent portion of points so he’d end up with 5 points instead of 7.  Net result, Daniel Sedin’s offensive zone start bias has accounted for just 2 additional points so far this season.

What about previous seasons?  Well, over the previous 3 seasons Daniel Sedin was on the ice for 197 5v5 goals for.  If we ignore the 30 seconds following an offensive or defensive zone start (and 30 seconds is more than ample to account for zone starts) he was on the ice for 151 goals for.  That means we can fairly safely assume that offensive zone starts at best resulted in 46 goals for.

Now, over the past 3 seasons Daniel Sedin was on the ice for 1164 offensive zone face offs and 656 defensive zone face offs for an OZone% of about 64%.  Those 1164 offensive zone faceoffs accounted for at most 46 goals meaning approximately every 25 offensive zone starts resulted in a goal.  If Sedin had a 50% OZone% over the previous 3 seasons instead of his 64% he’d have been on the ice for about 910 offensive faceoffs, or about 254 fewer than he actually had.  Since every 25 offensive zone starts results in a goal those 254 extra offensive zone face offs he took resulted in approximately 10 extra goals being scored.  So, on average Daniel Sedin was on the ice for 3-4 extra goals per season because of his offensive zone faceoff bias, and that is being generous with the math.  That result is not far off this seasons observations above.

So, considering one of the best offensive players in the game with one of the most significant offensive zone biases in the game is only on the ice for at most an additional 4 goals a season as a result of their offensive zone bias, I think we can chaulk up the zone start effect as mostly insignificant.  The majority of players aren’t near as talented as D. Sedin and his linemates are and the majority of players end up having between 45% and 55% zone starts.  As a result, the majority of the players probably only see a zone bias affect their stats by at most one or two goals a season.  It’s pretty much not worth consideration.

Of course, a corsi based analysis would show a more significant difference because zone starts affect corsi more than goals.


Mixing in toughness with skill.

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Feb 182012

The other day at Pension Plan Puppets there was a discussion about the merit so Steve Downie and whether the Leafs should go after him if Tampa made him available.   In it I brought up the fact that when Steven Stamkos or Martin St. Louis or Vincent Lecavalier play with Downie their offensive numbers increase, sometimes dramatically.  The following table shows each players goals for per 20 minutes of ice time in 5v5 zone-start adjusted situations with and without Steve Downie on the ice with them.

Teammate With Without % Impr.
2011-12 Stamkos 1.481 1.19 24.5%
St. Louis 1.441 1.182 21.9%
Lecavalier 1.408 1.139 23.6%
2010-11 Stamkos 1.297 1.122 15.6%
St. Louis 1.246 1.06 17.5%
2009-10 Stamkos 1.284 0.903 42.2%
St. Louis 1.257 0.868 44.8%
Lecavalier 1.167 0.849 37.5%
2008-09 Stamkos 1.113 0.685 62.5%

This year Downie has improved their offensive production between 20 and 25% and it isn’t because Downie is an elite offensive player relative to those three guys.  He only has 12 goals and 28 points in 54 games this year and is Tampa’s 6th leading scorer behind the three listed above and Purcell and Malone.  In his best season, 2009-10, he only had 22 goals and 46 points so he is far from an elite offensive player at the individual level and yet he does something that makes his skilled linemates better.

I have always been interesting in exploring the optimal way to build a team and so this prompted me to look a little deeper to see if mixing in some toughness in the form of a winger with good size (not a ‘goon’) but not necessarily elite offensive skill with more pure skill players makes the skill players better.   So, here are a few more examples that I found interesting.

Alex Burrows

Burrows Teammate With Without % Impr.
2011-12 D. Sedin 1.052 1.017 3.4%
H. Sedin 1.112 0.92 20.9%
2010-11 D. Sedin 1.365 1.06 28.8%
H. Sedin 1.277 0.982 30.0%
2009-10 D. Sedin 1.481 2.126 -30.3%
H. Sedin 1.321 1.994 -33.8%
2008-09 D. Sedin 1.534 0.851 80.3%
H. Sedin 1.434 0.94 52.6%

Milan Lucic

Lucic Teammate With Without % Impr.
2011-12 Krejci 1.182 0.607 94.7%
2010-11 Krejci 1.232 0.613 101.0%
2009-10 Savard 0.768 0.676 13.6%
2008-09 Savard 1.277 1.098 16.3%

Drew Stafford

Stafford Teammate With Without % Impr.
2011-12 Roy 0.939 0.585 60.5%
2010-11 Roy 1.137 0.926 22.8%
Connolly 1.407 0.765 83.9%
2009-10 Roy 0.913 0.876 4.2%
Connolly 1.318 1.01 30.5%
2008-09 Roy 0.965 0.809 19.3%
Connolly 1.384 1.195 15.8%
2007-08 Roy 1.415 1.235 14.6%

Ryan Clowe

Clowe Teammate With Without % Impr.
2011-12 Couture 0.984 0.722 36.3%
2010-11 Couture 0.933 0.733 27.3%
Pavelski 1.062 0.791 34.3%
2009-10 Pavelski 0.863 1.24 -30.4%
2008-09 Pavelski 0.922 0.557 65.5%

Scott Hartnell

Hartnell Teammate With Without % Impr.
2011-12 Giroux 1.286 0.812 58.4%
Jagr 1.309 0.683 91.7%
2010-11 Briere 1.169 1.064 9.9%
Leino 1.053 0.987 6.7%
Giroux 1.205 1.038 16.1%
2009-10 Briere 0.918 0.931 -1.4%
Carter 1.04 0.606 71.6%
M. Richards 0.719 0.707 1.7%
2008-09 Carter 1.299 0.699 85.8%
Lupul 1.272 0.327 289.0%
M. Richards 1.016 0.911 11.5%
2007-08 M. Richards 0.696 1.214 -42.7%
Briere 0.883 0.581 52.0%
Carter 0.936 0.754 24.1%
Lupul 1.305 0.923 41.4%

Dustin Penner

Penner Teammate With Without % Impr.
2011-12 Stoll 0.644 0.408 57.8%
M. Richards 0.577 0.494 16.8%
2910-11 Hemsky 1.004 0.714 40.6%
Cogliano 0.856 0.545 57.1%
2009-10 Gagner 1.063 0.653 62.8%
Brule 1.505 0.782 92.5%
Cogliano 1.171 0.669 75.0%
2008-09 Horcoff 1.123 0.724 55.1%
Hemsky 1.32 0.658 100.6%
Cogliano 1.09 0.671 62.4%
2007-08 Hemsky 0.974 0.741 31.4%
Horcoff 1.015 0.696 45.8%

Brenden Morrow

Morrow Teammate With Without % Impr.
2011-12 Ribeiro 0.887 0.642 38.2%
Eriksson 1.055 0.935 12.8%
Ott 1.069 0.880 21.5%
2010-11 Ribeiro 1.104 0.365 202.5%
Benn 1.040 0.900 15.6%
2009-10 Ribeiro 0.911 0.507 79.7%
Benn 0.923 0.692 33.4%
2007-08 Ribeiro 1.196 0.384 211.5%
Mietinen 1.087 0.808 34.5%

Nathan Horton

Horton Teammate With Without % Impr.
2011-12 Krejci 1.298 0.455 185.3%
2010-11 Krejci 1.276 0.632 101.9%
2009-10 Weiss 1.153 0.525 119.6%
2008-09 Weiss 1.141 1.094 4.3%
2007-08 Weiss 1.191 0.543 119.3%

Save for a small number of player combo seasons the big strong wingers made their smaller skilled linemates (particularly the centermen) better offensive performers and while most of the players I looked at above are quality players no one will really call them elite offensive stars that can carry an offense.  They are at best secondary top line players or second liners.  Now it could be that when some these guys are not on the top line they are replaced with a third line player who brings down the production of the top line but there does seem something happening here that makes me think if you have a small, skilled center you should really look to pair him up with a big, strong, winger even if that winger is less talented.



What is Rick Nash?

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Feb 142012

So word has come out over the last day that Rick Nash is, at least on some level, available in a trade from the Blue Jackets.  So, the question is, who is Rick Nash and would you want him on your team?

Nash has been a Blue Jacket from the day he was drafted first overall in 2002.  He has played 648 regular season games and has scored 277 goals and 527 points.  Since the lockout he is 10th in goals (only Ovechkin, Kovalchuk, Heatley, Iginla, Staal, Lecavalier, Marleau, Vanek and Hossa) and 25 in points.  He has a pair of 40+ goals seasons and has been a 30+ goal scorer six times.  He has just 4 NHL playoff games under his belt when he scored 1 goal and a pair of assists.  He was a member of the 2010 Canadian Olympic team scoring a pair of goals and 3 assists in 7 games on route to the gold medal.  That is the raw facts that we all know about Nash.  But what about advanced statistics.

Here are my HockeyAnalysis ratings for Rick Nash over the past 4 seasons plus this season as well as his 2007-11 four year average.

2007-08 2008-09 2009-10 2010-11 2011-12 2007-11 (4yr)
HARO+ 0.991 1.070 1.257 1.502 1.079 1.200
HARO+ rank 142/235 118/241 59/245 8/260 116/229 60/217
HARD+ 0.827 0.992 0.802 0.882 0.732 0.895
HARD+ rank 164/235 96/241 196/245 162/260 197/229 162/217
HART+ 0.909 1.031 1.030 1.192 0.905 1.047
HART+ rank 172/235 115/241 123/245 36/260 169/229 95/217

HARO+ is an offensive rating, HARD+ is a defensive rating and HART+ is his total/overall rating which is simply an average of his HARO+ and HARD+ ratings.  These ratings are for 5v5 close zone adjusted situations and the rank includes any players who played 400 ore more minutes in single seasons, 300 minutes for 2011-12 partial season (through this past Saturday’s games) and 1500 minutes for the 4 year average.  These ratings take into account quality of teammates and quality of competition.


Overall in 5v5 close situations Rick Nash looks to be a solid offensive player, but not elite overall and defensively he is relatively weak.

To put Nash’s 4 year numbers in perspective, the most closely ranked players in terms of HARO+ are Cammalleri, Weiss, Hemsky, Jussi Jokinen, Vanek, Boyes, Bertuzzi, Grabovski, Alfredsson and Parise.

How about Nash’s 5v4 power play numbers.

5v4 HARO+
2007-08 1.010
2008-09 0.853
2009-10 1.203
2010-11 0.902
2011-12 0.951
2007-11 (4yr) 0.967
2007-11 rank (500 min.) 154/184
2007-11 rank (750 min.) 92/99

Generally speaking, his PP numbers are quite poor relative to other top PP forwards.

An interesting comparable is Joffrey Lupul.  It is an interesting comparable because it is quite likely that the Leafs will have an interest in Rick Nash and also because Lupul is an interesting case because he has really had a break through season this year.  Or so it seems anyway.

Nash Lupul
2007-11 5v5close HARO+ 1.200 1.385
2007-11 5v5 HARO+ 1.080 1.118
2007-11 5v4 HARO+ 0.967 1.246

It’s interesting that Joffrey Lupul ranked better than Nash in each of the three categories.  Due to injury Lupul didn’t put up 1500 minutes of 5v5 close ice time (he had 1374:44), but of all 251 players to play 1350 minutes of 5v5 close ice time Lupul ranked 10th.  When looking at these numbers it is actually not a surprise to see Lupul tied for 5th in points and 17th in goals.  He is finally being given an opportunity to play big time first line minutes with offensive zone starts and #1 PP unit ice time and as a result, he is producing.

So, getting back to Nash, let’s take a look at how he has done with his various linemates over the previous four seasons.  Here are the scoring rates (goals for per 20 minutes) for all the forwards who have played at least 250 minites of 5v5 close zone adjusted minutes during the 2007-11 four year time period.

Linemate TOI Together Nash /wo Linemate Linemate /wo Nash
Huselius 969:45 0.969 0.938 0.907
Vermette 607:35 0.757 1.016 0.782
Umberger 448:34 0.803 0.985 0.845
Brassard 441:22 1.359 0.860 0.930
Voracek 426:33 1.313 0.873 1.020
Malhotra 425:06 0.894 0.963 0.790

Nash played best when he was paired up with Voracek and Brassard and only Voracek, Brassard and Huselius made Nash a better offensive player when playing with him.  Vermette, Umberger and Malhotra were drags on his offensive numbers.  When playing apart, Voracek’s numbers are better than Nash’s.  Same for Brassard’s (who is doing it again this year, 0.782 GF20 vs Nash’s 0.613 when apart).  As an aside, the numbers suggest that Voracek is a very good offensive player  and it was probably a big mistake to trade him.  It also suggest that the Flyers aren’t getting full value from him by playing him primarily with Maxime Talbot.  If someone acquired Voracek and put him in the right situations, he could be the next Joffrey Lupul.

So, to summarize, yes Nash is a good offensive player who may put up better numbers playing with better offensive players but he is probably not an elite offensive forward.  Also, he isn’t a great defensive forward so offense really is what you get him for.  If I were Columbus I would be willing to trade him if I can get a quality NHL ready player capable of playing in their top 6 forwards, a top tier prospect and a first round pick.  If I were other teams, I would be very wary of over paying because he is not an elite player but he is paid like one ($7.8M cap hit for 6 more seasons).



Feb 092012

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 in front of.  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 »

Feb 052012

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.


Zone Start Effects on Stats

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Feb 012012

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.