I was going to post something about my trip, which has very little to do with hockey, but I can’t in good conscience now.  Sid’s comeback is just a hair more impressive than mine.  I hope you’ve seen the game or highlights.  Two goals (both backhand shots, no less) and two assists.  Remember, he missed 68 games.  It’s impossible not to make a comparison with Mario Lemieux here.  Lemieux had a much longer absence, to be sure, but from a hockey perspective, Crosby means just as much to the Penguins now as Mario did then.  And, to top it all off, they both scored four points in their respective returns.  Just amazing.

Back to the Sharks- it’s been a great welcome home present for me to see them dismantle Detroit, Dallas, and Colorado in the last three games.  Of course the number one thing to talk about is the penalty kill, which I’m sure we will when we record the podcast this week after the Chicago game.  Just wanted to give a bit of a funny stat.  I decided to calculate how the Sharks ranked in terms of the percentage of games they gave up a PP goal, instead of just overall PK%, where they currently stand at 3rd worst (74.6%).  I fully expected the Sharks to have given up a PP goal in a greater percentage of games than any other team- it seemed like every goddam game the opponent scored with the man advantage.  But it’s not actually the case:

1. Dallas 65% (13/20)
2. Carolina 63.6% (14/22)
3. Islanders 61.1% (11/18)
4. Winnipeg 60% (12/20)
5. Columbus 60% (12/20)
6. Los Angeles 60% (12/20)
7. Detroit 57.8% (11/19)
8. Ottawa 57.4% (12/21)
9. Toronto 57.4% (12/21)
10. Colorado 57.4% (12/21)
11. San Jose 55.5% (10/18)

Dallas is a full seven percentage points better in PK% – 17th overall, the best on this list-  and yet has given up a PP goal in 65% of the games they’ve played.  I don’t really know what that means, but it’s interesting.

As I mentioned on the last podcast, I’ve managed to read the latest Hockey Prospectus annual from cover to cover in seven days.  Hockey Prospectus, formerly known as Puck Prospectus (which I like better, because hey, who doesn’t like alliteration?), is site of hockey writers/fans that plumb the vast depths of advanced hockey analytics, relying on, and in many cases inventing, the advanced stats that I’ve put in the Stats Glossary.  I didn’t expect whipping through the entire thing, and certainly not that quickly.  I more expected to read the way I did last year’s, which was to look up the Sharks, read the articles at the end about different stats topics, and browse a bit for certain players and teams.  Not this time.

Before I go on, I know Fear the Fin has already written about the book recently (which they got for free, dammit!), so I’m going to hope you all believe I’m been planning this post since I first bought the PDF last week.  Either way, I’m going to try to avoid talking about the same topics in that post.  Mostly, I’ll be pointing out fun facts and jabs the guys at Hockey Prospectus have levied on various NHL players, coaches, and management.  It’s really one of the unexpected delights of the book.  But the articles are always great, I would especially recommend the ones on Ultimate Faceoff Percentage (UFO%) and Core Age.

Here are, in no particular order, are some of my favorite quotes and stats from the book:

• “George Parros has certainly earned his devoted fan base with his community charity work, and was named the fourth-smartest professional athlete by the Sporting News, thanks to his time studying economics in Princeton.  Perhaps he can apply that knowledge to discuss Jason Blake’s contract with Bob Murray.”
• “At 6’7”, homegrown St. Louis Blue Ben Bishop is the tallest goalie to ever play in the NHL, but his suspect .896 save percentage in the AHL will have to improve if he hopes to make a more permanent return. Bishop can only move diagonally.”
• “(Bobby Ryan) also gets along great with Corey Perry since they both have two first names.”
• “John Scott is one of those few players who make the league minimum, but is yet overpaid.”
• “Huet played in Switzerland last year, but could play his final couple of years in the NHL once his deal expires this season (or says our Cristobal).”  <rimshot>
• “Steve MacIntyre can punch people really hard in the face.”
• “Brian Elliot was having a terrible season with the Senators before he was dealt in exchange for Craig Anderson.  Then he got worse.”
• “Of all players drafted since 2005, only Sidney Crosby and Anze Kopitar have played in more games than Marc-Edouard Vlasic.”
• “David LeNeveu’s save percentage is .887 in 22 NHL games, and .908 in the AHL.  Except for emergency situations, David should LeNever be used.”
• “With the Edmonton franchise now entering uncertain Year X of a rebuilding phase meant to last until indeterminate Year Y, Tambellini has at least proven completely capable of putting together the very worst roster in the league.”
• “All things considered, Ott is a gem of a player who doesn’t get the credit he deserves around the league as a whole because of the style that he plays.”  (That one hurt a little, but I agree.)
• “A big, rugged defenseman who had a fantastic defensive season in 2009-10, Matt Carkner’s defensive GVT wasn’t nearly as impressive last year, but was still solid. He’s also willing to punch people when Chris Neil needs to rest his hands.”
• “Cam Janssen is quite possibly the league’s worst player, whose only useful purpose is alerting other players that their careers are probably over whenever coaches line him up alongside them.”
• “No team needs more than one of Mike Brown, Colton Orr, or Jay Rosehill on its roster. Brown is plenty. Indeed, some teams would be happy to have none of them. And yet, here they all are, together again.”
• “This past season, only four forwards who played more than 30 games in the NHL had a zone start ratio above 70%: cheap shot artist and general liability Cam Janssen (74.4%) and the Vancouver Canucks first line (Henrik Sedin, Daniel Sedin, and Alex Burrows). To put those numbers in further context, only eight forwards had zone starts north of 65%… Vigneault is able to shower the twins with some of the softest possible ice time (for a first-line scoring unit at least) for several reasons: primary amongst them is this season’s Selke Trophy winner, Ryan Kesler… Of course, even Kesler can’t take all the defensive zone draws, which is why the Canuck’s bottom six is littered with players with awful zone starts and mostly marginal output as a result. In the last two seasons, Vigneault has opted to feed his bottom end the less desirable minutes, forcing guys like Ryan Johnson, Darcy Hordichuk, Tanner Glass, Jannik Hansen, Raffi Torres, and Manny Malhotra to climb uphill in order to cede the high ground to the club’s scorers. Last season, for instance, the trio of Malhotra, Torres, and Hansen had the very worst combined zone start of any regular forward unit in the entire league, 25.0%, 29.6%, and 34.3% respectively… The Sedins have enjoyed a meteoric rise to the very top of the NHL scoring charts the last couple of seasons and have been spoken of as legitimate Hart candidates as well. However, the truth of the matter is they are very good players who have stood on the shoulders of their coach and teammates to become elite ones. “
• (About Ryane Clowe) “Those mourning the demise of the “power forward” in the NHL are no doubt huge fans of the Newfoundland native, who was one of just three players in the league last season to amass at least 60 points and 100 PIM.”
• The Sharks have zero (0) prospects in Corey Pronman’s top 100.  Charlie Coyle is #69.
• Joe Thornton is 4th in the league in combined GVT since the lockout, behind Ovechkin, Crosby, and Datsyuk.  Boyle is 4th amongst defensemen behind Lidstrom, Rafalski, and Visnovsky.  People forget about the little guy.

Our Books We Like link on the right there is active, so if you want to buy this (and you should), go ahead and click that link first.  It’ll throw a buck or two our way, and not cost you anything.

I’m aware, and slightly sorry, for the lack of written word in the past few months.  I feel as though the podcast covered most of what we had to say.  But I’m am going to try and write a little more once the season begins.  For instance, I just added a Stats Glossary there on the right side, so click on that and read about some wonderful advanced hockey numbers.  I plan to do a longer stats-related post sometime later this week, so get your pillows out!

It’s been a while since I wrote a stats-related post, and I figured I’d write one to piggyback on an interesting post on the Behind the Net blog (not to be confused with Behind the Net, the numbers site).  A while back I did a purge on the RSS feeds I read, and for some stupid reason, this blog was amongst the casualties.  I really must have been in a slash-and-burn mood that day, because it’s really one of the best hockey blogs out there if you are statistically inclined. What I want to do today is highlight some of the stats talked about in the linked post, and who on the Sharks are the best at those categories.

First of all, some real quick and dirty explanations of some of the stats referenced there.  I would recommend reading more about them, but not everyone can spend hours reading about advanced hockey metrics.  And as Doug would say, why would you want to?

• GVT – Goals Versus Threshold.  A complicated stat that tries to create one number for the value of a player, measured in goals in a season versus the value of a replacement-level player.  Similar to VORP in baseball.
• Rating – a BTN stat that is the difference between your team’s +/- per 60 minutes when you are on the ice versus when you are off the ice.  Unlike the regular +/- stat, it helps level the playing field for those on bad teams.
• QualComp – quality of competition.   The weighted average of the Rating of the players you face on the ice.
• Corsi – a +/- stat that counts shots instead of goals.  Actually, it counts all pucks directed towards the net, including missed and blocked shots.  Unfortunately, it’s similar to the +/- stat in that players on good teams generally have better ones.  Of the 28 players that have played a game for the Sharks this season, only 10 have negative Corsi, and most (Moore, Mashinter, Desjardins, McLaren, Wingells) aren’t regulars.
• Corsi Rel – The difference in your Corsi when you’re on the ice versus off.
• Corsi Rel QoC – Quality of Competition calculated not by +/- per 60, but Corsi Rel.
• Zone Starts – the percentage of shift-starting faceoffs being in the offensive zone.

If you’re still reading, pat yourself on the back, because that’s a load of math.  Let’s highlight the different Sharks players leading the categories in the stats that the LOES highlighted, in the order that I think is most important. The following is all 5v5 stats, and I’m not including anyone that’s played fewer than 10 games.

Corsi Rel – Kyle Wellwood – 14.6

It’s surprising, and doubtless related to a red-hot Joe Pavelski and clicking third line since he arrived.  Still, Wellwood leads the team in a stat I believe is miles better than +/-.  One downside to Corsi Rel is that time-on-ice isn’t factored in, and it should be noted Wellwood has averaged only 13.07 minutes of even-strength ice time per game, good enough for 15th on the Sharks.  For this reason, it’s worth mentioning the second place player, Ryane Clowe (14.1), who’s averaging more than two minutes more 5v5 ice time, and who I might argue is the team’s MVP.  Top Corsi Rel among defensemen? Jason Demers (8.6).

QualComp – Patrick Marleau – 0.101

Marleau is way out in front on this stat, with the second place Joe Thornton at 0.087.  Despite the fact that Marleau tends to play the wing more now, traditionally not as defensively important as center, he’s  the go-to guy when the other team’s top line is on the ice.  Top defenseman – Dan Boyle (0.062).

Corsi Rel QoC – Patrick Marleau – 0.885

I’m not sure why the LOES like Corsi so much yet mention QualComp instead CorsiRel QoC.  If Corsi is better than +/-, then Corsi Rel QoC is better than QualComp.  Maybe that’s what they meant.  Anyway, unsurprisingly, Mareau leads again, but there’s a bit of shifting under him.  Jumbo drops to 5th on the team, and Joe Pavelski (0.747) moves up to 2nd.  Boyle moves up to 3rd.

Zone Starts – Scott Nichol – 39.4

This means when Nichol took a faceoff to start a shift, 60% of the time it was in the defensive zone.  That’s a lot of trust from the coaching staff, and certainly related to the fact that Nichol is the best faceoff guy on the team. Like the last stat, it’s a way of measuring how sheltered a guy is.  It’s been calculated that you give up about 0.25 shots every time you take a faceoff in the defensive zone, so this is why Nichol’s Corsi isn’t so good.  With that in mind, it’s unsurprising that Marc-Edouard Vlasic (46.8) has the lowest zone start percentage among defensemen.

Time on Ice – Dan Boyle – 19.13

No doubt Boyle is the workhorse, and even strength is no exception.  He also plays the most PP and ES time.  Contrast this to the Ducks (for instance), with Vish leading the category, but if you look for #2, you see that Toni Lydman and Cam Fowler play about the same amount.  However, Fowler plays almost no PK, and three and a half minutes per game on the PP.  Lydman is the opposite, almost no PP time, but is way out in front of PK time. Certainly important when trying to evaluate a player.

I didn’t include GVT here because there isn’t a day-by-day calculation of GVT that I know of, and to be honest, GVT makes a lot of assumptions about the weights of various measures that I don’t necessarily agree with.  I won’t go so far as to say the attempt to create one stat that measures everything is a fool’s errand, but I feel like I get a better picture of a player when I look at several stats, and not just one.

Just a note for tonight- Jamie Benn and Alex Gologoski lead the Stars in Corsi Rel, so watch out for those guys.

I like reading sports books, and I like reading economics-type books.  Which is why I was interested to read Sportscasting: The Hidden Influences Behind How Sports Are Played and Games Are Won.  Worst case, they’d have some interesting theories about sports with scarce or anecdotal data to back it up (the way some people criticize Malcom Gladwell’s work) and best case it would really shed light on some interesting sports conundrums.  So which is it?

Actually, the latter.  And in order to achieve it, the authors crunched a ton (is it tonne in Canada?) of data.  It’s not nearly as breezy as a Gladwell read, but it’s meatier.  More tables, more numbers, more statistics, more explanations and hedges about what can properly be controlled for, and what can’t.  Those who have read this site for a while and listened to our podcasts know I love that kind of thing.   A couple of long chapters of the book are setting up the dominos to answer the question- why do home teams have such a big advantage?

There’s no doubt there IS an advantage, and it’s substantial in pretty much every sport.  Soccer is the most lopsided, with well over 60% of games being won by the home team (they calculated the numbers for MLS, EPL, Serie A, La Liga, and others).  Basketball is next, with the NBA home teams winning 62.7% (they even calculate WNBA and college).  The NHL is next, with 59% percent of home teams winning, and football (57.8%) and baseball (54.1%) bringing up the rear.

So, some things that are interesting about this discussion, the first being obvious, the others not (but backed up by the data).

1. Home teams win the majority of games, sometimes a significant majority.
2. This winning percentage is constant across time.  The winning percentage of home teams was about the same 50 years ago as it was 10 years ago, or now.
3. The winning percentage is directly related to the sport itself.  Japanese baseball home teams have about the same winning percentage as MLB home teams.  Arena football the same as the NFL.

I’ve never thought about this too much before, but even #1 is really remarkable.  Why do home teams win so much, and so consistently?  There has been no NHL season where away teams won more games than home teams. As watchers of plenty of NHL games, I’m sure we all think of several reasons why this is.  One is the home crowds- the home players play better when you’re cheering them rather than booing them.  Another is travel- away teams have to deal with hotels, unfamiliar surroundings, and jetlag.

Incredibly, the authors make very good cases that both of these are myths.  It’s really hard to control for home crowds, because there are so many other interactions going on.  But here’s one feat in hockey that’s essentially isolated from all those player and referee interactions- the shootout.  It’s basically an interaction between two players and the crowd.  So if the crowd were a factor in home player’s effort and performance, you expect the shootout to have a home-rink advantage the way the rest of the game does, right?  Well, it doesn’t.  Since the shootout started, away teams won the shootout 50.6% of the time.  The home-rink advantage just doesn’t exist in the shootout.

And, amazing, they manage to control for travel as well.  How could you do that? Well, what about teams that are really close together, like the Devils, Rangers, and Islanders?  You’d expect less home advantage when those teams play each other, because they don’t have to really go anywhere- just drive a bit further.  But if you look at those games, the home advantage is exactly the same as all the other games.  They found there is a small effect with back-to-back games, which in most sports occur more often on the road.  But that’s not nearly enough to explain it all.

So what the hell is it?  I’m going to put a break here in the post, because some people might actually want to read the book and not get the spoiler.  I’ve condensed many pages into this post, and believe me, it’s worthwhile to read all the other support the authors have come up with.  Or, more likely, you just want to bail out because there’s too many words reading sux zzzzz…..

(more…)

Last night, the prominent emotion I felt after Pavs scored was relief, and not elation.  The Sharks are seemingly back on track for the moment, tying the series, again putting up more scoring chances than the opposition, but this time they won.  I got the idea for today’s post by reading this, and to a lesser extent, tweeting this last night.  Marleau, for some reason, looks largely disinterested in this series, and outside of a couple of speedy drives to the net, has seemingly avoided the Flying Body Show that this series has been so far.  The difference in his play from Seto’s, for instance, could not be more stark.  Seto is hitting everything that moves, grinding it out, and Marleau is trying the shifty thing, neither taking nor issuing hits.  But judging a guy on how he ‘looks’ is awfully subjective, and prone to bias.  How can we judge their effectiveness?

One way is Corsi number.  This is a number that Randy Hahn and Drew Remenda talked about on the telecast many times, though they call it “shots directed at net”.  That is, shots + missed shots + blocked shots.  Corsi is merely that, but you also subtract the opponent’s number from yours.  At that point, you have something kind of a like a shot +/-.  The events are much more common than goals, so you have a much larger sample size and thus less variation.  Corsi (or Hardwick, which is the same as Corsi but doesn’t include blocked shots) can also be calculated for each individual player.  Here are the season numbers for San Jose.  I believe this is normalized for ice time, otherwise we wouldn’t have fractional numbers.  But as we can see, we have Boyle #1, and Marleau #2 (I don’t count Ferriero really).  Thanks to timeonice.com, let’s look at playoff numbers through four games (not normalized for ice time).

First thing to notice is that all of these numbers are positive, which is really remarkable.  That’s just another way of saying the Sharks have vastly out-chanced and out-shot the Avs in the series.  Also, we can see Marleau is currently 5th among forwards, and behind Kent Huskins, who was barely positive in the regular season.  Thornton and Heatley did not have good games 1 or 4 (and Heatley even missed game 3), and that accounts for their low numbers.  As one would expect, the numbers for the top line are all more or less in line for each of the games- low single digits for games 1 and 2, around 10 for game 3, and back down for game four.  The main reason why Marleau is above the other two is because of game 4, where he was +8, where Thornton was +1 and Heatley -1.  So my observation that Marleau was doing particularly bad was almost completely backwards.  Still, all in all, this chart confirms with hard numbers what we already thought- the top line is not performing.  Not even close.  If we can get those guys rolling, we can expect the Sharks to roll better too.

Only hours away before puckdrop, and in visiting my usual blog suspects this morning, I got a little more interested in the goaltending matchup.  On the podcast (scroll down) David said that Anderson needs to have a near-perfect series in order to win, and I guess I might have mistakenly took this to mean that the Sharks have a goaltending advantage.  Gabe Desjardins disagrees, to put it mildly:

Is there any aspect of the game where Colorado’s better than San Jose?  Just one: goaltending.  Craig Anderson is a vastly better goaltender than Evgeni Nabokov, even if Nabokov has somehow managed to put up respectable numbers this season.

I asked him about it a bit in the comments saying I’d put them about even, and his reply was:

Since the lockout: Craig Anderson save percentage = .916; Nabokov = .910. Nabby sucked for four seasons; he didn’t become good this year.

Interesting.  I wouldn’t make the claim that Nabby is an elite goaltender, and reading the great Brodeur Is a Fraud blog where the argument is made that SV% isn’t the perfect stat, but it’s a hell of a lot better than all the others, seems to back this up.  However, Nabby does have a better SV% this year than Anderson – .921 to .916.  Also, after reading this and this from Jonathan Willis, we see that Nabby and Anderson are above average in consistency, with SV% standard deviations of 0.064 and 0.054, respectively.  Those are new numbers I calculated based on their stats from the entire regular season.  We did see Nabby regress a bit in save percentage, as Gabe pointed out, but stayed relatively consistent.

Also, since Nabby ‘sucked’ the last four years, I wanted to find out the difference between sucking and not.  So let’s look at last year, where Nabby’s SV% was 0.910, good for 27th in the league.  Certainly not great, not even good.  If Nabby faced the exact same number of shots, and ended up with a 0.921 SV%, a tick better than Bobby Lou and good for 4th in the league, I think we could call that a great (or even elite) performance.  So what was the difference between Nabby’s and Luongo’s performances?

That’s seventeen goals over the course of the season, equivalent to around 4 or 5 wins.  Another way to put it , since Nabby only played 62 games, that’s one goal every 3.6 outings.  To me, that doesn’t sound like a lot.  It really shows that the difference between an average or below-average goalie and an excellent goalie is very small- just one fewer shot facing a late lateral push, an open 5-hole or a sluggish glove.  If Nabokov didn’t do that once every 216 minutes of playing time he would have been a top-5 goalie in the NHL last year, versus a top-30.

Oh yeah, GO SHARKS.

This comes from an email we got for the podcast (make sure to listen, we actually have a guy with credibility this week), asking essentially, “Does the Sharks losing streak (or later, the winning streak) help or hurt the Sharks’ playoff chances?  Does success in the last 10 or 20 games result in deep playoff runs?”

This is an important question, and let me be perfectly honest- Gabe Desjardins’ recent post on something similar is probably a lot better than this one is going to be.  So I’d read this first with a sense of charity, then click over to Behind the Net and read the real stuff.  To defend myself, I was planning this post since a week ago, so I’m not copying Gabe- I swear.

To answer this question, I compiled the record of the last 10 and 20 games of every playoff team since 2001.  This gives us 112 teams, which isn’t a giant sample size, but it should smooth over some rough edges.  I correlated this (Pearson product-moment if you must know) against the round where they eventually lost (or not), but then I decided to copy Gabe just a little and use playoff wins instead, because that gives us more granularity than just rounds achieved- we get 16 gradations instead of 5.

Here’s the chart.  I’ll explain in a minute.

blah blah blah math

Ok, so this is what’s called an XY scatter plot, with the number of points in the last n games (red dots are last 10 games, blue dots are last 20) versus how deep that team went in the playoffs.  0 wins is swept in the first round, 16 is the Stanley Cup Champions.  If there were a strong correlation, we would see something of a line going from the lower left to upper right in each color.  That is, the teams that do poorly in the last bit of the season also bow out early.  Or we might see something completely counter-intuitive-  a negative correlation, in a line that goes from the upper left to bottom right.  That would signal that teams that do well in the final stretch “peak too early” and are more likely to bow out in the first few rounds.

We have what we call in the mathematics world, a “mess”.

Of course there’s going to be some variance, and the graph would be more like a cloud than a line, but here we just have a plate of spaghetti.  If you want numbers, the correlation for the last 20 games points and playoff wins is 0.12, and the last 10 wins 0.08.  Correlation ranges from -1 (late points always means bowing out early) to +1 (late points means going deep in the postseason).  0.12 is slightly positive- it’s probably a tiny bit helpful to do well late in the season- but it’s certainly nothing predictive.  It’s essentially a crapshoot- performing worse (or better) doesn’t mean much.  Let’s look at some extremes:

• The team with the most points in the last 20 games among all teams I looked at is the 2006 Red Wings, who only lost one game in regulation in the last 20.  That team lost in the first round to Edmonton.
• That very same year, the Carolina Hurricanes scored only 21 points in the last 20, barely .500.  That’s the second worst record of any of the playoff teams that year (only the #8 New York Islanders were worse).  For those that remember, the Canes won the Cup.
• Last year, the team that had the best late record of all playoff teams, the Pittsburgh Penguins, won the Cup.  They played the Wings (again) in the Finals, who had 12 fewer points in the same number of games, good for 13th amongst playoff teams.

Does anything correlate better to playoff success?  The answer is yes.  The overall 82-game record correlates better: 0.32.  And even better is the correlation of seeding to playoff wins: -0.37 (negative because a lower number (seed) correlates to a higher number (playoff wins)).  This does tell us something interesting- it’s better to be lucky than good.  It’s better to luck into a higher seed with a worse record (in a weak division) than score a ton of standings points and only get the #4 or #5 seed.  With the Sharks guaranteed the #1 or #2, they are in the best possible position.  Now the question is, can they transform this advantage, however slight, into real playoff happiness?

We’re going to find out.  I’m nervous.

Now that the Sharks are firmly in WTF-land, it’s time to prognosticate on their postseason chances.  The Sharks, even as bad as they’ve been playing, are still (barring the craziest thing I’ve ever seen) assured a spot in the postseason.  The question for today is- how do teams fare when they have a major meltdown like this late in the season?  I’m going to define a meltdown as a losing streak of 5 games or more (or 3 games to end the season), and ‘late in the season’ I’ll define as March and April.  Roughly the last 6 weeks.  I’m only doing post-lockout, because before the lockout there were ties, and having a bunch of ties in the middle of a winless streak doesn’t seem like the same thing.  Let’s go to the chart:

Nine teams in four years was a little more than I was expecting.  Six of the nine lost in the first round, which sounds like a lot, but keep in mind 50% of the teams that make it to the playoffs lose in the first round.  That’s really only one team over a random distribution.  Five of the nine were the top four seed, which is basically random.  No 1- or 2-seeds, which I suppose is to be expected- it’s hard to retain the conference lead when you drop 5 or more games in a row late in the year.  The fact that none of the teams went to the finals is also easily within the bounds of random chance.

When I started this tedious process, I was expecting the overwhelming majority (if not all) of the teams to get smoked early in the playoffs, and that’s just not the case.  The two teams that went to the conference finals were four and five seeds, which is likely where the Sharks will end up.  On the flip side of the coin, the top two teams in seeding, the 2009 Devils and 2008 Wild, both lost in the first round.  There also seems to be little correlation between the lateness of the streak and the playoff outcome.  If the Sharks win either Tuesday or Thursday, they will be tied for 6th in ‘lateness’.

So what’s the conclusion?  Losing five games in a row in the last 6 weeks of the season is not significantly correlated to playoff disappointment.  Mostly because Sharks teams of recent years aren’t on this list.  Har!

But seriously, if the Sharks manage to right the ship a little bit this week, there’s not much reason to think they are automatically doomed.  However, if they lose the next ten, I’d say that’ll be unprecedented.

This week is the Week of Secondary Scoring.  I read these posts on Fear the Fin ten days ago, and they put into blog form what everyone was a little worried about – the fact that Heater and Patty were scoring all the goals.  This week, all that changed.

My analysis is different from FTF because I included Joe Thornton, trying to make a distinction between top scorers, top lines, and balanced scoring.  Partly because I think you can’t say Heatley and Marleau would be scoring at the same clip without Big Joe’s 67 54 assists this year.  And also partly because if your top two scorers are on different lines (like Kopitar and Brown in L.A.), your scoring is more balanced than Detroit, Anaheim, and the Sharks, whose top three scorers are on the same line together.  Keep in mind this is an inexact science, since many coaches shuffle lines fairly regularly.  I got these lines from the most recent games these teams have played, thanks to timeonice.com.  Here they are, in current conference standing order.

As expected, the Sharks are way out front, the top line scoring over 20 goals more than any other top line, and accounting for more than two out of every five goals scored.

But this past week, and admittedly it’s a small sample size, it’s wildly different.  The top line, in the four games this week, scored 5 of the 22 goals scored, or 22.7%, lower than any other top line on this list.  The Olympic Line (or the Burger Line, or the HTML line, whatever) will be staying together for the conceivable future, and teams have been targeting them all season to no avail.  That’s not to say that they couldn’t suffer a letdown, maybe after the Olympics, or in the playoffs (again.  Do I really have to type ‘again’ again?).

So which is better- having an unstoppable first line, or having four very even lines like Buffalo had on Saturday?  Frankly, I want the superstars putting up superstar numbers.  If the Sharks only have three guys that can score at all, it doesn’t matter how the lines are constituted, we’ll be in for another playoff disappointment.  But the opportunities presented themselves, Boyle was out of the lineup, and the second and third lines stepped up.  I’m very encouraged.  If Patty, Jumbo, and Heater decide to put up six goals between them per game and freeze everyone else out, I’ll find a way to live with it.