Standings in “Points Blown” format

Standings after tonight’s games, looking only at the number of points each team has blown out of the total points possible. Looking at the standings this way, the lowest point total is best, since it means you have blown the fewest points (a score of zero would be perfect, no losses, no OTLs or SOLs). The number in parenthesis is the official, standard rank of the team, per ESPN. Note that everyone is in a different spot, except Colorado and Anaheim.

1. Calgary 15 (2)
2. Chicago 15 (3)
3. San Jose 16 (1)
4. Colorado 21 (4)
5. Nashville 21 (6)
6. Los Angeles 22 (5)
7. Columbus 22 (10)
8. Detroit 22 (9)
9. Phoenix 23 (7)
10. Dallas 23 (8)
11. Vancouver 24 (10)
12. St. Louis 25 (11)
13. Anaheim 26 (13)
14. Minnesota 27 (15)
15. Edmonton 30 (14)

Quick vs. LaBarbera [UPDATE post-Thrashing]

[UPDATE: after last night's 7-0 fun fest, I thought I would revisit Q's numbers to see where he stands vis-a-vis LaBarbera. The post has been altered accordingly:]

Halfway through Quick’s next game, he will have played exactly the same number of minutes LaBarbera played for the Kings last season. Q has now played about a game’s worth of minutes more than JLB did for us last season. How do they compare?

LaBarbera:

995 minutes / 2.83GAA / .893 SV% / 2 shut outs / 47 goals allowed

Quick:

966  minutes / 2.61GAA / .900 SV% / 0 shut outs / 42 goals allowed.

1061 minutes / 2.77GAA / .894 SV% / 0 shut-outs / 49 goals allowed.

Some other observations: LaBarbera allowed four or more goals in four games, 4, 6, 4, 4, all losses. Quick has allowed four or more five six times, 6, 4, 4, 4, 4, 4 (plus 3 in the Atlanta game in 35 minutes) but the Kings won two of those games. As noted above, JLB had two shut outs to Quick’s zero. JLB allowed only one goal in two complete games and twice when coming in relief of EE. Quick has allowed only one goal five times, all complete games. JLB was pulled three times. Quick has never been pulled has been pulled once. Shots against are roughly even: JLB faced 439 shots; Quick has faced 464.

Conclusion: I instinctively believe Quick to be far superior to JLB. The numbers, however, aren’t persuasively on my side. Quick’s numbers are slightly better overall. I made the observation on a message board that if Quick had let in one fewer goal in each of his games, his numbers would be best in the league (this was in response to the comment that there have been goals in every game that Quick would like to “have back”). The point being, I don’t think it’s realistic or fair to expect him (or anyone) never to let in a soft goal. But I do think it’s reasonable for him to cut down on the number of unforced errors leading to goals. Maybe by half. That would be consistent with our expectations of him in comparison to the often-maligned JLB.

[UPDATE: since the original post, he's let in seven goals in a game and a half. Going, as they say, in the wrong direction.]

Scoring Benchmarks After Game 19

Before the season started, I offered up the hypothesis that, if the Kings were to make the playoffs, they needed to increase their scoring to 246 goals and decrease their goals against to 230. Subsequently, I offered up a template for how I thought the Kings could get to the 250 goal mark. (I rounded up.) I assigned each player what I thought to be a reasonable benchmark. Here is the chart I came up with, back in the pre-season (right):

Now, here’s how the team and the players are doing nineteen games in (below):

The Kings have scored five more goals than they need to to be on track for 250. At this rate, they will score 276 goals, 10% over the target. What’s remarkable is who is doing the scoring and who’s not. The chart is sorted by number of goals over or under the target for the season. The five players at the top (dark green) are scoring much more than I thought it was reasonable to expect (obviously Parse was not even on anyone’s radar, but as the chart is sorted by the over/under, he jumps way up into the elite group). I budgeted Kopitar for 35 goals. He’s on pace for 60. I budgeted Smyth for 25 goals. He’s on pace for 39. Handzus and Simmonds are also exceeding my expectations by a long shot. Doughty’s surplus, combined with goals from Jones and Drewiske, essentially washes out the underages from Johnson and Greene — however, Doughty is doing marginally better than I thought he would, while Johnson is doing much worse than I hoped. Lewis and Moller are both in Manchester, of course, so their projected numbers suck. The numbers for the “kids” who are playing now (Parse, Simmonds and Purcell) don’t quite off-set the goals not being scored by Lewis and Moller, but it’s close. And then there’s Frolov, who I pegged for 35 goals and who is on pace for 17. Luckily, Kopitar, Smyth, Handzus, and Simmonds have scored 14 goals more than budgeted, which more than makes up for Frolov’s lack of production (-4).

It’s Never Too Late For the Wheels to Fall Off

I was just looking at the standings, the ones that have the Kings in 5th in the Western Conference. I noticed that they have played more games than most teams behind them. That’s not good. Let’s look at the standings a couple of different ways. First, the tradition way, by points:

1. SJS – 28
2. COL – 26
3. CHI – 20
4. CGY – 23
5. LAK – 22
6. CBJ – 20
7. PHX – 20
8. DAL – 20
9. VAN – 20
10. EDM – 18
11. DET – 17
12. STL – 16
13. NSH – 15
14. ANA – 14
15. MIN – 14

Now here it is by winning % (actually it’s points-per-game, which is WIN% times 2):

1. SJS 1.474
2. COL 1.444
3. CHI 1.250
4. CGY 1.438
5. CBJ 1.250
6. LAK 1.222
7. PHX 1.176
8. DAL 1.176
9. DET 1.133
10. VAN 1.053
11. EDM 1.000
12. STL 1.000
13. NAS 0.938
14. ANA 0.933
15. MIN 0.824

Kings move down one to 6th. Wings jump from 11th to 8th. Now, here’s a third method, which may be crackpotty, and I’ve only ever seen it used by me. Ignore it if you want to. It’s related to WIN%, but not. It’s points-lost. If you are 6-2-0, your total is 4 (2 losses times 2 points each); if you are 0-2-0, your total is also 4. The difference between this and WIN% is a team gets no bonus for having played more games. It’s all about the points you’ve blown. (In these standings, a lower point total is better, because obviously the fewer points blown the better.)

1. SJS 10
2. COL 10
3. CGY 11
4. CHI 12
5. CBJ 12
6. DET 13 (up 5)
7. LAK 14 (down 2-4; depending on how you break the three way tie)
8. PHX 14
9. DAL 14
10. STL 16 (up 2)
11. ANA 16 (up 3-4)
12. NSH 17
13. VAN 18 (down 4)
14. MIN 20

I don’t think I missed any of the teams that moved two or more spaces. What leaps out at me is this: (1) DET is doing much much better than people think. (2) ANA is not doomed. (3) The Kings are doing well but not as well as it seems. (4) The Kings are in a three-way tie for 7th, which is to say they are a hair away from 10th. (5) They are two points, i.e. one loss, away from 12th. (6) They are also one win away from 4th.

This early in the season, I find it useful to keep in mind what a win or a loss does to a team’s position in the standings. There’s a big difference between being in 1st and so far ahead that a few losses won’t hurt you, and (as SJS is) being in 1st such that one loss could drop you to 6th. So I prefer to think of a team’s position in the standings as a range, the range being (at the top) where you could be tomorrow morning if you win and the right teams lose, and (at the bottom) where you could be if you lose and the “right” teams win.

Looking at it that way, the Kings’ range is between 4th and 12th in the standings.

Bernier v. Quick in Manchester

Quick:

33 games / 17-13-2 record / 2.32GAA .922 SV% (‘07, 19g), 2.68 GAA .919 SV% (‘08, 14g)

Bernier:

67 games / 30-28-6 record / 1.63GAA, .946 SV% (‘07, 3g), 2.40GAA, .914 SV% (‘08, 54g), 1.67GAA, .953 SV% (‘09, 10g)

Observations: Bernier has played twice as many AHL games. Bernier has a better GAA and SV% at the AHL level. And keep in mind with SV% that Bernier faces fewer shots than Quick, all things being equal, because Bernier gives up fewer rebounds than Quick. Last season, at the time of Quick’s call-up to the Kings, it was noted somewhere that Quick was facing 6 more shots per game than Bernier, and this was attributed to the fact that Quick himself was creating these extra shot opportunities.

How much more seasoning does Bernier need before he’s called up? I’m not saying it’s necessary now, or even desirable. But ask me after Christmas.

Colorado’s Hot Start – Behind The Net

More great stuff from Behindthenet:

Since 1967-68, there have been 36 teams that have started the season with a winning percentage between .769 and .846, essentially one more win or one more loss than the Avs.  The second WPCT is Colorado’s pythagorean winning percentage, based on its goals for and goals against.

Here’s how they finished the season – some numbers may not add up perfectly due to rounding:

	W	L	T	WPCT	Pts	GF	GA	WPCT
Start	9	2	2	.793	22	54	32	.743
Rest	34	21	13	.594	88	245	201	.599
Total	44	23	15	.626	110	299	233	.624

I’ve assumed that OT and SO performance is pure luck, and awarded 1.5 points per game that ends tied after regulation.  Teams that have started hot have generally been pretty good – 110 points over a full season would be an amazing outcome for the Avalanche, a 41-point improvement over last season’s disaster.  Note that the GF and GA totals clearly reflect other eras in the history of hockey!

Now that’s the average performance.  What about the range of possible performances?

	W	L	T	WPCT	Pts
Average	44	23	15	.595	110
-1 stdev	36	29	17	.543	98
Worst	31	33	18	.488	89

We can be completely sure that the 2009-10 Colorado Avalanche are not the 1976 Montreal Canadiens, who went 58-11-11 and whose 9-2-2 start was actually worse than their record the rest of the way.  So if we compare the Avalanche only to the teams whose record over the rest of the season was in the bottom half of our sample, what does their playoff performance look like?

Miss	1st Rd	2nd Rd	3rd Rd	Finals	Win Cup	Avg # Playoff GP
2	10	2	2	0	2	8.3

So even among the bottom half of this group, we still have some very good teams – the cup winners were the 1998 Red Wings and the 1983 Islanders, both of whom swept the finals.  But 2/3 of our comparative group either missed the playoffs or didn’t make it out of the first round.

If Colorado finishes with 98 points (the -1 standard deviation record shown above), 6th in the West, and makes a 1st-round playoff exit, their fans should be overjoyed.  There’s really very little precedent for a team starting the season this hot and not actually being very good – except the 2008-09 Avalanche was, by six losses, by far the worst team ever to have this hot a start to the next season.

As best I can tell, NHL futures have the Avalanche in the middle of a dogpile at around 90 points, just outside of the last playoff spot.  In other words, the people who have money riding on this are giving Colorado 50/50 odds to be the worst team ever to have this good a start to the season.

Colorado’s Hot Start – Behind The Net.

Just a Bit More Perspective on Winning and Losing Streaks – Behind The Net

Since the NHL expanded to 12 teams in 1967-68, thirty-one franchises have played 908 total seasons. If you had to guess, what percentage of those seasons do you think included a 10-game stretch like the Leafs just had, with only one win in regulation? Would you believe 541, or 60%? And how many teams had a 13-game stretch, like the Colorado Avalanche, where they only lost one game or less in regulation? Still a very high 32%. And the number of seasons in which a team did both?! 10.5%.

That seems amazing to me – 1 out of every 10 teams has a stretch that’s as bad as the Leafs just had and as good as the Avs just had – in the same season! Even more amazing – six of the last sixteen Stanley Cup winners had both stretches during their championship season.

via Just a Bit More Perspective on Winning and Losing Streaks – Behind The Net.

New Skate-Sharpening Method Takes Hockey by Storm – NYTimes.com

New Skate-Sharpening Method Takes Hockey by Storm – NYTimes.com.

Exclusive: Kings Hits Per Sixty Minutes of Icetime

	m/h	h/60
Brown	3.2	19
Moller	3.9	15
Ivanans	5	12
Lewis	5.5	11
Greene	5.8	10
Richardson	7.4	8
Parse	7.7	8
Johnson	8.3	7
Harrold	9.6	6
Williams	10.2	6
Kopitar	13	5
Doughty	13	5
Drewiske	13.3	5
Purcell	13.4	4
Simmonds	13.8	4
Frolov	13.8	4
Handzus	15.6	4
O’Donnell	15.9	4
Scuderi	17	4
Smyth	21.7	3

 

First column is how many minutes go by before a player hits someone. Second column is hits per sixty minutes of ice time. What jumps out at me is Moller and Parse.

Kings forwards, QUALCOMP and plus/minus numbers

QUALCOMP – Kings Forwards (who faces the toughest opponents):

1. Parse
2. Kopitar
3. Smyth
4. Richardson
5. Brown
6. Purcell
7. Ivanans
8. Stoll
9. Williams
10. Simmonds
11. Frolov
12. Handzus
13. Lewis
14. Moller

Notice that the Handzus line is still facing the weakest competition, counter-intuitive if you believe they are really our checking line (I don’t). Toughest assignments are going to the #1 unit. The Stoll/Purcell line is right in the middle, as they have been all year (except for the first couple of games, where they were at the top of the list).

Here are the numbers for GAON/60 (Goals Against a player is on the ice for, per 60 minutes of icetime):

1. Moller 0.00
2. Simmonds 0.73
3. Purcell 1.49
4. Stoll 1.78
5. Handzus 1.96
6. Williams 2.15
7. Ivanans 2.40
8. Frolov 2.43
9. Kopitar 2.57
10. Richardson 2.68
11. Smyth 2.85
12. Brown 2.89
13. Lewis 4.23
14. Parse 5.19

Notice that Simmonds, Purcell, Stoll and Handzus have the best defensive numbers here. I cite this to counter people who don’t understand what Purcell is doing right this year. Now, here’s +/-ON/60 (which is plus/minus per 60 minutes):

1. Kopitar 2.20
2. Williams 2.15
3. Simmonds 1.82
4. Smyth 1.78
5. Purcell 1.49
6. Stoll 0.89
7. Handzus 0.39
8. Moller 0.00
9. Parse 0.00
10. Frolov -0.40
11. Ivanans -2.40
12. Richardson -2.68
13. Lewis -4.23

Again, I notice how high Purcell is on this list. Parse’s numbers are dead even, despite the above-mentioned high GA/60. He got screwed on a couple of those goals against, so I have to think these numbers are very good for him. Frolov’s numbers are not good, but they were much much worse before the benching, so that’s also a net (very) positive. I believe, since he sat, Frolov’s numbers have come up +2, which is a big change for a handful (2?) games.

After 10 games, 12 points; is that good?

Historically, the Kings have made the playoffs in 23 of their 41 seasons. 56% of the time.

They have gotten exactly 12 points in their first ten games nine times. In those nine seasons, they went to the playoffs five times (56% – exactly the overall average).

They have gotten more than 12 points six times, out of which they made the playoffs four times (67%).

They have managed less than 12 points twenty-six times, out of which they made the playoffs fourteen times (54%).

Which I guess means that doing this well doesn’t improve the Kings chances by much (+2%). But doing any better than this increases the odds by more than 10%.

In the post-99 era, the Kings hit 12 or greater three times: 14 points in 05-06, 13 in 02-03 and 12 in 99-00. In the 99 era, they did it six times (failing twice): 12 in 88-89, 15 in 90-91, 12 in 91-92, 13 in 92-93, 12 in 93-94, 12 in 95-96. Pre-99, they did it six times, including the franchise best 17 points in 80-81. That team’s record after ten games was 8-1-1. It was Jim Fox’s rookie season, in which he played 71 games, tallying 18 goals and 24 assists.

Quick Breaks the 3.00 GAA and .900 SV% Barriers

As of tonight, he’s at 2.88 GAA and and even .900 SV%. I guess that’s not quite “breaking” that second one, but so what. It’s good.

via Jonathan Quick, Kings – Stats – Los Angeles Kings – Team.

How many times have the Kings been at .500 after 8 games, and how many of those teams make the playoffs?

Eight times. Half of them make it. Kings are a playoff team 50% (4/8) of the time when they’re at .500 after eight games. If their record is better than .500, they make the playoffs 71% (12/17) of the time. If worse than .500, they make it 44% (7/16) of the time.

From The Frontal Cortex: Should You Go For It On Fourth Down?

Consider some research done by David Romer, an economist at UC Berkeley, who published a 2001 paper entitled “Do Firms Maximize? Evidence From Professional Football”. The question Romer was trying to answer is familiar to every NFL fan: what to do on 4th down? Is it better to bring on the kicking team for a punt or field-goal attempt? Under what conditions should coaches risk going for it?

To answer this immortal mystery, Romer analyzed every fourth down during the first quarter in every NFL game between 1998 and 2000. (He had help from a computer program.) The first thing Romer did was figure out the fluctuating value of a first down at each point on the football field. After all, a first down was more valuable for a team if it occurred on an opponents two yard line than on their own twenty yard line.

Then Romer calculated the statistical likelihood of going for it on fourth down under various circumstances and actually getting a first down. He also calculated the probability of kicking a successful field goal from various spots on the field. So let’s say you are NFL coach, and you have a fourth and three on your opponent’s 30 yard line. Romer could tell you that 1) you have a 60 percent chance of getting a first down, and that teams with 1st downs inside the thirty yard line score a touchdown 40 percent of the time, for an expected point value of 1.7 and 2) that field goal attempts from the 32 yard line failed almost 65 percent of the time, which meant that going for a field goal only had an expected point value of 1.05. In other words, it’s almost twice as effective to go for it than to attempt a field goal.

So what do most coaches do? Well, NFL coaches consistently make the wrong decision. According to Romer’s analysis, teams would have been better off going for it on fourth down during the 1st quarter on 1100 different drives. Instead, coaches decided to kick the ball 992 times. This meant that NFL coaches made the wrong decision over 90 percent of the time. Romer summarized his counterintuitive results: “This analysis implies that teams should be quite aggressive. A team facing fourth and goal is better off on average trying for a touchdown as long as it is within 5 yards of the endzone. At midfield, being within 5 yards of a first down makes going for it on average desirable. Even on its own 10 yard line – 90 yards from a score – a team within three yards of a first down is better off on average going for it.” Romer conservatively estimates that a more aggressive approach on fourth downs would make a team 5 percent more likely to win the game. This is a significant advantage: a coach willing to endure the risks would win one more game in three seasons out of every four.

But if kicking a field goal or punting on fourth down is such a bad idea, then why do coaches always do it? To explain the consistently bad decisions of NFL coaches, Romer offered two different answers. The first is risk aversion. If coaches followed Romer’s strategy, they would fail about half the time they were within ten yards of the endzone. This means that instead of kicking an easy field goal and settling for three points, they would come away empty handed. Although that’s a winning strategy in the long-run, it’s awfully hard to stomach. (As Daniel Kahneman notes, “Worst case scenarios overwhelm our probabilistic assessment, as the mere prospect of the worst case has so much more emotional oomph behind it.”) After a long drive down the field, fans expect some points. A coach that routinely disappointed the crowd would quickly get fired.

The second reason coaches stink at making decisions on fourth down is that they stink at statistics. As Romer politely writes, “Many skills are more important to running a successful football team than a command of mathematical and statistical tools…It may be that individuals involved want to make the decisions to maximize their teams’ chance of winning, but that they rely on experience and intuition rather than formal analysis.”

So how have coaches reacted to this data? In 2001, before Romer published his findings, the average team went for it on fourth down 15.1 times per season. During the 2005 season, the average NFL team went for it on fourth down 14.5 times. Learning about our mistaken decisions led to even worse decisions.

Football : The Frontal Cortex.

Kicking performance affects perception of goal size : Neurophilosophy

This is the latest in a series of studies showing that our perceptions are grounded firmly in our actions. Witt’s group has previously demonstrated that perceptions of goal size in golfers and softball players are apparently affected by performance. Other researchers have shown that perception is also influenced by the amount of effort required to perform an action. A location seems further away when one has to walk uphill to reach it, or if one is tired or in pain during the walk, and hills look steeper when one is carrying a heavy backpack. Similarly, objects that are just out of reach are perceived to be closer when one is holding atool that extends reach, while those that are positioned so that they are difficult to grasp are perceived as beign further away.

All of these studies show that perception does not merely involve reconstructing the geometry of one’s environment from visual information. Rather, our perceptions seem to be firmly grounded in, and strongly influenced by, the abilities, intentions and efforts of the perceiver. This may be because we view the environment in terms of energy costs, and plan our actions accordingly. Thus, a tired walker who perceives a hill to be steeper than it actually is will walk more slowly, and an athelete who perceives a target to be bigger will need to expend less energy and attention. Conserving energy is vital for survival, so such an adaptation would confer an important evolutionary advantage.

via Kicking performance affects perception of goal size : Neurophilosophy.

How Often and Does It Matter 2.0

Kings are 4-1. At 3-1, we determined that the Kings actually make the playoffs less frequently when they are at 3-1 or better after four games. But what about after being 4-1 after five games? Are the odds any better? (The list shows season/record/yes-no-playoffs?)

1970-71 4-1 no

1980-81 4-1 yes

1988-89 4-1 yes

1992-93 4-1 yes

Three out of four, or 75%. When they did not start this well, they only made the playoffs 54% of the time. So the odds are with us. (Incidentally — and I declare this a jinx-free comment — the Kings only reached 5-1 twice, in ‘80 and ‘92, and they’ve managed 6-1 never. The best start in franchise history is 10-1-1 in ‘80. As of today, we can call it the third best start in franchise history.)

When is Your Checking Line Not Your Checking Line?

I just noticed something fascinating in the Kings exotic stats numbers from the first four games. Actually, I noticed it after game three, but put off mentioning it because I thought maybe it was statistical white noise. I still think it might be. But I’m getting ahead of myself. Let me back up.

We all know which Kings line is the number one line. And we all know which line is the checking line. There has been much gnashing of teeth at the gall of Terry Murray to put our leading goal scorer on the third line, the checking line. (What, is he is insane? Etc.) I spent most of last year defending Frolov’s supposed defensive weaknesses by citing QUALCOMP stats which showed he drew the toughest defensive assignments of any Kings forward. And clearly when Murray reunited the Frolov/Handzus/Simmonds line for this season, we all knew it was more of the same.

Except it’s not. At least not so far.

I looked at the QUALCOMP numbers after the third game, and much to my surprise, who did I find at the top of the list, playing against the opponents’ highest rated forwards? Stoll/Brown/Purcell, that’s who.

And who was at the bottom of the list, getting the easiest opponents? Frolov/Handzus/Simmonds.

Could it be that Terry Murray has quietly switched his line-matching strategy, treating his publicly-labeled “checking line” like the number two offensive unit and his declared “#2″ line as the real checking line? The idea made me giggle at its shifty brilliance. But it also occurred to me that the numbers could be messed up in the following way: with such a small sample size, wasn’t it possible that the reason Purcell/Stoll/Brown were getting high QUALCOMP numbers was because they themselves (Pucell/Stoll/Brown) were underperforming, thus CAUSING their opponents to have higher ratings and artificially juicing their own QUALCOMP numbers due to their own suckiness? I decided to give it at least another game.

Looking at the numbers after last night’s game, Purcell/Stoll/Brown were still at the top of the forwards, with Frolov/Handzus/Simmonds creeping toward the middle of the pack and the Kopitar line edging toward the easiest opponents. This would of course corroborate the theory that the Stoll line has quietly become the checking line, while the other two lines are getting matched against the weaker opponents.

We’ll see how these numbers look when get farther along. It may still be statistical white noise. Meanwhile, I can enjoy the thought that this is really what Murray is doing; since, after all, I’ve always maintained that Brown is the ideal third-line winger despite the fact that we can never call him that; and we all know that Stoll is not really a top-six forward, while Frolov obviously is one, and Simmonds is playing more like one every day.

[POST GAME 5 UPDATE: Handzus and Frolov are still getting the weakest opponents, with Simmonds creeping up and the Brown line creeping down. The big change with game 5 is that the Kopitar line now has the highest QUALCOMP numbers, which makes sense to me since they are playing so well they're drawing the best the other team has to offer. Although the numbers after five games are not as persuasive as the numbers after three or four games, it's still remarkable that the Stoll line is drawing tougher assignments than the Handzus line. The comparison between Frolov of last year and Frolov of this year is especially striking, since he was the highest rated forward and now he's the lowest rated one -- in terms of the quality of opponent Murray is putting Fro out against. But like I said above, this is still very much "to be continued". The first five games could be an abberation and everything could soon level out.]

How often do the Kings get off to such a good start (and does it mean anything?)

season /record / playoffs?

1970-71     3-1          no

1974-75    3-1          yes

1980-81   3-1          yes

1982-83    2-0-2      no

1988-89   4-0        yes

1992-93    3-1         yes

1993-94    3-1           no

1995-96   2-0-2       no

1999-00   3-1        yes

2002-03   3-1          no

2003-04   3-1          no

2005-06   3-1          no

2009-10    3-1          ??

If I can count, that’s 12 starts at 3-1 or better, with 5 play-off appearances. That’s 42%. Not a great indicator. And it’s even worse when you consider this: the Kings have been in the playoffs 23 times out of 41 seasons (56% of the time). If you subtract the 12 seasons in which they started the season at 3-1 or better, you’re left with 29 seasons, in which they made the playoffs 18 times. 62% of the time.

That’s right: historically, the Kings are 20% more likely to make the playoffs when they do worse than 3-1 in their first four games. Grrrr.

How the Kings Spend Their Cap Dollars Compared to Those Losers in Detroit, San Jose, Boston, Washington and Pittsburgh

First, this is Mirtle’s post in full (my Kings-centric notes follow):

This is something I was working on before vacation, a mini-study of how five of the NHL’s best teams are allocating their cap space this year. The five I went with are all right up against the cap, and all experienced a ton of success last year: The Sharks, Red Wings, Bruins, Capitals and Penguins.

Part of the reason I wanted to do this was, in looking at how GM Doug Wilson had revamped San Jose’s roster by cutting depth and adding a top end stud in Dany Heatley, it was really clear how top heavy he had elected to go. And that’s becoming the norm under the cap these days:

In dealing with a 22-man roster, the top 11 players take home 82 per cent of these teams’ salary dollars, with the bottom 11 players (the last seven forwards, three defencemen and the backup goaltender) making the remaining 18 per cent.

In other words, these teams’ top 11 players average about $4.2-million apiece, whereas the players on the low end, filling out checking lines and minor roles, make an average of about $930,000.

Here are a few more breakdowns of these top five teams:

	Avg	%	Avg %
Forwards	$33,220,239	58.5%	4.5%
Defencemen	$18,386,166	32.4%	4.6%
Goaltenders	$4,901,000	8.6%	4.3%
Buyouts	$515,000	0.9%
	Avg	%	Avg %
Starting 6	$33,249,434	58.5%	9.8%
Top F line	$18,380,268	32.4%	10.8%
Top six F	$26,402,768	46.5%	7.7%
Top D pair	$10,610,833	18.7%	9.3%
Top four D	$15,692,500	27.6%	6.9%
Starting G	$4,258,333	7.5%	7.5%
Bottom 11	$10,153,804	17.9%	1.6%

Some of the noteworthy numbers there? These teams spend nearly one-third of their total salary on their top forward line, with another 19 per cent on their top two defenders. Add in the No. 1 goalie and you have what I’m calling the “Starting 6,” a group that takes home nearly 60 per cent of these five teams’ payroll.

The top six players on the NHL’s top five teams average more than $5.5-million each, which leaves only $23.5-million for the other 14 to 17 players on the roster (depending on the number of healthy scratches involved).

I haven’t crunched all the numbers on how this has evolved over time since last season, but I’d be willing to wager that those low end numbers have continued to fall as the “Starting 6″ and top 11 players pull in more and more of the pie. Cap or no cap, the skilled players are getting their contracts; it’s everyone else who’s fighting for what’s left.

And that’s probably the way it should be.

via How the top teams spend their cap space – From The Rink.

Okay. I ran the Kings numbers for comparison.

	Kings	Mirtle’s 5	$$ +/-
Starting 6	42%	58.5%	-11.4MM
Top F line	31.8%	32.4%	-1.9MM
Top six F	46%	46.5%	-2.5MM
Top D pair	9%	18.7%	-5.9MM
Top four D	18.5%	27.6%	-6.1MM
Starting G	1%	7.5%	-3.7MM
Bottom 11	29%	17.9%	+4.9MM

What I make of it:

Starting 6 – Kings are spending $11.4MM less on their “starting 6.” Why? Because O’Donnell is cheap and Quick is cheaper. In two or three years, Doughty will get a raise, O’Donnell will be replaced by someone making $3MM and Quick will get his adult contract, which ought to add about $6MM to this number. Which still leaves the Kings running about $5MM under the top5 teams.

Top forward line – Kings are on a par with Mirtle’s 5.

Top six forwards – ditto

Top D pair – As discussed above, this is due to Doughty’s entry level contract (albeit with bonuses) and SOD being a cheap, old dude. Presumably, Mirtle’s 5 have premium D-men in their prime.

Top 4 D – Difference is entirely due to the Doughty/SOD issue above. One can conclude from this that the Kings’ second pair D are on a par with Mirtle’s 5.

Starting G – Obviously, the Kings goalies are all on entry level contracts or close to it.

Bottom 11 – There are a couple of reasons the Kings appear to be over-spending on their bottom 11. The first reason is: they are. Handzus gets $4MM/year. That’s obviously high for a third line center. The second reason: Murray has the line playing more like a second line; the line as a whole is actually $300K more pricey than the second unit. Either way, the Kings’ second and third lines are quite balanced, which appears at first blush to be somewhat unusual (at least compared to the Mirtle 5). Certainly that fits with my general sense of the Kings forwards, which is that they are pretty balanced over three lines but, as is often noted by everyone, lack that one true superstar talent. Last season, the Kings often seemed to have a second line and two third lines, but no first. This year, so far, it’s better: a first, a great third, and a mediocre second.

Moller vs. Stoll

There’s been quite a bit of talk about Oscar Moller being the odd man out this fall. It goes something like this: Purcell is having a great camp and it appears he will be given first crack at LW on the second line, with Stoll and Brown. Moller, meanwhile, is small and slight and easy to bump off the puck. He would do well to spend a season in Manchester. I put all of that in italics because it’s at least partially wrong and also because it irrationally pisses me off. Purcell is in fact having a good camp, maybe a great one; certainly, he has improved his attitude, and bulked up a bit, since last year at this time, when he got beat out by Moller for a roster spot on the team that was Purcell’s to lose. I am not anti-Purcell. In fact, I love Purcell in nine out of the ten ways it’s possible for a fan to love a hockey player. But this is not a battle between Purcell and Moller, not even if Purcell wins that spot and Moller is sent down to play on Loktionov’s line in Manchester. [UPDATE: since I started this post, both Moller and Loktionov have in fact been assigned to Manchester.] Purcell vs. Moller doesn’t matter.

The real battle is Moller vs. Stoll.

Let’s start with this: a list of Kings’ players with NHL experience who are capable of playing the role of top-six center.

Kopitar

Stoll

Moller

That’s it. Three guys. And everyone basically admits that Stoll is really a third line center, like Handzus. If you include people in the pipeline, the list includes Schenn, Azevedo and Loktionov, none of whom are ready now. Now, it’s possible that Moller is truly not ready now for that job and really should spend the season (or part of it) in Manchester getting bigger and better against men. And it’s also possible that Stoll is not crippled by arthritis and will click with Purcell and Brown and everything will be fine until Moller is “ready.”  However, let’s look at some numbers:

Jarret Stoll is a guy who has scored 20 goals in his career exactly once, four years ago.

Since then, he has scored 13, 14 and 18 goals. Moller, in his first season, was on a pace to reach 14 goals, when he left mid-season to captain the Swedish WJC team. These are basically the numbers Stoll has settled into in his prime.

Now, consider that Stoll scored his 18 goals last year while playing with the best players on the team (his QUALTEAM number is 0.056, 2nd highest among Kings forwards), while Moller scored his 7 goals while playing with just about the worst quality teammates possible (his QUALTEAM number is -0.098, which is with Purcell at the bottom of the barrel ahead only of the Zeilervananstrong party). Stoll mostly played top line minutes. Moller mostly did not.

Okay, that’s offense. I don’t think anyone would really argue that Stoll’s upside is higher than Moller’s. The main knock against Moller would be his size. You know, that bit about being knocked off the puck and being weak. So, if Stoll is better than Moller in that regard, I would expect Stoll to — for example — draw more penalties than Moller (being stronger on the puck, etc.). But does he?

Not even close.

Stoll draws 0.7 penalties per 60 minutes of icetime, which is just about the worst on the team among forwards. Moller, meanwhile, draws 1.3 per, nearly double Stoll’s number, and second on the team only to Brown, who is the best in the league. So let’s underline that: when Moller is on the ice, people have to take penalties to stop him; Stoll, no.

What about penalties taken? Because if Moller is so small and weak, he would have to take a lot of penalties, right? And Stoll should easily be able to best Moller in this category.

Guess what?

Moller, penalties taken per 60 minutes of icetime: 0.6. This is just a hair from the team best (Kopitar and Handzus are at 0.5). And Stoll? 2.0 penalties taken per 60 minutes of icetime, the worst on the entire team, and DOUBLE the number of the next closest forward (Calder).

How about Corsi numbers? [Corsi is like plus/minus, except shot "events" are counted instead of goals; missed shots, blocked shots, saves and goals all count the same; as a result, goaltending is factored OUT, and you get a sense of generally how much offense as opposed to defense is generated when a given player on the ice.] Moller’s CORSI is 7.3, 2nd among Kings forwards only to Kopitar, who is at 9.5. Stoll is at 2.8, which is behind Kopitar, Moller, Brown, Purcell and Williams among players who could be considered top-six. Actually, Stoll’s CORSI is below all the top-sixers except Frolov, who is the one top-six forward who has huge defensive duties. Stoll is also lagging behind Calder, which I mention just for fun.

5-on-5 plus/minus… Moller -1 (tops among top-six forwards, second to Calder [!] among all forwards), Stoll -6.

Goals For While on Ice, Stoll has an edge, 2.21 to 1.90 per 60. However, the figures are reversed for Goals Against: Stoll, 2.65; Moller 2.05. We know that Moller scores more on the power-play. So this stat tells us that Stoll scores a bit more than Moller at even strength, but is not as good defensively, so it cancels out his even-strength offensive edge and puts him behind Moller on the whole (again, that doesn’t “fit” with the accepted wisdom that Moller is small and weak and Stoll is not; especially since Moller plays with much worse teammates than Stoll…hmmm….).

So now I get to spend money on Monarchs webcasts. I wonder who will be the third guy on the Moller/Loktionov line. Lewis? Can’t wait.

mc79hockey.com – Comrie might as well be an Oiler – easier to just boo them and him at the same time » The Red Wings model entails more than just lots of guys

mc79hockey.com – Comrie might as well be an Oiler – easier to just boo them and him at the same time » The Red Wings model entails more than just lots of guys .

Five Hole Fanatics: Offensive Defensemen are grossly overvalued

Five Hole Fanatics: Offensive Defensemen are grossly overvalued.

From the Chicago Daily Herald (possibly fictitious newspaper?): Bowman reveals Yoda-like wisdom on crashing the net

It was Bowman, while coaching in Detroit, who made Red Wings left wing Tomas Holmstrom the best in the game at screening goalies and causing havoc in front of the net.

“That’s why Holmstrom is so effective – he stands outside the line,” Bowman said. “When I was in Detroit we had a lot of goals called back and I would say to him, ‘You can’t make it close (for the referees). Don’t make it close.’

“I’d say, ‘If that’s the line, Tomas, you do a better job when you’re out a foot. You block his view more and the goalie can’t look around you.’ We were tough on him because when he first started he would tumble into the goalie.”

via Daily Herald | Bowman sees more net crashing than years ago.

I went looking for this in the archives only to discover that I saved it but never actually posted it. Grrr.

An Amicus Brief In Support of Dustin Penner – The Copper & Blue

From Mirtle: The Flyers goaltending: Not as bad as you think (and other netminding myths) [ME: Ah, but the Kings' goaltending is worse than you think!]

There’s something about goaltending that, when it’s bad, it can hang on a franchise like an odour, weighing on fans and netminders alike. Pre-Luongo, for example, Vancouver was always pegged as a goalie graveyard (although some would argue a few of his predecessors were stiff upon arrival). In Ottawa, they seemingly haven’t had a lot of luck at the position either, with Patrick Lalime’s ugly turn in the 2004 postseason serving as just one reminder of failures past.

Anyway, it all made me wonder which franchises have really been home to the worst goaltenders in recent years and where exactly teams like Flyers, Sens and Canucks fit into the picture. So I made a chart.

Over the past 10 seasons, the average team save percentage has been about .904, with the top team (Minnesota) coming in well ahead of the pack at .916, followed by Florida and Anaheim at .911. Bringing up the rear are the Lightning with a god-awful .894.

If we limit this analysis to just the postlockout period, things change up slightly, however. The Wild, Devils, Panthers, Predators and Ducks are the top teams the past four seasons, with the Lightning, Leafs, Kings, Blues and Avalanche all at the bottom:

TEAM 2005-06 2006-07 2007-08 2008-09 AVG

1 Minnesota 0.914 0.922 0.915 0.922 0.918

2 New Jersey 0.906 0.917 0.914 0.914 0.913

3 Florida 0.912 0.896 0.920 0.922 0.913

4 Nashville 0.916 0.919 0.908 0.905 0.912

5 Anaheim 0.909 0.912 0.920 0.906 0.912

6 NY Rangers 0.911 0.909 0.911 0.913 0.911

7 Vancouver 0.898 0.918 0.913 0.911 0.910

8 Boston 0.902 0.896 0.914 0.925 0.909

9 Montreal 0.903 0.907 0.917 0.908 0.909

10 Calgary 0.915 0.912 0.904 0.899 0.908

11 Ottawa 0.913 0.913 0.901 0.901 0.907

12 Buffalo 0.906 0.906 0.900 0.911 0.906

13 San Jose 0.892 0.908 0.906 0.911 0.904

14 Pittsburgh 0.886 0.905 0.916 0.906 0.903

15 Detroit 0.906 0.905 0.907 0.894 0.903

16 Philadelphia 0.893 0.889 0.913 0.913 0.902

17 NY Islanders 0.892 0.912 0.904 0.900 0.902

18 Columbus 0.900 0.896 0.907 0.902 0.901

19 Dallas 0.897 0.907 0.905 0.891 0.900

20 Atlanta 0.891 0.907 0.904 0.896 0.900

21 Edmonton 0.884 0.900 0.904 0.909 0.899

22 Washington 0.896 0.899 0.900 0.901 0.899

23 Carolina 0.897 0.894 0.896 0.909 0.899

24 Chicago 0.885 0.896 0.902 0.911 0.899

25 Phoenix 0.892 0.886 0.911 0.904 0.898

26 Colorado 0.896 0.896 0.903 0.894 0.897

27 St. Louis 0.887 0.895 0.897 0.903 0.896

28 Los Angeles 0.891 0.886 0.900 0.902 0.895

29 Toronto 0.895 0.888 0.893 0.885 0.890

30 Tampa Bay 0.887 0.884 0.885 0.900 0.889

Average 0.899 0.903 0.906 0.906 0.903

via The Flyers goaltending: Not as bad as you think (and other netminding myths) – From The Rink.

Goal Differential: How the Non-Playoff Teams Can Become Playoff Contenders — NHL FanHouse

NHL Fanhouse corroborates what I said earlier about the Kings’ playoff chances being tied to goal differential (post here). I said the Kings needed to add 38 goals while cutting 4 goals against. They say (see below) the Kings need a 47 goal improvement; I said 42 would do it. I based my numbers on eyeballing the numbers. He actually crunched the numbers. So his 20 goal differential is probably more accurate than my 15.

If we go back to the 1999-2000 season, there have been 144 playoff teams in the NHL, and of those teams, 134 finished the regular season with a positive goal differential. Ninety-one finished with a differential greater than plus-20, and only two playoff teams finished with a differential worse than minus-10 only 10 teams had any kind of negative differential.What does that mean for last years non-playoff teams? Lets take a look.Since the 99-00 season, only two teams have finished the regular season with a differential greater than plus-20 and missed the playoffs the 2001-02 Oilers and the 2006-07 Avalanche. That said, lets go ahead and say that if you finish the season plus-20 you are, at the very least, in serious contention for a playoff spot. After all, since the start of the decade 91 of the 93 teams that finished with such a mark made the playoffs.

…

Los Angeles Kings
2008-09 goal-differential: -27
Needed Improvement: 47 goal improvement — (Example: score 24 more goals and allow 23 fewer goals). Excellent young team. Talented forwards, young defense, they spent the offseason adding experience and grit (Ryan Smyth, Rob Scuderi) and if they can get even competent goaltending, the playoff drought could end in Los Angeles.

via http://nhl.fanhouse.com/2009/08/19/goal-differential-how-the-non-playoff-teams-can-become-playoff/

[UPDATE: I also did a little estimate of how the Kings could score 250 goals here]

From The Puck Stops Here: Worst 20 Corsi Rates

“Puck Stops Here” puts another nail in Raitis’s coffin:

Today, I am listing the worst 20 adjusted Corsi rates.  The adjustment method involves calculating a player’s Corsi rate while he is on and off the ice and subtracting them to get the adjusted rate attributed to a given player.  This is similar to the adjustment that behind the netdoes with on/off ice adjusted +/- ratings.

Here are the 20 worst adjusted Corsi rates from 2008/09 among players with 50 or more games played:

[oops, sorry. Kings-Kool-Aid Editorial interruption. That's obviously not PuckStopsHere's chart. It's Purcell and Simmonds on the recent Kings Fan cruise. I wanted to do a post on the cruise but never got around to it, though I grabbed the picture from the Kings site. Okay, here's PSH's chart of the worst Corsi numbers:]

This list appears over-represented by shut down forwards who struggled last season (such as Kris Draper and John Madden).  This is in contrast to the counting stat adjustment which is over-populated by shut down defensemen.  Neither push puck possession on their teams, but since defencemen tend to get more ice time they tend to lead in a counting stat format, while the forwards have worse overall rates.  Nine players appear on both worst Corsi lists after adjustment.  They are Kris Draper, Rob Niedermayer, John Madden, Jay Pandolfo, Boyd Gordon, Colton Orr, Lauri Korpikoski, Kurt Sauer, Mike Commodore and Tim Jackman.  Three players on this adjustment list are not eligible for the counting stat adjustment since it requires players to play on only one team during the season and they were traded (Travis Moen, Sami Pahlsson and Niclas Havelid). The other players who appear on this list are players who had limited roles on their respective teams (to not get enough ice time to appear on the counting stat list) and failed. Ryan Johnson of the Vancouver Canucks leads this group.  He probably played himself out of the NHL last season.

This list is by no means a list of the worst twenty players in the NHL.  Many of the shutdown forwards, though they did not have good seasons, are made to look worse because of high calibre opposition. Any player appearing on this list who did not play against top opposition last season did not belong in the NHL.  Many of the players in that group will soon find themselves without NHL jobs.

I don’t know why I’m feeling so down on Ivanans lately. I was a luke-warm supporter of his most of last season. Maybe it was that league-topping obstruction penalty stat (among the worst, not THE worst — he might have been THE worst, but I’m too lazy to work it out — whatever the number was, it was ugly). Or maybe it’s just the fact that the roster spot he’s squatting on I would rather be filled by Purcell, Moller, Lewis, etc. etc. and I don’t want to see someone I like get sent down because we need 6 minutes of knuckle-dragging every game. And yes I know he works hard in practice. Maybe he also has a pet mouse he keeps in his pocket, which, one day, in a foreshadowing of what is to come, he will accidentally crush to death in his palm.

From The Puck Stops Here: Team Corsi Numbers

Puck Stops Here (blog) calculates team Corsi numbers, and the results are interesting. Here’s what he says; my thoughts follow the quote block:

Corsi Numbers are the difference between shots directed at the goal (shots on goal, missed shots and blocked shots) for and against when a player is on the ice in five on five situations.  The benefit is that they encompass a lot more events than +/- does.  However, whether or not it is a better or comparable series of events is somewhat of an open question.

I have calculated the Corsi Numbers for all 30 teams in the NHL.  These can be compared to team +/- ratings.

Team	Corsi Rank	Corsi	+/- Rank	+/-
Detroit Red Wings	1	+918	5	+30
Calgary Flames	2	+717	14	+2
Washington Capitals	3	+664	8	+18
Chicago Blackhawks	4	+653	2	+45
San Jose Sharks	5	+406	10	+16
New York Rangers	6	+405	23	-20
New Jersey Devils	7	+301	3	+38
Carolina Hurricanes	8	+280	13	+4
Columbus Blue Jackets	9	+199	8	+18
Anaheim Ducks	10	+145	12	+8
Los Angeles Kings	11	+105	27	-31
Toronto Maple Leafs	12	+80	25	-26
Boston Bruins	13	+49	1	+60
Dallas Stars	14	+9	19	-11
Ottawa Senators	15	-75	23	-20
Vancouver Canucks	16	-113	4	+32
Nashville Predators	17	-129	20	-12
Buffalo Sabres	18	-131	16	-1
St Louis Blues	19	-251	22	-14
Tampa Bay Lightning	20	-262	28	-34
Pittsburgh Penguins	21	-285	7	+23
Montreal Canadiens	22	-286	17	-5
Edmonton Oilers	23	-323	15	-1
Philadelphia Flyers	24	-347	6	+24
Minnesota Wild	25	-350	21	-13
Colorado Avalanche	26	-354	29	-49
Atlanta Thrashers	27	-415	18	-10
Florida Panthers	28	-499	11	+11
New York Islanders	29	-513	30	-57
Phoenix Coyotes	30	-598	25	-26

It is clear that Corsi Numbers do increase the separation between teams.  There is an over 1500 point spread between the highest Corsi (Detroit) and the lowest (Phoenix).  With +/- this spread is slightly over 100 points.  That aim of Corsi Numbers is clearly satisfied.  However, it isn’t clear how similar what Corsi and +/- measure is.  The order of teams in the two rankings is changed significantly.  In general, good teams have top rankings and poorer teams have weaker rankings with both systems, but there are some rankings that seem a bit odd (or interesting).  The Stanley Cup champion Pittsburgh Penguins were the 21st team in Corsi in the regular season.  That is awfully low isn’t it?  The +/- leading Boston Bruins fall to 13th in Corsi.  The main difference here is that Corsi looks at shots and +/- at goals.  Since Boston had the best goaltending in the league, there is a significant difference between the two.  The worst Corsi ranking for a playoff team was Philadelphia who finished 24th.  Does this show something was wrong with the Flyers or with Corsi rankings? The best ranking for a non-playoff team was Los Angeles, who finished 11th.

via KuklasKorner : The Puck Stops Here : Team Corsi Numbers .

Here’s my interpretation. The reason the Pens have a relatively low Corsi number is that they have a lot of goal-scoring talent (which means they will bury a higher percentage of their chances) and they play a more wide-open style, which means the opponents will get lots of opportunities. The Pens don’t need as many shots/missed shots/blocked shots to get goals, so their Corsi number drops.

Now, the Kings. Why is their Corsi so high? Well, it’s been noted elsewhere that the Kings offensive problems may be due to an incredibly low shooting%. In other words, they don’t bury their chances, but they do generate a lot of chances. This would increase their Corsi. Also, they had some goaltending problems early on. This would mean that more goals against are happening relative to the number of shots/missed shots/blocked shots. That would also raise the team Corsi. Add to that the fact that the Kings play a defensive system that allows few shots (the fewest in the league for most of the season), and it’s easy to see why the Kings’ Corsi was so high.

Why is this good news, and not just b***sh**? Because in one sense Corsi measures potential. It tells us that the Kings are doing two things right: limiting chances against and maintaining a high level of chances for. In order to translate high Corsi into high team +/-, the Kings need to address two things: finish (burying the chances they get, as opposed to needing to create more offense) and goaltending (stopping a higher percentage of shots that actually get through).

Since the Kings did a good job of addressing their goaltending in the second half of the season, and made moves to increase the likelihood of being able to finish chances in the offensive zone (by acquiring players who aren’t “afraid of the blue paint” — Williams and Smyth), these numbers are reason for some small amount of optimism.

How much does your favorite team travel? Check the NHL Super Schedule – On the Forecheck

How much does your favorite team travel? Check the NHL Super Schedule – On the Forecheck.

Behindthenet Blog: Goaltender Rebound Percentage Leaders

In his work on Shot Quality, Alan Ryder noted that shooting percentage is extremely high on rebounds. That is, in the two seconds following another [i.e. initial] shot [the average sv% is .694]. The overall save percentages for 2008-09:

Overall   1-2 s   3 s   4 s

919     694 807      892

After four seconds, shooting percentages are in the low-to-mid 900s. So it is significant to look at what percentage of a goalie’s shots are rebounds allowed less than 2, 3 and 4 seconds after a previous shot. Here are the league “leaders”:

Goalie PCT2s PCT3s PCT4s

EVGENI NABOKOV 5.52 6 7.61

MIIKKA KIPRUSOFF 5.33 6.24 8.25

CAM WARD 5.31 6.38 7.09

CHRIS OSGOOD 5.25 5.59 6.48

MATHIEU GARON 5.19 5.73 6.55

MICHAEL LEIGHTON 5.16 6.25 7.06

MARTIN BRODEUR 5.08 5.72 6.67

TOMAS VOKOUN 4.98 6.63 8.2

MANNY FERNANDEZ 4.68 5.81 6.78

PETER BUDAJ 4.59 5.37 6.06

And at the other end:

Goalie PCT2s PCT3s PCT4s

CHRIS MASON 3.25 4.42 5.96

STEVE MASON 3.24 4.43 6.22

KARRI RAMO 3.21 5.22 7.22

ERIK ERSBERG 3.18 4.67 5.09

JONATHAN QUICK 3.08 3.77 5.71

JASON LABARBERA 3 4.6 5.61

RYAN MILLER 2.84 3.84 5.76

BRENT JOHNSON 2.69 3.92 4.9

MARTIN GERBER 2.6 3.9 4.72

JOSH HARDING 1.79 3.58 5.67

via Behindthenet Blog: Goaltender Rebound Percentage Leaders.

I’ll have to think about what this really means. But the first thing that occurs to me is: Jonathan Bernier. Gives up 20-25% fewer rebounds than Quick. Regardless of your “rebound” save percentage, that’s one way to solve the problem. Ersberg, we know, is pretty good at rebound control, too.

One stat I would like to see — maybe the guy who did the original numbers already did this, I will have to look — is a SV% that treats a shot and its rebounds as one event. Then you would see the effect of a goalie’s rebound control. For example, a goalie could suck on rebounds, but if he doesn’t give up any, the number is meaningless. Conversely, a goalie could be great on the original shot, but if the rebound always goes in, that — um — sort of cancels it out.

From Behindthenet.ca: Junior Hockey Projections

Twenty years ago, almost all every NHL player was drafted from the Canadian Hockey League. Even today, the majority of NHL draft picks still come from the three Tier I junior hockey leagues, the Western Hockey League (WHL), the Ontario Hockey League (OHL) and the Quebec Major Junior Hockey League (QMJHL). But how does player performance translate from these leagues to the NHL?

It is obvious that, despite drafting thousands of times, NHL scouting hasn’t adequately answered this question. Looking at the 14th-23rd post-expansion drafts, from 1979 to 1989, 15 of 33 picks had careers that lasted more than 1000 games. But NHL teams also used the second and third picks on players like Dave Chyzowski and Neil Brady, not to mention famous “busts” Doug Wickenheiser, Doug Smith and Perry Turnbull. Even after 20+ years of evaluating CHL players, some teams looked at Vincent Damphousse and Adam Foote and decided they’d be better off with Brady and Chyzowski.

So were these errors easily avoidable? That’s the question we’ll answer.

Junior League Parity

Some preliminaries: in the aggregate, there’s no significant difference between the three junior hockey leagues. [...] While individual performances vary, the average player who moved from the OHL to the minors or the NHL played as well as the average player who started out in the WHL or QMJHL.

The Significance of Age

Does it matter how old a player is when he puts up big numbers in Junior? Obviously it does – Wayne Gretzky had 70 goals and 112 assists in 64 games for the Sault Ste Marie Greyhounds as a 17-year-old in 1977-78. Seven years later, Dan Hodgson had the exact same statistics when he was a 20-year-old playing for the Prince Albert Raiders. Hodgson was drafted 83rd overall despite his prolific scoring, and had 74 points in a 114-game NHL career. (He is still active in the Swiss National League.) At age 22, Hodgson had 57 NHL points; Gretzky had already scored 1024 points between the NHL and WHA.

So in a qualitative sense, it’s obvious in this case that a 17-year-old player’s performance predicts a much better career than a 20-year-old’s stats. But there is also a strong quantitative relationship between past and future performance. Based on the performance of thousands of drafted players, we can predict how many points a player will score in the NHL when he’s 21-years-old. If he’s 17, four years later, we expect him to score at 72% of his junior rate. But if he’s 20, on average, he’ll retain just 26% of his scoring.

There is a caveat: younger players are a bit less predictable than older players. For a 17-year-old, the middle 50% range of the projection is from 45% to 98%, while for a 20-year-old, it’s from 17% to 33%. This wide range reflects how unpredictable future performance is for NHL players. From age 21 to age 25, Wayne Gretzky scored between 196 and 215 points each season, which is only a 10% variation, while this method predicts a possible 2:1 variation in scoring. The performance of an individual player is much more consistent than it is for the large group of drafted NHL players.

We could narrow the bounds of the projection if we had more data about the players. This method tries to capture a player’s performance despite having no information about linemates, ice time, injury status, size and performance in other seasons. Who you play with can have a profound effect on your performance: Rob Brown played with Mario Lemieux and had 49 goals and 115 points. The Penguins traded him away two years later, and without Lemieux setting him up, he couldn’t crack an NHL roster.

PPG Projections by Age

[...]

In the aggregate, players reach their peak performance level at age 22 and hold it for several years. [...] A 17-year-old player with 2 PPG in Junior can expect, on average, to score 1.5 PPG in the NHL at age 22, while an 18-year-old Junior doing the same thing has an NHL projection of 1.0 PPG, which is 40 fewer points over the course of a season. This is the difference between elite players (Joe Sakic, Denis Savard, Dale Hawerchuk) and much lesser players (Jimmy Carson, Terry Yake, Mike Bullard.)

This is very significant for the NHL Entry Draft. An entire year’s worth of players become eligible for the draft, but the players born earlier in the year have a peak value 35% lower than players born late in the year. This is obvious when you consider the difference that one year of physical maturity can make at age 17. In evaluating a player, it is critical to keep in mind his exact age, down to his month of birth.

The whole article, with charts, at Junior Hockey Projections.

Pronger Calculates Pi to Three Digits

WARNING: Evil Ducks content.

From Mirtle: How hard is it to win the Stanley Cup?

It took me a couple days to cobble this together, with some major assistance for the raw data from stats maestro Gabe Desjardins, but I’ve finally got an answer: Of the 6,400-some players to have played in the NHL in the league’s 90-plus year history, 14 per cent won at least one Stanley Cup championship in their career.

It adds up to 917 players, with Mr. Hossa and a few others potentially joining the club later tonight.

Those are the big picture numbers, and they tell us only a little of the story. Part of the problem is that they include about 200 players who were born before World War I, in an era when close to 40 per cent of the players in the league would, at some point, win a Cup.

read the rest, at Mirtle’s blog: How hard is it to win the Stanley Cup? – From The Rink.

Ken Campbell: Best the Wings will ever be?

The link failed to import for some reason, but this is from Ken Campbell’s blog at The Hockey News: 

Ask Detroit Red Wings GM Ken Holland about it and you get a terse, “I’m not going there.” Ask Red Wings veteran Kris Draper and you get the death stare.

(Trust me, I got it when I asked him about it.)

But no matter what transpires over the next two games, the Red Wings – once again – will be faced with some difficult decisions over the summer and they know there are players in their dressing room who won’t be with them next season.

And we’re talking about core players here. If the Red Wings are intent on signing Marian Hossa this summer – and every indication is they are – it’s going to mean some very loyal and productive players are going to be leaving Hockeytown this summer.

The Red Wings already have $53.5 million dedicated to their salary cap next season and that’s before Hossa takes a hometown discount. So you don’t have to be Warren Buffett to come to the conclusion that there are going to be some key players gone next season.

“We know we have some difficult decisions ahead,” said Holland. “This is probably the best team that we’re going to be able to put together in the cap world. We’re going to lose some players.”

This time last year, the Red Wings were in the middle of the NHL pack when it came to the salary cap, which was about $5 million under the high-water mark. That allowed them to trade for Brad Stuart at the deadline and sign him to a long-term deal over the summer, then sign Hossa to a one-year deal as a free agent.

“We got lucky (last summer),” Holland said. “We signed some guys long-term and they took some big jumps in their careers and they were at the tail end of their contracts, it allowed us to add a star in Marian Hossa. Those days are over.”

Hossa will undoubtedly take a discount and will sign on the Red Wings terms if he wants to stay, but even then they still have to re-sign Ville Leino and decide what to do with Jiri Hudler, who is due to become a restricted free agent. Henrik Zetterberg’s cap hit jumps from $2.65 million to $6.08 million in 2009-10 and Johan Franzen’s cap hit goes from $942,000 to $3.95 million.

Mikael Samuelsson will become an unrestricted free agent July 1 and, as surprisingly good as he has been in his four seasons as a Red Wing, it’s difficult to fathom there will be room for him on next year’s roster. Hudler is another prime candidate to go and the Red Wings might be at least able to get something for him if they take him to arbitration and the team acquiring him knows what his salary will be for next season.

What about Tomas Holmstrom, who appears to have been rendered redundant by Franzen? Have veterans such as Draper and Kirk Maltby played their final games for the Red Wings? Will Chris Chelios come back knowing there’s a good chance he won’t play any more than the 28 games he played this past season?

Although Red Wings coach Mike Babcock has not dwelled upon it, he realizes there will be changes next season. He has watched as players such as Brendan Shanahan, Mathieu Schneider and Robert Lang left the organization without the Red Wings being too adversely affected.

“We don’t want to lose players,” Babcock said. “So I don’t know how Kenny (Holland) is going to work his magic there, but he usually comes up with some theory to keep as many players as he can. And we try not to lose players that we think are the cornerstones to our organization.”

One thing that takes some of the sense of urgency away is teams can be 10 percent over the cap during the off-season. But if it doesn’t go down this summer, any ceiling increase will be negligible and that doesn’t help, either.

“If the cap was going up again $7 million, maybe it would be different,” Holland said. “But we’re faced with the same kinds of decisions 29 other teams are and we’ll make them.”

Why hockey pucks are frozen – Kansas City Star

“Rubber is one of the most elastic materials on earth, and even vulcanization can’t stop hockey pucks from bouncing,” Hache wrote. “Smashed against a hard surface like concrete or ice, a puck rebounds with between 45 and 55 percent of its original velocity (less so on a softer surface like a board). This percentage is the so-called coefficient of restitution. In an ideal world, the puck wouldn’t bounce off the ice at all. To minimize this unruly behavior, someone discovered a long time ago that freezing the puck before a game would make it slide better and bounce less, owing to its increased stiffness.”

Hache even proposed a test. Take two pucks of the same weight and size and put one in a freezer for an hour. Then drop both pucks on a concrete surface.

“You will find that the cold puck bounces less than half the height of the warm puck,” Hache wrote. “In fact, they will bounce to about 12 percent and 27 percent of their original height, respectively.”

Basically if you warm a puck, the added heat means it has more energy. That’s why it will bounce higher.

A hockey puck is made of vulcanized rubber which means sulfur has been added to it and then heated.

This generates more bonds between the sulfur atoms (disulfide bonds) which makes the chains tighten more quickly. This increases elastic force constant and makes the rubber less extendable.

When a puck is cooled, chain segments freeze and the rubber loses its elastic properties. Thus is will not be as bouncy.

via Why hockey pucks are frozen – Kansas City Star.

Scientists Reveal the Secret to Hockey’s Wrist Shot | LiveScience

It takes less than a second, but the wrist shot in hockey is one of the hardest skills in sports to master. Just ask the Pittsburgh Penguins and the Detroit Red Wings who will face each other starting this weekend in the National Hockey League’s Stanley Cup Finals. Both teams know the value of the “quick wrister” and the scoring chances it creates. Now, a team of Canadian (of course) researchers believe they have isolated the key components of a successful wrist shot using 3-D motion capture analysis.

Hockey players have a few different types of shots that they use when trying to score. The slap shot, with its big wind-up and speed is a fan favorite, but the quick, on-the-fly wrist shot can be deadly accurate and accounts for 23-37 percent of shots taken at the professional level. To send the puck into one of the four corners of the goal (and avoid the goalie), a player must be able to control not only its horizontal direction but also its height when aiming at the top corners. This precision has to happen while the puck is sliding on the ice independently of the player and his stick.

Professors David Pearsall and Rene Turcotte, along with graduate student Yannick Michaud-Paquette of McGill University wanted to find out how the mechanics of the hockey stick and blade affected the flight of the puck and be able to point to specific movement patterns which resulted in more accurate shots. As Turcotte told LiveScience, “Very little information exists describing the kinetics and kinematics of skating and shooting skills. We are for the first time learning about how skills are executed in an optimized fashion.”

The McGill team sees potential for continuing their research to the point of giving practical coaching tips in the future. “Research in this area is relatively new and so many of the findings in our laboratory and in two or three others are producing new knowledge in this area,” said Turcotte. “Our increased understanding will have implications for teaching and coaching and can help practitioners to teach players to optimize skill development.”

Also important was something called the blade’s “roll angle” at puck release. Video of the expert players showed that when getting the puck ready to shoot they drag or draw in the puck and the stick’s blade closer their feet. This would let them use their wrists more to give the stick that well known “flick.” The beginner players would more often push the puck forward without any pre-shot adjustment closer to their body.

Twenty five hockey players, ranging in skill from novice to varsity level players, were told to shoot pucks at targets located in the four corners of a goal until they had hit each target ten times. They were allowed 20 shots per target with their accuracy percentage recorded. Their stick and the pucks were marked with reflective stickers that could be seen by six 3-D motion capture cameras placed around the goal. Everything from the angle, pitch and yaw of the stick blade to the stick velocity and contact time between puck and stick were measured as possible variables for accuracy.

As expected, the successful shot percentages were evenly divided between the novices (as low as 27 percent) and the expert players (as high as 80 percent). When shooting at the bottom targets, the most significant variable that affected accuracy was the position of the puck on the blade when released. The novices tended to position the puck closer to the blade’s heel, while the better players put it closer to the center of the curve in the blade, closer to the toe.

Hitting the top two corners of the goal was by far the most difficult task, with 20 percent less accuracy compared to the bottom corners. This makes sense as the third dimension of height is now added. Since the flight of the puck now is affected by gravity, the initial trajectory of the puck becomes important. Just like a baseball pitch, the faster the object travels, the flatter and more accurate the trajectory. Imagine the flight path of a fastball versus a curve ball. The team found that faster initial puck velocity when released from the stick significantly improved accuracy.

via Scientists Reveal the Secret to Hockey’s Wrist Shot | LiveScience.

MaxHockey.com: The Last Stand of the Wooden Stick

During a game against Colorado, Holik entered the faceoff circle against Paul Stastny, son of Hall of Famer Peter Stastny. The linesman noticed that both centers carried wooden sticks and commented.

Holik joked to the young Stastny that he would bet that Peter would never allow him to use a composite. The younger Stastny responded, “You got that right.”

via MaxHockey.com Jason Lockhart.

Read the whole article, though.

Puckprospectus.com: The Plus and Minus of Plus/Minus

Hockey is usually not the sport where statistical or analytical innovation takes place. So it’s ironic that one of the most useful concepts in sports statistics has been an official NHL statistic for over 40 seasons, leading other sports by at least three decades. The statistic in question is Plus/Minus, which is perhaps the biggest subject of debate in hockey right now. Even though it accounts for the two most important actions in hockey, scoring goals and preventing goals, nobody can agree on what’s wrong with it or how to fix it.

via Puck Prospectus | Articles | The Plus and Minus of Plus/Minus .

And then there’s this:

Let’s make one final adjustment: take a player’s +/- production and subtract from it what happens when he’s not on the ice. In other words, generate a plus/minus relative to his team. This is very similar to what Tom Awad suggested recently:

NAME     RATING  NAME        CORSI RATING

BERGLUND 2.63	 GETZLAF      23.4
DATSYUK	 2.32	 PERRY	      23.0
B. RYAN	 2.16	 BOOTH	      22.2
STREIT	 1.82	 STREIT	      22.2
PERRON	 1.70	 B. RYAN      21.0
WHEELER	 1.67	 M-A.BERGERON 19.3
KREJCI	 1.58	 PENNER       19.1
MONTADOR 1.54	 GOMEZ	      18.8
KLEE	 1.52	 KUNITZ	      18.6
RYDER	 1.52	 PARISE	      18.3

This is a bit more interesting: we still see the top Bruins and Wings, but we also see players who’ve had dominant (and generally unsung) seasons for bad or mediocre teams, like Mark Streit. Perhaps the most interesting player on the Corsi list is the much-maligned Dustin Penner, who has continually failed to live up to expectations in Edmonton despite posting cryptically good numbers on a regular basis.

I especially like the phrase “cryptically good numbers.”