Jan 282013
 

I have updated stats.hockeyanalysis.com to include 2012-13 data (even though it is way too early to draw any real conclusions from it) and also to add several new features:

  1. Added team data.
  2. Added QoT and QoC (offense, defense and overall) stats based on Hockey Analysis HARO (offense), HARD (defense) and HART (overall) ratings. These QoT and QoC are essentially the average teammate or opponent HARO, HARD or HART rating.
  3. Changed WOWY pages so that both goal and corsi data are on the same page for easy comparison.
  4. Included individual stats in the WOWY pages so we can see how many goals Perry scored with Getzlaf on the ice with him.
  5. Also included each player in their on “with you” table so we can see that players overall individual stats for easy comparison with how he performed with his line mates (i.e. Perry scored 18 5v5 goals last year, 15 with Getzlaf on ice with him)
  6. The WOWY Against table is now split into two, one for opposition forwards and one for opposition defense and as a result have removed goalies from the list.
  7. I have merged 5v5up1 and 5v5up2+ situations into 5v5leading and 5v5down1 and 5v5down2+ into 5v5trailing.  Needed to do this to make my program more efficient and I didn’t think the distinction was all that important/useful compared to some of the other stuff.

There may be a few other changes that I don’t recall making but that should be the most important ones. Have a look around and if you see any issues or have any other features you’d like to see be sure to let me know and I’ll see what I can do.

 

 

Jan 252013
 

The last few days I have been looking at the percentage of a teams ice time for a given situation that a particular player is on the ice for.  So for instance, what percentage of the Leafs 5v5 even strength ice time was Joffrey Lupul on the ice in games in which Joffrey Lupul played. When I write a new program to calculate these numbers I need to to some testing to make sure the results are correct.  The first test is always the standard sniff test.  When the program runs I look at the output and ask myself “does the output make sense?”. When I first looked at the output the other day one of the numbers surprised me so much that I had to do some double checking to make sure it made sense. That number was the percentage of his teams power play ice time that Ilya Kovalchuk was on the ice for. That number was 87.25%.

That’s insane I thought so off to NHL.com to check and see if it could be at all possible. I first checked and noticed that the Devils had 439:59 minutes of PP ice time last year, including 420:36 minutes of 5v4 ice time. Next I checked out much PP ice time Kovalchuk had last year and see that he had 379:08 minutes of PP time. I do not know his exact 5v4 PP ice time numbers but 379:08 is about 86% of 439:59 so my calculation of Kovalchuk being on the ice for 87.25% of his teams PP ice time is perfectly within reason.

To me this seems like a crazy high number.  It means for every 2 minute penalty Kovalchuk is on the ice for 1:44 of it. That just makes me say “WOW!” but Kovalchuk is not alone in getting big PP minutes.  Here are some other players who have played in >70% of his teams 5v5 PP minutes (in games he played in) over the past 5 seasons.

Player 5v4 TOI%
Ilya Kovalchuk 87.25%
Alex Ovechkin 83.08%
Mike Green 76.86%
Mark Streit 75.35%
Sergei Gonchar 74.76%
Evgeni Malkin 73.83%
Sidney Crosby 73.01%
Dan Boyle 72.78%

I knew some players played a lot of PP ice time, but that still astonishes me. Oh, and for the record, in addition to being on the ice for 87.25% of his teams 5v4 PP ice time, Kovalchuk was on the ice for 89.66% of his teams 5v4 PP goals.

On the other end of things, over the last 5 years Willie Mitchell has played a whopping 59.2% of his teams 4v5 PK ice time which is might actually be more impressive considering how much more demanding playing on the PK is.

 

Jan 242013
 

The other day I introduced a new way of visualizing player time on ice and usage and today I am taking that one step further by superimposing a players performance on those charts.

So, with the TOI usage charts I presented the other day you can see how frequently a player was on the ice in any particular situation relative to how frequently the team plays during that situation.  So, a player might be on the ice for 30% of the teams 5v5 game tied minutes.  The next logical step is to take a look at his production during those situations relative to his teams production. If a player is on the ice for 30% of his teams 5v5 game tied minutes but he was only on the ice for 25% of the teams 5v5 game tied goals, that isn’t a good thing.  The team under-produced during his ice time relative to when he was not on the ice. We can also do the same for goals against and the resulting chart might look like this one for Zdeno Chara over the past 5 seasons.

The blue is Chara’s TOI usage percentages, the green is his goals for percentages and the red is his goals against percentages. You will notice that I have removed special teams play. The reason for this is because GA is not significant on power plays and GF is not significant on penalty kill so the chart ends up looking odd but in theory you could include them.

In an ideal situation the red box is smaller than the blue box (give up fewer goals than expected) and the green box is bigger than the blue box (give up more goals than expected). For Chara his results are a little mixed. When trailing he is very good having more goals for than expected and fewer goals against than expected when he is on the ice. His goals against relative to his teammates rises significantly when leading. I am not certain why, but maybe it has to do with his defense pairings when protecting a lead or opposing teams pressure him more when they are trailing.

Let’s take a look at another player who has been in the news lately, for both a contract signing and an injury.  Joffrey Lupul.

Strangely, almost the opposite of Chara. Lupul’s ‘leading’ stats are better than Chara’s while Chara is better when trailing. I am thinking maybe matchups are a factor here. When leading coaches are more diligent in matching Chara up against the opposing teams top line and keeping Lupul away from the opposing teams top line. Something to investigate further.

That said though, for Leaf fans if the Leafs get a better team that spends more time leading than trailing, Lupul’s numbers should, at least according to the chart above, get better. Especially goals against numbers.

Let’s finish off with one more superstar player, Sidney Crosby.

That is the chart of an offensively dominant player. Crosby’s offense is through the roof. Like Chara though, he is much weaker protecting a lead than any other situation.

As I said in my previous post, I am not sure where I will go with these radar charts, but they seem to be a valuable way of visualizing data so when appropriate I will attempt to make use of them. For example, it might be interesting to take a look at how a players usage and performance changes from year to year. In particular it might be interesting to see how ice time and performance changes for young players as they slowly improve or older players who are on the downsides of their careers.

 

Jan 232013
 

One of the challenges in hockey analytics, or any type of data analysis, is how to best visualize data in a way that is exceptionally informative and yet really simple to understand. I have been working on a few things can came up with something that I think might be a useful tool to understand how a player gets utilized by his coach.

Let’s start with some background. We can get an idea of how a player is utilized by looking at when the player gets used and how frequently he gets used.  Offensive players get more ice time on the power play and more ice time when their team is trailing and needs a goal. Defensive players get more ice time on the PK and when they are protecting a lead. This all makes sense, but the issue is some teams spend more time on the PP or PK than others while bad teams end up trailing more than good teams and leading less. This means doing a straight time on ice comparison between players on different teams doesn’t always accurately depict the usage of the player. If a player on the Red Wings plays the same number of minutes with the lead as a player on the Blue Jackets it doesn’t mean the players are used int he same way.  The Blue Jackets will lead a game significantly less than the Red Wings thus in the hypothetical example above the Blue Jackets are depending on their player a higher percent of the time with a lead than the Red Wings are their player.

To get around this I looked at percentages. If Player A played 500 minutes with a lead and his team played a total of 2000 minutes with a lead during games which Player A played, then Players A’s ice time with a lead percentage would be 25%. In games in which Player A played he was used in 25% of the teams time leading. I can calculated these percentages for any situation from 5v5 to 4v5 or 5v4 special teams to leading and trailing situations. The challenge is to visualize the data in a clear and understandable way. To do this I use radar charts. Lets look at a couple examples so you get an idea and we’ll use players that have extreme and opposite usages: Daniel Sedin and Manny Malhotra.

For those not up to speed on my terminology f10 is zone start adjusted ice time which ignores the 10 seconds after a face off in either the offensive or defensive zone.

The charts above are largely driven by PP and PK ice time but players that are used more often in offensive roles will have their charts bulge to the top and top right while those in more defensive roles will have their charts bulge more to the bottom and bottom left. Also, the larger the ‘polygon’ the more ice time and more relied on the player is. In the examples above, Sedin is clearly used more often in offensive situations and clearly gets more ice time.

Let’s now look at a player who is used in a more balanced way, Zdeno Chara.

That is a chart that is representative of a big ice time player who plays in all situations. We can then take it a step further and compare players such as the following.

In normal 5v5 situations Gardiner was depended on about as much as Phaneuf, but Phaneuf was relied on a lot more on special teams and a bit more when protecting a lead. Of course, you can also compare across teams with these charts:

Phaneuf and Chara were depended on almost equally in all situations except on the PP where Phaneuf was used far more frequently.

I am not sure where I will go with these charts but I think I’ll look at them from time to time as I am sure they will be of use in certain situations and I have a few ideas as to how to expand on them to make them even more interesting/useful.

 

Jan 202013
 

The Leafs announced today that they have re-signed Joffrey Lupul to a 5 year contract extension at an average salary and cap hit of $5.25M/yr.  Some Leaf fans are a little dismayed at both the value and the term of the deal as many people seem to view Lupul as a second line winger with a defensive liability that should have been traded, not re-signed.  I won’t deny that Lupul is a defensive liability (though wingers generally have less impact on defense than centers or defensemen), but I will dispute the claim that he is a second line winger.

Last season I wrote an article pointing out that Lupul’s production was not an anomaly and that he has always been that good of a player. In it I showed that he made almost all of his line mates more productive offensively when they were skating with him than when they were not.  I also showed that Lupul’s even strength goal production had not increased dramatically last year.  I won’t reiterate that here as you can go read it if you want, but I just wanted to post one more chart.  This chart shows the top 20 players in terms of goal scoring rates (individual goals per 20 minutes of ice time) during 5v5 zone start adjusted play over the last 5 years (minimum 3000 minutes of ice time).

Rank Player G/20
1 SIDNEY CROSBY 0.272
2 ALEXANDER SEMIN 0.259
3 STEVEN STAMKOS 0.240
4 MARIAN GABORIK 0.234
5 ALEX OVECHKIN 0.234
6 BOBBY RYAN 0.210
7 RICK NASH 0.209
8 ILYA KOVALCHUK 0.207
9 JEFF CARTER 0.201
10 PATRICK SHARP 0.200
11 ALEX BURROWS 0.197
12 PHIL KESSEL 0.196
13 JOFFREY LUPUL 0.196
14 JONATHAN TOEWS 0.196
15 DANIEL SEDIN 0.196
16 JAROME IGINLA 0.195
17 JAMES NEAL 0.195
18 MATT MOULSON 0.195
19 MARIAN HOSSA 0.193
20 EVGENI MALKIN 0.191

Lupul sits right there in 13th spot right behind Kessel and just ahead of guys like Toews, D. Sedin, Neal, Hossa and Malkin. That’s not too shabby if you ask me and certainly worthy of a $5.25M/yr deal if you ask me. The reason for Lupul’s perceived performance increase last year is largely due to more ice time, and more PP ice time in particular, and not because of luck or a one year wonder type thing.

Update: Edited to indicate the chart uses 5 years of data, not just last season.

 

 

Jan 172013
 

Earlier today I wrote a post about Tim Connolly and his offensive production at even strength. Shortly after posting that I thought a similar article comparing the performances of Bozak, Kadri and Conolly would be an interesting piece since they are sort of competing for roster spots (more so Kadri and Connolly than Bozak though). Of course, in the mean time Connolly has been put on waivers so to some extent he isn’t relevant anymore but I am including him for interest sake.

Here is a look at their individual offensive performances for the last 2 seasons for Bozak and Kadri and last year for Connolly (since he wasn’t with the Leafs in 2010-11).

Bozak:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 1121:53 14 20 34 33:00
2010-11 1190:08 8 12 20 59:30
Combined 2311:01 22 32 54 42:48

Kadri:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 263:24 4 2 6 44:04
2010-11 382:22 3 7 10 38:14
Combined 645:46 7 9 16 40:22

Connolly:

Season ESTOI Goals Assists Points TOI/Pt
2011-12 940:12 11 20 31 30:20

What is interesting is of the three, Connolly had the best TOI/Pt last year, even better than Bozak who benefited from playing primarily with Kessel and Lupul. Kadri’s most frequent line mates were Lombardi, MacArthur and Connolly while Connolly’s played with almost everyone but had the most minutes with Crabb, Kessel, Lupul, Lombardi and MacArthur (between 180 and 260 with all of them). It seems Connolly was far from the least productive Leaf forward at even strength.

That said, it seems irrelevant now what Connolly has done so more important is to look at Bozak vs Kadri. Overall they have had similar point rates over past 2 seasons but Bozak was much better last year. If all that came from playing with Kessel and Lupul then maybe Kadri is at least equally good.  And when you factor in that Bozak is a downright terrible defensive player I’d almost certainly give Kadri ice time over Bozak.

To put the above stats into perspective, here are Grabovski’s over the past 2 seasons.

Season ESTOI Goals Assists Points TOI/Pt
2011-12 1126:42 18 23 41 27:29
2010-11 1232:33 19 24 43 28:40
Combined 2359:15 37 47 84 28:05

Clearly Grabovski has produced much more at even strength than any of the other three and pretty consistent too. To put Grabovski into perspective though, Malkin had an even strength point every 16:37 last season. That’s domination.

 

Jan 172013
 

Yesterday evening James Mirtle from the Globe and Mail posted an article on The Curious case of Tim Connolly and the Leafs.  It’s worth a read so go read it but the premise of the article is how the narrative around Tim Connolly in training camp is he had a poor year last year and he needs to perform better this year.  Makes sense from most peoples view points but Connolly tries to present a different perspective.

Connolly can be prickly to deal with and wasn’t particularly interested in talking about last season, but when pressed, you could tell he felt he did more of value than the narrative – that he’s been an unmitigated bust in Toronto – would suggest.

Here was his answer when asked (maybe for the second or third time) about needing to “rebound” this season.

“Even strength, I think I had my second highest career points last year,” Connolly said. “I’d like to improve my play on the power play and maybe play a bigger role. Penalty killing, I think, my individual percentage was 89 per cent I read somewhere. I was able to lead the forwards in blocked shots.”

He makes two points in there.  The first is that he had his second highest even strength points last year and the second was something about individual percentage was 89 percent. Lets deal with the first one first by looking at his even strength points since the first lockout.

Season Goals Assists Points
2011-12 11 20 31
2010-11 7 16 23
2009-10 9 27 36
2008-09 12 16 28
2007-08 3 20 23
2005-06 9 20 29

(Note: Connolly only played 2 games in 2006-07 so I have omitted it from the table and discussion)

Tim Connolly is actually correct.  His best even strength point total came in 2009-10 when he had 36 points followed by his 31 even strength points last year.  But let’s take a look at those point totals relative to even strength ice time.

Season ESTOI Points TOI/Pt
2011-12 940:12 31 30:20
2010-11 840:31 23 36:33
2009-10 966:41 36 26:51
2008-09 631:26 28 22:33
2007-08 603:18 23 26:14
2005-06 708:47 29 24:26

The last column is time on ice per point, or time on ice between points.  Last year he was on the ice for an average of 30 minutes and 20 seconds between each of his even strength points. This was his second worst since the locked out season. So, while Connolly was technically correct in saying that he had his second highest even strength point total last season, it was a somewhat misleading representation of his performance.

Now for the individual PK percent. It generated a bit of twitter conversation last night questioning what it actually is.

One might think it is the penalty kill percentage when he was on the ice but that seems like a strange thing to calculate.  Is it goals per 2 minutes of PK time?  Is it goals per PK he spent any amount of time killing?  I really didn’t know so I dug into the numbers deeper by looking at the Leafs PK percentages on my stats site and noticed that Connolly had the best on-ice save percentage (listed as lowest opposition shooting percentage) of any Leaf last season during 4v5 play and that save percentage while he was on the ice was just shy of 89% (88.68%). It seems that maybe what Connolly meant to say was that he had an on-ice PK save percentage of 89%.

How good is an 89% save percentage on the PK?  Well, of the 100 forwards with at least 100 4v5 minutes of ice time last year, Connolly ranks 42nd in the league so league wide it isn’t that impressive but considering the Leafs weak goaltending it might actually be fairly good.

Here is the thing though. Single season PK save percentage is so fraught with sample size issues that it is next to useless as a stat for goalies let alone forwards.

One could evaluate Connolly based on PK goals against rate in which he came up 3rd on the Leafs (trailing Lombardi or Kulemin) but that is still fraught with sample size issues. More fairly we probably should evaluate Connolly’s PK contribution based on shots against rate or maybe even more fairly fenwick or corsi against rates. In each of those categories he ranked 5th among Leafs with at least 50 minutes of 4v5 ice time with only Joey Crabb being worse. Furthermore, among the 110 players with 100 minutes of 4v5 PK ice time last year, Connolly ranked 99th in fenwick against rate.

I don’t mean for this article to be a Connolly bashing article. I actually do think Connolly was a little misused and would probably do better with a more well defined role and not bounced around in the line up so much so in that sense I agree with the premise of what Connolly is saying. With that said though, it probably is fair to say that he didn’t have a great season and if he wants a regular role in the top six with time on the PP and PK he needs to perform better as his use of stats to attempt to show he had a good season is really just evidence to how statistics can be misused to support almost any narrative you want.  As they say, there are lies, damn lies, and then there are statistics.

 

Jan 102013
 

The news that shocked the hockey world yesterday had nothing to do with the CBA or Bettman or Fehr but rather that the Maple Leafs ownership group decided to make a strangely timed move to remove Brian Burke from his President and General Manager position of the Maple Leafs.  I think it is only fair to take a look back at the Burke years and evaluate where the Leafs are after his 4 years at the helm.  Let’s look at the Leafs position by position starting with the good and heading downhill from there.

Defense

Burke made some mistakes on defense (Komisarek, maybe Liles contract and to a lesser extent Beauchemin) but generally speaking defense is the Leafs strong point.  Phaneuf and Gunnarsson really developed into a quality top pairing last year capable of playing big minutes in any situation.  Jake Gardiner still has lots to learn but has shown flashes of brilliance, particularly as a puck moving offensive defenseman.  Cody Franson hasn’t been given much of an opportunity in Toronto but there is certainly a decent amount of potential there and at the very least trade value.  Morgan Rielly is the Leafs best prospect and has a chance to be a quality NHL defenseman in the not to distant future.  Beyond those guys there are some decent depth prospects close to ready like Korbinian Holzer and Jesse Blacker and second tier prospects a year or two away like Stuart Percey and Matt Finn.  Even more veteran players like Mike Kostka and reclamation project Paul Ranger provide some nice depth.  There is certainly a need for the organization to add another quality shut down defenseman but overall there are a number of quality defensemen on the active roster with good depth in the organization and a number of quality prospects on the way.

Wingers

At the NHL level Burke has left a nice stable of quality wingers with guys Kessel, Lupul, van Riemsdyk, MacArthur, Kulemin and an emerging player like Matt Frattin.  Generally speaking that is a pretty good set of wingers for your top 3 lines and there is also a decent group of role players to fill out the fourth line and depth winger positions.  Unlike the defense position, there are not an abundance of quality winger prospects that project to top 2 line duty.  There are some prospects like Tyler Biggs, Brad Ross, Greg McKegg, Jerry D’Amigo, Carter Ashton, etc. but they all have significant question marks and in the cases of D’Amigo, Ashton and McKegg poor seasons with the Marlies this year have dropped their status from maybe prospects to not players we can seriously count on.  Luckily Burke has done a decent job at putting together some quality wingers who are mostly young or in their NHL primes because there isn’t a lot of top talent in the pipe line.

Centers

Now we get to Burke’s failures.  Although not someone Burke brought in, Grabovski has really grown during Burke’s tenure and has proven himself to be at least a very good second line center if not a second tier first line guy.  But beyond Grabovski the center position is somewhat of a disaster.  There are some decent bottom of the line up guys like Steckel and McClement but Burke has failed miserably in finding a center to complement Grabovski on the top 2 lines.  Bozak has some skills but is not the guy for the job, maybe in part because he was never properly developed for the job but rather was just thrown to the wolves.  Tim Connolly was expected to be a short term fix but so far that has failed miserably.  Long term there was hope for Nazem Kadri and while there is still reason for some hope (he is having a decent year with the Marlies) management seemed to have more interest in publicly criticizing Kadri (from everything from his fitness level, to his attitude, to his defensive ability) than properly developing him.  The other great hope at center was Joe Colborne who was picked up from Boston in the Kaberle trade.  At the time I didn’t know much about Colborne but when I looked at his numbers I was underwhelmed but lots of people thought he had a ton of potential so I kept an optimistic view of him.  But two years later and he is struggling big time with the Marlies and his status as a prospect center for the top 2 lines is all but gone.  The only hope for Colborne now is he can learn to play defense and become a big, strong, defensive third line center not unlike what Manny Malhotra has done with his career but that is probably being too optimistic.  And beyond Kadri and Colborne there is very little in terms of center prospects.  This is an area that desperately needs attention at both the NHL and at the prospect level.

Goaltending

So the score card so far is the defense situation is good all round, the winger situation is good at the NHL level, a little weak at the prospect level and the center situation needs a fair bit of work at both the NHL level and especially at the prospect level.  That leaves the goaltending situation which is a complete and utter mess.  The current Leaf goaltending situation has the Leafs with James Reimer as their starter who is really only on anyone’s radar because he had a stellar second half of a season with the Leafs in 2010-11.  If it weren’t for that stretch nobody would have any hope for him because for the several years prior to that he wasn’t even a full time starting goalie at either the AHL or ECHL (hadn’t played more than 30 games in a year since 2006-07 in WHL).  After Reimer there are second (or third) tier prospects like Ben Scrivens, Jussi Rynnas, Mark Owuya and Garret Sparks.  Scrivens is having another solid year (not quite as good as last year though) with the Marlies and might be close to at least being a back up at the NHL level but predicting goalies development at the NHL level is extremely difficult.  In the end the Maple Leaf goalie situation can best be described as one big question mark with a grand total of 81 NHL games started experience in the entire organization.  The goaltending situation was a disaster before Burke got here, was a disaster when he was here, and is still a disaster.  Easily the absolute worst and uncertain goalie situation of any NHL franchise.

 

Nov 082012
 

Eric T. over at NHL Numbers had a post last week summarizing the current state of our statistical knowledge with respect to accounting for zone start differences.  If you haven’t read it definitely go read it because it is not only a good read but because it concludes that how the majority of people have been doing is is wrong.

Overall, no two estimates are in direct agreement, but the analyses that are known to derive from looking directly at the outcomes immediately following a faceoff converge in the range of 0.25 to 0.4 Corsi shots per faceoff — one-third to one-half of the figure in widespread use. It is very likely that we have been overestimating the importance of faceoffs; they still represent a significant correction on shot differential, but perhaps not as large as has been previously assumed.

In the article Eric refers to my observation that eliminating the 10 seconds after a zone start effectively removes any effect that the zone start had on the game.  From there he combined my zone start adjusted data found at stats.hockeyanalysis.com with zone start data from behindthenet.ca and came up with an estimate that a zone start is worth 0.35 corsi.  He did this by subtracting the 10 second zone start adjusted corsi from standard 5v5 corsi and then running a regression against the extra offensive zone starts the player had.  In the comments I discussed some further analysis I did on this using my own data (i.e. not the stuff on behindthenet.ca) and came up with similar, though slightly different, numbers.  In any event I figured the content of that comment was worthy of its own post here.

So, when I did the correlation between extra offensive zone starts and difference between 5v5 and 5v5 10 second zone start adjusted corsi I got the following (using all players with >1000 minutes of ice time over last 5 seasons):

My calculations come up with a slope of 0.3043 which is a little below that of Eric’s calculations but since I don’t know the exact methodology he used that might explain the difference (i.e. not sure if Eric used complete 5 years of data, or individual seasons).

What is interesting is that when I explored things further, I noticed that the results varied across positions, but varied very little across talent levels.  Here are some more correlations for different positions and ice time restrictions.

Position Slope r^2
All Players >1000 min. 0.30 0.55
Skaters >1000 min. 0.28 0.52
Forwards >1000 min. 0.26 0.50
Defensemen >1000 min. 0.33 0.57
Goalies >1000 min. 0.44 0.73
Forwards >500 min. 0.26 0.50
Forwards >2500 min. 0.26 0.52
Forwards 500-2500 min. 0.26 0.39

Two observations:

1.  The slope for forwards is less than the slope for defensemen which is (quite a bit) less than the slope for goalies.

2.  There is no variation in slope no matter what restrictions we put on a forwards ice time.

There isn’t really much to say regarding the second observation except that it is nice to see consistency but the first observation is quite interesting.  Goalies, who have no impact on corsi, see the greatest zone start influences on corsi of any position.  It is a little odd but I think it addresses one of the concerns that Eric had pointed out in his article:

The next step would be to remove the last vestige of sampling bias from our analysis. The approaches that focus on the period immediately after the faceoff reduce the impact of teams’ tendency to use their best forwards in the offensive zone, but certainly do not remove it altogether.

I think that is exactly what we are witnessing here, but maybe more importantly teams put out their best defensive players and, maybe more importantly, their best face off guys for defensive zone face offs. If David Steckel, who is an excellent face off guy, is getting all the defensive zone face offs, it is naturally going to suppress the corsi events immediately after the defensive zone face off because he is going to win the draw more often than not.  There is probably more line matching done for the zone face offs than during regular play so the line matching suppresses some of the zone start impact.  It is more difficult to line match when changing lines on the fly so a good coach can more easily get favourable line matches. The result is normal 5v5 play offensive players might see a boost to their corsi (because they can exploit good matchups) and during offensive zone face offs they see their corsi suppressed because they will almost always be facing good defensive players and top face off guys.  Thus, the boost to corsi based on a zone start is not as extreme as should be for offensive players.  The opposite is true for defensive players.

Defensemen are less often line matched so we see their corsi boost due to an offensive zone face off a little higher than that of forwards, but it isn’t near as high as goalies because there are defensemen that are primarily used in offensive situations and others that are primarily used in defensive situations.

Goalies though, tell us the real effect because they are always on the ice and they are not subject to any line matching.  In the table above you will notice that goalies have a significantly higher slope and an impressively high r^2.  I feel I have to post the chart of the correlation because it really is a nice chart to look at.

I have looked at a lot of correlations and charts in hockey stats but very few of them are as nice with as high a correlation as the chart above.

I believe that this is telling us that an offensive zone start is worth 0.44 corsi, but only when a player is playing against similarly defensively capable players as he would during regular 5v5 play which I speculate above is not necessarily (or likely) the case.  The 0.44 adjustment really only applies to an idealistic situation that doesn’t normally occur for any players other than goalies.  So where does that leave us?  Should we use a zone start adjustment of 0.44 corsi for all players, or should we use something like 0.33 for defensemen and 0.26 for forwards?  The answer isn’t so simple.  One could argue that we should apply 0.44 to all players and then make some sort of QoC adjustment and that would make some sense.  But if we are not intending to apply a QoC adjustment, does that mean we should use 0.33 and 0.26?  Maybe, but that is a little inconsistent because it would mean you are using a QoC adjustment only for the zone start adjustment of a players stats, and not for all his stats.  The answer for me is what I have been doing the past little while and not even attempt to adjust a players stats based on zone starts differences and rather simply just ignore the the portion of play that is subject to being influenced by zone starts – the 10 seconds after a zone start face off.  To me it seems like the simplest and easiest thing to do.

 

Oct 302012
 

Offensive players generally get all of the attention but defensive players are often just as valuable to a team.  Ask any NHL fan who the top offensive centers in the league are and they will quickly ramble off a few names from Crosby to Stamkos to Getzlaf to Malkin, etc.  Ask a fan to list the top defensive centers and the task becomes a little more difficult.  So, I decided to look into defensive centers a little further.

What makes a valuable defensive center?  Well, they should play against tough competition, they should give up fewer goals than expected, and they should be trusted to play a lot on the penalty kill.  So, with that in mind, I decided to set the following parameters in my defensive center search.

1.  I limited myself to players who have played >2000 minutes of 5v5 zone start adjusted ice time over the past three seasons.

2.  I only considered players who had an average opposition goals for per 20 minutes of ice time above 0.800 (i.e. only consider players who played against tough offensive opponents, must have OppGF20>0.800).

3. I then eliminated all forwards with a goals against per 20 minutes of ice time >0.800 (i.e. eliminate players who didn’t get good defensive results, must have GA20<0.800).

4.  I then took each players on ice goals against rate and divided it by his line mates goals against rate to ensure that they are performing better than their line mates and make their line mates better defensively (GA20/TMGA20 < 1.00).

5.  I then eliminated any players who didn’t have >300 minutes of 4v5 PK ice time over the past 3 seasons.

After doing this I got the following list of players sorted by GA20/TMGA20, or in English  sorted by how much better defensively they were than their line mates.

  1. Brandon Sutter
  2. Samuel Pahlsson
  3. Mikko Koivu
  4. Frans Nielsen
  5. Travis Zajac
  6. Martin Hanzal
  7. Mike Richards
  8. Brooks Laich
  9. Jordan Staal
  10. Joe Pavelski

Honorable Mentions:  Logan Couture, Pavel Datsyuk, Mikhail Grabovski and Alexander Steen missed the cut due to not having enough PK minutes.  Couture would have been slotted second behind Sutter, Datsyuk between Pahlsson and Koivu, and Grabovski and Steen immediately after Hanzal.  Plekanec, Kopitar, Bergeron and Legwand met the PK ice time criteria and would come in after Pavelski except that their line mates had a better GA20 when not playing with them so they were cut from the list.

All in all I am pretty happy with the defensive forward list above.  They all make sense and the only real surprise on the list might be Frans Nielsen but that is mostly because I don’t pay attention to he Islanders (who does really?) and this haven’t really paid much attention to him.  For a player on the lowly Islanders to meet these criteria it probably means he is a pretty good defensive player.

It is interesting to see Sutter and Jordan Staal both make this list as they were traded for each other this past summer.  When I compared these two players after the trade when down I suggested that Sutter is one of the best defensive forwards in the NHL and this certainly backs that up.

What do you think?  Am I missing someone from this list of elite defensive centers?