Apr 152011
 

Before I get into the main subject of this post let me first point out that I have updated stats.hockeyanalysis.com to include all 1, 2, 3 and 4 year player ratings that can be calculated using the last 4 years of NHL data.  For more information on my player ratings read this.

I generate offense, defense and overall ratings for each and every player in the NHL and I wanted to get an idea of how much each position contributes to the performance of the team.  To accomplish this I multiplied each players offensive and defensive ratings (HARO+, HARD+) by their ice time (5v5 ratings and ice time used) and summed them up by position and then compared the positions total to the overall total.  I did this using the ratings calculated for the past 4 seasons combined as well as for each of the past 4 individual seasons.  This is the result I came up with :

Offense:

Season(s) Center RW LW D D
2007-11 24.64% 18.04% 17.14% 20.09% 20.09%
2007-08 26.91% 16.22% 16.47% 20.20% 20.20%
2008-09 25.23% 18.01% 16.66% 20.05% 20.05%
2009-10 23.93% 18.47% 17.49% 20.06% 20.06%
2010-11 25.13% 18.02% 16.76% 20.04% 20.04%

Defense:

Season(s) Center RW LW D D G
2007-11 20.67% 15.08% 14.27% 16.72% 16.72% 16.55%
2007-08 22.46% 13.83% 13.81% 16.75% 16.75% 16.39%
2008-09 21.06% 15.49% 13.76% 16.67% 16.67% 16.35%
2009-10 19.98% 15.46% 14.79% 16.73% 16.73% 16.30%
2010-11 21.35% 15.08% 14.21% 16.51% 16.51% 16.35%

Average of Offense + Defense:

Season(s) Center RW LW D D G
2007-11 22.65% 16.56% 15.71% 18.40% 18.40% 8.28%
2007-08 24.69% 15.03% 15.14% 18.48% 18.48% 8.19%
2008-09 23.14% 16.75% 15.21% 18.36% 18.36% 8.17%
2009-10 21.95% 16.96% 16.14% 18.39% 18.39% 8.15%
2010-11 23.24% 16.55% 15.48% 18.27% 18.27% 8.17%

Note:  I split the defense contribution over 2 positions.

Now, the first thing I noticed with these numbers is how surprisingly consistent they are from season to season, especially for defense and goaltending.  Up front players frequently shift from center to wing and from left wing to right wing so that may account for some of the (still relatively small) seasonal fluctuations.  Maybe I shouldn’t be surprised at this consistency but it does give me some confidence in my rating system that it is consistent across seasons as well as with multiple season ratings.

The second thing that caught my attention was the importance of defensive contribution to the offense.  Approximately 40% of offensive production can be attributed to the two defensemen on the ice and the defensemen are more important than the wingers. Part of this is simply that defensemen get more ice time than forwards since there are only 3 defense pairs versus 4 forward lines.  The other part is probably that they play an integral part of collecting rebounds and transitioning the team from defense to offense so they may have greater influence in the percentage of time played in the offensive zone.

Of the three forward positions, the center position is clearly the most important but we probably figured that.  Face offs might be a contributing factor but also we might just find that the most talented players end up playing center.  Right wings are slightly more important than left wings but the difference is not substantial.

Next I wondered what this data would mean to what teams should allocate for salaries.  For a 60 million payroll the average salary for position should work out to the following:

Pos Salary (Million$)
Center 13.6
RW 9.9
LW 9.4
D 11.0
D 11.0
G 5.0

Of course elite players skew the team payroll structure a fair bit.  As a LW earning over $9.5M Alexander Ovechkin is eating up the entire Capitals allotment for LWs and Crosby, Malkin and Staal are way over budget for the Penguins but you have to work around the talent you have.  A couple months ago Behind the Net Hockey Blog had a post outlining the salary allocated to players by position (split between forwards, defense, and goaltending).  Forwards were allocated 59.1% of a teams payroll, defense 32.2% and goaltending 8.7% over the past 4 seasons which compares to 54.9%, 36.8% and 8.3% for my ratings.  That would mean that forwards are overpaid (relative to their contribution) by about 4.1%, defense under paid by 4.6% and goalies over paid by about 0.4%.

For interest sake I decided to take a look at the Vancouver Canucks performance distribution since they have a fairly well balanced team and are a serious cup contender.  Here is what I found:

2007-11 2010-11
Position Offense Defense Average Offense Defense Average
Center 23.44% 19.96% 21.70% 21.04% 17.15% 19.10%
RW 11.44% 9.88% 10.66% 9.97% 10.34% 10.15%
LW 25.14% 21.88% 23.51% 31.12% 25.11% 28.11%
D 19.99% 17.21% 18.60% 18.94% 15.92% 17.43%
D 19.99% 17.21% 18.60% 18.94% 15.92% 17.43%
G 0.00% 13.86% 6.93% 0.00% 15.55% 7.77%

(Note:  The above is calculated using the current roster using the ratings and ice time over the past season or four seasons regardless of whether that ice time was with the Canucks.  This is an evaluation of the team ending the 2010-11 season with the Canucks, not the Canucks team performance over past seasons.  Also four season ratings should give a better player evaluation than single season ratings due to the larger sample size so I would consider them closer to true value.)

The Canucks are definitely a team driven by a group of quality left wingers or at least players listed as playing LW such as D. Sedin, Burrows, Raymond, Torres but I suspect some get shifted to RW from time to time.  Also, as good as Luongo is the quality and depth of the team in front of him reduces his relative contribution to his team to below average levels.  In the future I’ll take a look at some other teams as it’ll be interesting to see how goalie contribution changes from good teams with subpar  goalies (Detroit maybe) to bad teams with good goalies (Florida – Vokoun!! Though my ratings don’t value him as highly as many others do).

Mar 182011
 

The guys over at Behind the Net have initiated a ‘prove shot quality exists’ competition and in response to that Rob Vollman took a quick and dirty look at shooting percentage suppression.  As I showed the other day, Rob’s logic was a little off.

Rob started off by identifying a number of players with high on ice save percentages over the past 3 seasons.  Some of these guys included low minute players mostly playing on the fourth line against other fourth line caliber players, but there were a handful of players who played relative significant number of minutes and still put up good on ice save percentages.  Let me remind you of a few names that Rob identified:  forwards Marco Sturm, Manny Malhotra, Tyler Kennedy, Travis Moen, Taylor Pyatt, Michael Ryder, defensemen Kent Huskins, Sean O’Donnell, Mike Weaver, Mark Stuart.  I’ll get back to these guys later but I’ll claim that Rob dismissed some of them prematurely by claiming they played against weak competition.

As you may or may not know I have developed offensive and defensive ratings for every player and these can be found at http://stats.hockeyanalysis.com/ Furthermore, I have created these using goals for/against as well as shots for/against, fenwick for/against, and corsi for/against.  For clarification, fenwick is shots + missed shots while Corsi is shots + missed shots + blocked shots.  For this study I decided to use fenwick instead of shots because I had the data handy and I was too lazy to get the shot data in the right format but there shouldn’t be a significant difference (the two are very highly correlated).

Continue reading »

Mar 162011
 

I have posted a few articles here recently about the existence of shot quality, one of which related to last seasons Washington Capitals and one related to how shot quality varies according to game score but there are still shot quality deniers out there.  One of the comments I received from a shot quality denier to those posts was as in depth as “You did it wrong” but offered no further explanation.  So there it stands.

Derek Zona and Gabe Desjardins over at Behind the Net Hockey (mostly shot quality deniers) have put up a $150 prize for anyone who can show that shot quality exists.  One method they suggested one could pursue to prove such a thing was the following:

Are there players or teams with the ability to drive or suppress on-ice shooting percentage?  What are their characteristics?

This prompted Rob Vollman (who I presume is a shot quality denier, my apologies if not) to look into just that and to do so he identified a group of players who had the highest save percentage against while they were on the ice.  The theory is, if shot quality suppression was a talent then there should exist players who experience a very good save percentage for their team while they are on the ice.  The group of players identified varied significantly from George Parros to Kyle Wellwood to Sean O’Donnell to Marco Sturm.  In the end Rob came to the conclusion that these players all had high save percentages while they were on the ice because they mostly played against weaker quality of competition.

But none of them are facing their team’s toughest minutes.  If they truly had the ability to suppress shooting percentage, why would Kesler and Burrows hop out against Ovechkin instead of Malhotra?  Why would Pronger keep an eye on Crosby instead of O’Donnell?  Kudos to each of them for playing their roles very well, but the explanation still appears grounded in Quality of Competition.

And there is the fault in logic.

  • Claim:  Shot quality doesn’t exist.
  • Counter-evidence:  Some players do experience higher save percentages while they are on the ice.
  • Rational:  They do so because they play against weaker quality of competition.
  • Claim Confirmed:  Phew, my claim that shot quality doesn’t exist remains valid.

Now the whole problem with that theory is the rational part because the rational part requires shot quality to be real for it to be true.  The only way you can have a better quality of competition (in terms shooting/save percentage) is to have shot quality exist.  If shot quality didn’t exist all competitors would have the same level of shooting percentage talent.  The claim and rational can’t both be true, so the logic fails.

And that is where identifying shot quality becomes difficult.  Players that are generally good at reducing the quality of shots against are lined up against opponents who are generally good at creating quality shots for.  The net result is their talents cancel each other out to some extent making it difficult to identify shot quality driving/suppressing talent just by looking at the numbers in isolation of who they are playing with and against.

Mar 152011
 

I thought this debate had been fully hashed out already but apparently some people still don’t believe that the game score has an impact on shooting percentage (and shot quality).  The following table shows the shooting percentages by game score over the past 3 seasons (2007-08 to 2009-10) during even strength situations where neither goalie is pulled for any reason (including delayed penalty situations).

Situation Shots Goals SH% Prob<= Prob>
Down2+ 23650 1852 7.83 0.3794 0.6206
Down1 30447 2356 7.74 0.1696 0.8304
Tied 60753 4427 7.29 0.0000 1.0000
Up1 26842 2288 8.52 0.9999 0.0001
Up2+ 19351 1779 9.19 1.0000 0.0000
Overall 161043 12702 7.89 0.5024 0.4976

The Situation, Shots, Goals, and SH% columns are self explanatory.  As you can see, shooting percentage is at its lowest in game tied situations, increases slightly for teams that are trailing and increases significantly for teams that are leading.

The second last column titled Prob<= show the probability (according to a binomial distribution) that that number of goals or fewer would be scored on that number of shots if the expected shooting percentage was 7.89%, the same as the overall 5v5 shooting percentage.  The last column titled Prob> is simply 1-Prob<= and shows the probability of getting more than that number of goals on that number of shots.  So, in down 2+ goal situations, there is a 37.94% chance of their being 1852 or fewer goals scored on 23650 shots which indicates that the down2+ shooting percentage isn’t different from the 5v5 mean at any reasonable confidence level.  The same conclusion can be drawn about down1 situations.  But, the shooting percentages in game tied, up1 and up2+ situations are statistically different at an extremely high confidence level.  Essentially there is zero chance that game tied, up1, or up2+ situations have the same natural shooting percentages as game overall 5v5 situations.  In no way can luck be the sole reason for these differences.

So, does this conclusively tell us that shot quality exists and varies according to game score?  It probably does, but I can’t say it is conclusive as it could mean that teams that trail a lot have bad goaltending (the reason they are trailing) and this results in the team leading having an inflated shooting percentage.  So, what if we looked at shots against a particular team.  Let’s say, for example, against the NY Rangers.  Here is what that looks like.

Situation Shots Goals SH% Prob<= Prob>
Overall 5159 386 7.48 0.5135 0.4865
Up1 843 73 8.66 0.9116 0.0884
Up2+ 485 46 9.48 0.9571 0.0429
Leading 1328 119 8.96 0.9800 0.0200
Tied 2004 138 6.89 0.1658 0.8342

I chose the Rangers because they use predominantly one goalie and that goalie is generally speaking a quality goalie.  As you can see, the confidence levels aren’t quite as strong as league wide mostly because of the smaller sample size but if we combine the up1 and up2+ categories we can say that shot quality against the Rangers when the opposing team is leading is statistically different than shooting percentage against the Rangers overall.

If you are interested in seeing what happens with a team that has had chronically bad goaltending, here is the same table for the Maple Leafs.  We see the same sort of things.

Situation Shots Goals SH% Prob<= Prob>
Overall 5309 491 9.25 0.5120 0.4880
Up1 938 94 10.02 0.8098 0.1902
Up2+ 906 100 11.04 0.9698 0.0302
Leading 1844 194 10.52 0.9712 0.0288
Tied 1985 149 7.51 0.0034 0.9966

So what have we learned.

  1. Shooting percentages vary according to game score.
  2. Those shooting percentage differences can’t be attributed to luck.
  3. Those shooting percentage differences can’t be attributed to goaltending.

That means, it must be the quality of the shots that varies across game scores.  In short, we can conclude that when teams get down in a game they open up and take more chances offensively which in turn gives up higher quality shots against which makes perfect sense to me.

When we combine this with my previous post on the Washington Capitals shooting percentage last season, it is probably safe to assume that shot quality exists and we can’t safely assume that all shots can be treated equal in all situations.

Feb 182011
 

First off, it should be a sad day in Leaf land as we all say good bye to Tomas Kaberle.  It seems many people are unaware of just how good Kaberle was and still is.  Here are some facts about Tomas Kaberle:

  1. Since Kaberle entered the NHL in 2008-09 the only defenseman with more assists than Kaberle is Niklas Lidstrom and only Lidstrom, Gonchar and Pronger have more points.
  2. Since the lockout only Lidstrom has more assists among defensemen and only Lidstrom and Rafalski have more points.
  3. Among all skaters, not just defensemen, Kaberle ranks 20th in assists since the lockout and has more assists than Vincent Lecavalier and Eric Staal.
  4. His point production has not tailed off significantly the past several seasons despite many people seemingly believing otherwise.  He had 49 points last season and is on pace for about 52 this season.
  5. Only 2 defensemen (Keith, Enstrom) have more combined assists this season and last and only 8 defensemen (Green, Keith, Boyle, Lidstrom, Enstrom, Visnovsky, Doughty and Yandle).

That is pretty good if you ask me and while he had his flaws he truly was an elite puck moving and passing defenseman.  He’ll be missed in Toronto.

Now, it is time to take a look at the Leafs future.  What Burke has done the past couple years has actually been pretty extraordinary and for all those who have begged for the Leafs to go with the build through the draft method of team building here is some of the assets currently in the Maple Leaf organization.

  • 2011 Boston 1st round pick
  • 2011 Philadelphia 1st round pick
  • Nazem Kadri – 7th overall 2009
  • Luke Schenn – 5th overall 2008
  • Joe Colborne – 16th overall, 2008
  • Jake Gardiner – 17th overall, 2008
  • Phil Kessel – 5th overall, 2006
  • Dion Phaneuf – 9th overall 2003
  • Joffrey Lupul – 7th overall 2002
  • Mike Komisarek – 7th overall, 2001
  • Fredrik Sjostrom – 11th overall 2001
  • Colby Armstrong – 21st overall, 2001

Now we don’t know what Kadri, Colborne, Gardiner and the two 2011 draft picks will turn out to be, but that is what rebuilding through the draft is all about (and isn’t that what Leaf fans have been demanding).  So while it may not be the traditional build through the draft, what Burke has done to the depth of young talent on these Leafs has been amazing, even if we haven’t seen the results on the ice yet.  On top of that, the Leafs should have about $25M in cap space available for next season (though MacArthur, Bozak, Schenn and Gunnarsson need to be re-signed). Fear not Leaf fans, I believe good times are ahead, and not too far away.

Feb 102011
 

I have seen and heard a lot of comments on the Beauchemin for Lupul and prospect Gardiner trade with respect to Lupul’s value and most of them suggest Lupul was the cost Burke had to pay to have Gardiner included in the trade and that Lupul doesn’t have a lot of value and won’t likely score 25 goals.

From Bitter Leaf Fan Page:

Lupul may be a top six player on the Leafs but this is more a testament of just how thin the Leafs top six is, than it is an indication of Lupul’s so-called talent;

I doubt Lupul pushes the 25 goal mark as many have suggested. He’s only crested that mark twice in 6.5 seasons and it’s rare for an oft-injured 28 year old to suddenly find a scoring touch;

In general, that seems to be the sentiment though there are exceptions.  But honestly, I think everyone is down playing Lupul’s potential to perform.  If you just quickly take a look at his stats you’ll see this:

  • 2005-06:  28g, 53pts, 81 games
  • 2006-07:  16g, 28pts, 81 games
  • 2007-08:  20g, 46pts, 56 games
  • 2007-09:  25g, 50pts, 79 games
  • 2009-10:  10g, 14pts, 23 games
  • 2010-11:  5g, 13pts, 26 games

So, in 6 years he has only scored 25 goals twice and only scored 20 goals half the time, but that is in large part due to his injuries.  If we prorate his goals scored to 82 games each season his goal totals would be 28, 16, 29, 26, 35, 16.  Looking at those totals and things seem a little more impressive.  He’d have been a 4 time 25 goal guy in the past six seasons.  The two seasons he didn’t reach 25 goal pace was an awful season in Edmonton on an awful team and this current season when he has been recovering from a serious back injury and has seen his ice time fall and has played more time with lesser talented players.  Over his entire career he has scored 117g in 421 games which works out to 23 goals over 82 games.  In the playoffs he has scored 14g in 39 games which equates to 29 goals over 82 games.  Based on this there is no reason not to expect Lupul to be a 25 goal guy, maybe even more, if he is healthy.  Over the past 3 seasons there has been an average of 63 twenty five goal scorers (or just over 2 per team) and 110 twenty goal scorers (or under 4 per team) so I am perfectly happy to see the Leafs add another one to their lineup which already included 3 guys (Kessel, Grabovski, Kulemin) who should reach 25 this season and two more who should/could reach 20 (MacArthur, Versteeg).

Now I’ll grant the naysayers that Lupul’s health is a question mark and whether he can return to pre-back surgery and subsequent infection form is a question mark, but it is certainly a question mark that is smaller (probably much smaller) than any question marks that would come with a draft pick or a 20 year old prospect who has never played an NHL game (see Caputi, Luca).  As for Lupul’s salary, who cares.  Yes, it is probably $1.5M on the high side, but the Leafs have ample salary cap space this season and going forward and if they need more have the financial ability to bury contracts in the AHL (see Finger, Jeff).  I don’t see it as an issue.

All in all, I love this trade for the Leafs.  They traded a good, but not irreplaceable defenseman for a potential 25 goal winger and a good defense prospect.  They haven’t really solved their #1 center hole yet, but they have really filled in the parts around that hole nicely.  If they could just plug in Brad Richard’s I’d be more than happy with next seasons group of forwards.

Kessel-Richards-Lupul
MacArthur-Grabovski-Kulemin
Versteeg-Kadri-Armstrong
Brown-Brent-Orr
Jan 302011
 

Yesterday there was a post on the Behind the Net Blog which discussed the Washington Capital’s 2009-10 even strength shooting percentage of 11.0% and the conclusion was that it must be mostly luck which resulted in a shooting percentage that high.  But was it?  It was noted in the article that in 2007-08 the Capitals shot at 8.1%, in 2008-09 they shot at 8.2% and this season they are shooting at 8.2% again.  So clearly 2009-10 appears to be an anomaly, but was it a luck driven anomaly or something else?

Most people in the hockey analysis world have been using a simple binomial distribution to simulate luck so I’ll do that here too.  The thing is, if the Washington Capitals were really a 8.2% shooting team last year, the chances of them shooting 11.0% or better on 2045 shots is a mere 0.0042%.  That kind of luck we should expect once every 8000 NHL seasons.  In short, we can be pretty confident that the Capitals 11.0% shooting percentage wasn’t all luck driven.

So the next question is, how much of it is luck, and how much can we attribute to other factors?  Well, let’s assume that their good luck was significant to the point where there would only be a 5% chance they could have experienced even more luck.  We can do this by constructing a binomial distribution using centered on a shooting percentage where the chance of producing a shooting percentage of >11.0% is 5%.  The result is shown in the following chart:

The far left vertical line is the number of goals that Washington would produce if they had an 8.2% shooting percentage and the far right line is their actual shooting percentage.  The center vertical line is the theoretical shooting percentage we would need to meet the 5% luck conditions outlined above.  Under this scenario one could suggest of the extra 57 goals that Washington scored above what they would get if they shot at 8.2%, 22 of those goals can be attributed to luck and 35 can be attributed to skill.

But what if we assumed the Capitals were extremely lucky and there was only 1% chance of having greater luck.  Under that scenario their true talent level would be 9.49% shooting percentage and 26 goals would be due to skill and 31 would be due to luck.

Regardless of how you want to look at it, a significant portion of the Capitals elevated shooting percentage was likely due to non-luck factors, be they actual talent, playing style, score effects, etc.

Jan 092011
 

The Los Angeles Kings have signed Jack Johnson to a 7 year contract extension which will pay him $3.5 million the first 3 seasons and $5 million the final four seasons with a cap hit that works out to a cap hit of $4.36M per season.  So the question is, is it a good deal for the Kings?  I am not sure it is.

First, let me start off by saying that I really don’t watch the Kings that much so I haven’t seen Jack Johnson play all that much.  My comments here are based purely on a statistical analysis.  For some of you that makes these opinions objective, for others it probably means you think I am out to lunch, how can you fairly evaluate someone without having watched him a lot.  So be it.

So, lets start off with the good.  Over the past couple of seasons he has significantly improved his offensive output, especially on the PP.  In 2007-08 he had 3g, 11pts in 74 games.  In 2008-09 he had 6g, 11pts in just 41 games.  Last season was a bit of a breakout year for him as he posted 8g, 36pts in 80 games and this season he has taken that up another level with 4g, 31pts in just 41 games.  That said, the majority of his point production increase this season has been on the power play where he has 3g and 21 points or 68% of his points vs 36% one year ago.  Of course, his PP ice time has risen from 2:48 a game to 4:02 a game so that was a factor.  His PP performance so far seems to be coming at the expense of Drew Doughty who has seen his PP points drop significantly this season from last.    He had 23 even strength points last season and is on pace for 20 this season so even strength there is no real improvement.

Now for the bad, or should I say ugly.  It can be shown that statistically he has been and still is one of the worst defensive defensemen in the NHL.  Of the 110 NHL defensemen who have had 200+ minutes in 5v5 game tied situations, Johnson ranks 109th in my HARD+ rating system with a 0.588 score (a 1.00 score would be an average defenseman) which evaluates a players defensive performance while taking into consideration the quality of both his teammates and the opposition he plays against.  This isn’t anything new.  Of the 92 defensemen who played 400+ 5v5 game tied minutes last season Jack Johnson finished dead last in my HARD+ rating.  If you are one of those people who prefer to use fenwick/corsi, Jack Johnson finished 86th of 92 in my FenHARD+ rating last season.

If you don’t fully understand my HARD+ rating systems that’s OK, you can take a look here to see all Kings defensemen sorted by FenF% (Fenwick For / (Fenwick For + Fenwick Against) and you will see that last season he was dead last among Kings defensemen with 50+ minutes 5v5 game tied.  To to be fair, he is a bit better so far this season in FenF% but that isn’t the case in GF% (goals for / (goals for + goals against)).  It seems the coaches are questioning his defensive responsibility as well as his short handed ice time has been cut from 1:35 a game last year to 1:07 a game this year.

No matter how you look at the numbers, Jack Johnson has probably been  somewhere between bad and dreadful defensively thus far in his career and while he looks to be developing into a good, or maybe very good, offensive defenseman, particularly on the PP, one has to wonder if making a 7 year big $$ commitment to him was a wise decision.  It probably isn’t unusual for defensemen to improve their defensive skills as they age but Johnson has a long way to go to even become an average defensive defenseman.  It was a risky signing in my opinion that the Kings may regret down the road.  It’s a lot to pay for a PP specialist, especially when you already have Doughty, a much better player in all aspects of the game including probably the PP, already on your roster.

Jan 062011
 

The score of a game influences how a team plays.  When a team is trailing they play a more aggressive offensive game, when they are up a goal or more, they play a more defensive game.  The question I answer today is, how does score influence a teams save percentage.

To answer this question I looked at the past 3 seasons of 5v5 even strength save percentage data when the score is tied, when the team is up by a goal, when the team is up by 2 or more goals, when the team is down a goal and when the team is down by 2 or more goals.  For each team and score category I have a data point for 2007-08, 2008-09, 2009-10 as well as a three year average (2007-10).  For each score category I sorted from lowest to highest save percentage and then plotted them on one chart and got the following:

As you can see, when the game is tied generally produces higher save percentages than when a team is leading or trailing and when a team is trailing their save percentages are at their worst.  This is probably not surprising as a team will open up its game in hopes of creating offense but also puts them at risk defensively.  Now, what that table doesn’t tell us is if all teams experience the same score effects or, for whatever reason, do some teams actually have improved save percentages when trailing or leading.  The following chart shows each teams 3 year save percentage by score ordered from lowest 5v5 game tied save percentage.

The majority of teams have the majority of their leading or trailing save percentages below the game tied save percentages but there are a number of occassions where that doesn’t occur and they are mostly related to up2 or up2+ save percentages.  The only teams that had a down1 or down2+ save percentage above game tied save percentage were:

  1. Dallas – Down1: 92.51% vs Tied: 91.74%
  2. Detroit – Down1: 93.05% vs Tied: 92.16%
  3. Pittsburgh: Down2+: 92.87% vs Tied: 92.78%
  4. Minnesota:  Down2+: 93.21% vs Tied: 92.89%
  5. Florida: Down1: 93.92% vs Tied: 93.23%

On average, teams had their down 1 goal save percentage 1.3% lower than their game tied save percentage and their down 2+ goal save percentage 1.90% lower than their game tied save percentage.  The average team save percentage at 5v5 tied is 92.7% vs 91.4% down a goal, 90.8% down 2+ goals, 92.2% up a goal and 92.1% up 2 goals.  Tailing can have a sizable negative impact on save percentage where as leading can have a minor negative impact.

So what does this mean?  It means we need to be careful when evaluating goalies (and probably shooters to some extent) based on save percentage (special team effects) or even 5v5 even strength save percentage because the game situations a goalie has been exposed to will influence the goalies save percentage.  A goalie on a weak team will have his save percentage lowered simply because his team is going to be trailing more often and be forced to take chances to create offense and thus he will be exposed to tougher shots where as a goalie on a good team who leads the game more than they trail a lot will not face as many tough shots.

One interesting thing I noticed while doing all this was the Toronto Maple Leafs up by a single goal performance over the last 3 seasons.  While they were middle of the pack 5v5 game tied (16th in 3 year 5v5 game tied save percentage), they were downright horrific when they got up a goal.  They just couldn’t hold a lead.  The three worst single season save percentages when up a goal were the 2009-10 Leafs, 2008-09 Leafs, and the 2007-08 Leafs so they were three for three there.  Over the course of the past 3 seasons the Leafs posted an 88.4 save percentage when up a goal which was 3.44 standard deviations from the mean.  Next worse what the Ottawa Senators who were well ahead of them at 90.8, a mere 1.23 standard deviations from the mean.  The good news for Leaf fans is their 5v5 up a goal save percentage is much better this year: 95.6% (better than any team in any of the last 3 seasons), 97.2 for Gustavsson and 93.9% for Giguere so they are much better at maintaining the lead.  Unfortunately this season they can’t score well enough to get them a lead to protect.

Dec 162010
 

In the Hockey Statistical Analysis world Tomas Vokoun is an interesting case study because depending on how he gets evaluated he either shows up as an very good goalie or in some cases a true elite goalie in the NHL.  Most ways we evaluate goalies has to do with save percentages.  We either look at overall save percentage or even strength save percentage or even even strength game tied save percentage.  Under all of these scenarios Vokoun excels to various degrees.  A recent Behind the Net Hockey Blog post asked several hockey statistic analysts to discuss “elite goalies” and Tomas Vokoun’s name came up frequently.  What is dumbfounding to me is Vokoun’s record because his won-loss record (79-80-25) is notably worse over the past 3 seasons than his backups (32-22-8).  That can’t be a sign of an elite goalie, even if his backups have been relatively good (i.e. Craig Anderson).  One may postulate it is due to facing tougher competition as backup goalies often get the to play against weaker teams or one may postulate it is just due to bad luck.  Or maybe, he just isn’t a great goalie.

Since shots totals and shooting/save percentage is often affected by game score I’ll focus on 5v5 even strength game tied statistics to balance everything out.  Over the last 3 seasons (2007-08 to 2009-10) there are 35 goalies with 1500 or more 5v5 game tied minutes.  Of these goalies, Tomas Vokoun ranks 8th in 5v5 game tied save percentage which may not be elite, but still very good.  Jonas Hiller tops the list with a .942 save % with Vokoun at .933 and Chris Osgood trails the list with a .906 save %.  So, Vokoun looks pretty good.

But, Tomas Vokoun ranks just 23rd in goals against average which isn’t great and probably average at best.  Those who are in love with fenwick numbers will note that Vokoun has the second highest fenwick against of any goalies with 1500+ 5v5 tied minutes and he gives up so many goals because Florida gives up so many shots and scoring chances.  Of course, I believe that not all shots against are equal and shot totals can be influenced by style of play as much as talent.  If you don’t believe style of play affects shot totals and scoring chances, ask yourself why there are score effects on shot/corsi totals?  The answer is depending on the score, teams play differently.  But teams play differently when the score is tied as well.  Some teams play a defense first style, even when game is tied, and others play a more wide open offensive style.  Florida, without any true elite offensive stars, probably plays more of a defensive game which would naturally lead to more shots against, but not necessarily more quality scoring chances against.

So yes, Florida gives up a lot of shots, but how good is Tomas Vokoun’s competition really.  He does play in the weakest division in the NHL and yet he can’t produce a good won-loss record.  Just looking at Vokoun’s opposition, his opponents rank dead last in goals for per 20 minutes so compared to other goalies he is playing against relatively weak opponents offensively.  His oppositions GF% (goals for / goals for + against) is also fourth worst so overall so he plays against very weak opposition in terms of scoring goals and stopping goals.  For those who prefer Fenwick, his opposition has a FF% (fenwick for / fenwick for + against) of .499, good for 27th among the 35 goalies.  So his opposition isn’t good and his performance in goals against average isn’t good either.  That isn’t a good combination if you want to be considered an elite level goalie.

How about a direct comparison with his backups.  In 2007-08 his goals against average per 20 minutes was significantly worse than Craig Anderson’s (0.949 for Vokoun, 0.538 for Anderson) while Anderson’s opponents had a slightly better goals for per 20 minutes (0.678 vs 0.671).  In 2008-09 Vokoun had a much better season giving up 0.697 goals per 20 minutes compared to Anderson’s 0.896 though Anderson played against slightly better offensive competition.  In 2009-10 Vokoun had a much better goals against than Clemmensen (0.621 vs 1.058) but played against weaker competition as well (OppGF20 of .714 vs 0.743 for Clemmensen’s opponents).  Generally speaking Tomas Vokoun had a very weak 2007-08 season but much better 2008-09 and 2009-10 seasons even though he always seemed to play against weaker offensive opponents.

In terms of my Hockey Analysis Ratings, Tomas Vokoun ranked 16th out of 35 goalies in 2007-10 HARD and 18th in 2007-10 HARD+ rankings.  Middle of the pack.  The seasonal breakdown positioned him 35th of 38 in HARD+ for goalies with 500+ minutes in 2007-08, 19th of 35 in 2008-09, and 6th of 37 in 2009-10.  So far this season he is closer to the bottom again.

Is Tomas Vokoun an elite goalie, or even great goalie?  Probably not.  He just posts good save percentages because his team gives up a lot of shots, but not necessarily quality scoring chances, and he plays against weak offensive competition.