Jul 042014
 

The other day I put up a post on Mike Weaver’s and Bryce’s Salvador’s possible ability to boost their goalies save percentage and I followed it up with a post on the Maple Leafs defensemen where we saw Phaneuf, Gunnarsson, Gleason and Gardiner all seemingly able to do so as well while Robidas had the reverse effect (lowering goalie save percentage). This got some fight back from the analytics community suggesting this is not possible. My question to them is, why not?

Their answer is that if you do year over year analysis of a players on-ice save percentage or a year over year analysis of a players on-ice save percentage relative to their teams you will find almost no correlation. While this is true I claim that this is not sufficient to prove that such a talent does not exist. Here is why.

We Know Players Can and Do Impact Save %

The most compelling argument that players can and do impact save % is that we see it happening all the time and it is fully accepted among the hockey analytics community. It is known as score effects. Score effects are a well entrenched concept in hockey analytics.  It is why we often look at 5v5 “close” or 5v5 tied statistics instead of just 5v5 statistics. Generally speaking, the impact score effects have is that the trailing teams usually experiences an increase in shot rate along with a decrease in shooting percentage while the team protecting the lead experiences a decrease in shot rate but an increase in shooting percentage. The following table shows the Boston Bruins shooting and save percentages when tied, leading and trailing over the past 7 seasons combined.

. Tied Leading Trailing
Shooting% 7.27% 9.14% 7.66%
Save% 93.36% 93.86% 92.53%

The difference in the Bruins save percentage between leading and trailing is 1.33%. This is the difference between a .923 save percentage goalie and a .910 save percentage goalie which is the difference between an elite goalie and a below average goalie. That is not insignificant. Is this the goalies fault or does it have something to do with the players in front of him? The latter seems most likely. It makes sense that when protecting the lead the players take fewer risks in an attempt to generate offense and in return give up fewer good scoring chances against albeit maybe more chances in total. Conversely, the team playing catch up take more offensive risks so they end up giving up more quality scoring chances against. This is reflected in their teams save and shooting percentages when leading and trailing.

So, now if a team can play a style that boosts the team save percentage when they are protecting a lead, why is it so inconceivable that a player could see the same impact in his on-ice save percentage if that player plays that style of hockey all the time? If Mike Weaver and Bryce Salvador play the same style all the time that teams play when protecting a lead, why can they not boost on-ice save percentage? There is no reason they can’t.

It is Difficult to Detect because Individual Players Don’t Have a lot of Control of Outcomes

The average player’s individual ability to influence of what happens on the ice is actually fairly small as there are also 9 other skaters and 2 goalies on the ice with him. At best you can say the average player has a ~10% impact on outcomes while he is on the ice. That isn’t much. Last week James Mirtle tweeted a link to Connor Brown’s hockeydb.com page as evidence why +/- is a useless statistic. Over the course of three OHL season’s Brown’s +/- went from -72 to -11 to +44. I suggested to Mirtle that if this is the criteria for tossing out stats we can toss out a lot of stats including corsi% because most stats are highly team/linemate dependent. When challenged that this dramatic of reversal is not seen in corsi% I cited David Clarkson as an example.  In 2012-13 Clarkson was 4th in CF% but in 2013-14 he was 33rd (of 346) in CF%. From one year to the next he went from 4th best to 14th worst. Why is this? WEll, Clarkson essentially moved from playing with good corsi players on a good corsi team to playing with bad corsi players on a bad corsi team. No matter how much puck possession talent Clarkson has (or hasn’t) his talent doesn’t dominate over the talent level of the 4 team mates he is on the ice with.

Now think about how many players change teams from one year to the next and think about how many players get moved up and down a line up and change line mates from one season to next. It is not an insignificant number. TSN’s UFA tracker currently has 109 UFA’s getting signed starting July 1st, the majority of them changing teams. There are only ~800 NHL players (regulars and depth players) in a season so that is pretty significant turnover. Some teams turn over a quarter to half their line up while others stay largely the same. With that much roster turnover and with so little ability for a single player to drive outcomes it should be expected that the majority of statistics see relatively high “regression”. Regression doesn’t mean lack of individual talent though.

Think of this scenario. We have a player with an average ability to boost on-ice save percentage and he has been playing on a team with a number of players who are good at boosting on-ice save percentage but generally speaking he doesn’t play with those players. Under this scenario it will appear that the player is poor at boosting on-ice save percentage because he is being compared to  players who are good at it. Now that player moves to another team who isn’t very good at boosting on-ice save percentage. Now that same average player will look like he is a good player because he has a better on-ice shooting percentage than his teammates. The result is little year over year consistency but that doesn’t mean there aren’t talent differences among players.

Hockey is not like baseball which is a series of one-on-one matchups between pitcher and batter or isolated attempts to make a fielding play on a hit ball. Outcomes in hockey are completely interdependent on up to 12 other players on the ice. QoT is the largest driver of a players statistics in hockey. Only when we factor out QoT completely can we truly be able to identify every players talent level for any metric we measure. This is a kind of like the chicken and an egg problem though because to identify a players talent level we need to know the talent level of their team mates which in turn required knowledge of his own talent level. We can’t just look at year over year regression to isolate talent level.

Comments

The “team” aspect in hockey is more significant than any other sport and any particular players statistics are largely driven by the quality of his team mates. Even more than teammates, style of play can be a significant factor in a players statistics. The quality of the players that a particular player plays with is a function of both the team the plays on and the role (offensive first line vs defensive third line) he is playing on the team and this is maybe the greatest driver of a players statistics. This is why David Clarkson can be a Corsi king in New Jersey and a Corsi dud in Toronto. It also accounts for why James Neal can be a 25 goal guy playing on the first line in Dallas to a 40 goal guy in Pittsburgh (and probably back to a 25 guy guy in Nashville next year).  This also accounts for why year over year correlation in many stats is not very good despite there being measurable differences in the talent that that stat is measuring. Significant statistical regression is not sufficient, in my opinion, to conclude insignificant controllable talent if no significant attempt to completely isolate individual contribution to team results has been successfully made.

Just for fun, here is a chart of Lidstrom’s on-ice save percentage vs team save percentage. It is pretty outstanding that an offensive defenseman can do this too.

LidstromOnOffSavePct

 

Apr 172012
 

Last week I took a look at the Leafs forwards, today I’ll take a look at their defense and goaltending.  As with the forwards, I’ll evaluate the defensemen using their 5v5 zone start adjusted HARO+, HARD+ and HART+ ratings but with goalies I will evaluate them using their 5v5 zone start adjusted HARD+ rating and save percentage.  I have included the past 5 individual seasons as well as the most recent 3 year rating and 5 year rating.  Personally, I like to use 3 year ratings as the best guide for player value as it gives a large sample size but not too large that other factors come into play for most players (i.e. aging and natural career progression).

Dion Phaneuf

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 0.975 1.083 1.023 1.355 1.125 0.996 0.955
HARD+ 0.866 1.069 0.941 0.741 0.972 0.969 0.928
HART+ 0.920 1.076 0.982 1.048 1.049 0.983 0.942

Phaneuf had a bit of an off year this year, particularly in the second half.  Both his offensive HARO+ and defensive HARD+ ratings are down from previous seasons.  Generally speaking over the years Phaneuf has been a good offensive player and more of an average defensive player over the several years.  He does seem to contribute quite well on the powerplay so if I had to define his role, he’d be an ideal #2/#3 defenseman on a good team who is relied upon heavily on the powerplay.  It is my opinion, he is not a top #1 defenseman and most good teams in the NHL have at least one defenseman better than Phaneuf, often significantly better.  Unfortunately this means he is significantly over paid at $6.5M/yr and his actual worth is probably more in the $5M/yr range.

John-Michael Liles

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 0.884 1.035 1.011 0.909 1.145 0.957 0.848
HARD+ 0.848 0.860 1.090 0.847 0.952 0.909 0.916
HART+ 0.866 0.948 1.051 0.878 1.049 0.933 0.882

Liles offensive numbers really took a dip this year as he never really got his game back on track after returning from injury.  Before he suffered  his concussion he had 21 points in 34 games but after his return he had just 6 points in 32 games.  Taking that into account, Liles is an above average offensive player but an average to below average defensive player.  He is good on the powerplay but unlike Phaneuf not quite as reliable in defensive situations.  Liles is probably a #3/#4/#5 defenseman depending on the makeup of the team.

Luke Schenn

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 1.165 1.083 1.204 0.924 1.117 1.121 0.940
HARD+ 0.713 0.878 0.912 0.792 0.808 0.844 0.837
HART+ 0.939 0.981 1.058 0.858 0.963 0.982 0.888

I have been extremely critical of Schenn’s defensive game over the years, but surprisingly he has been very solid offensively in 5v5 situations.  If you compare Schenn’s 5v5 point totals to Phaneuf’s and adjust for ice time they are awfully close.  Unfortunately Schenn’s defensive game is dreadful and it took a step back this season.  Of the 161 defensemen with 2000 5v5 zone start adjusted minutes over the past 3 seasons, Schenn ranks 151st.  Schenn is a perfect example of a young defenseman who was rushed to the NHL and asked to play under a coach that isn’t known for defensive structure and his development suffered.  I really hope that Randy Carlyle who is much more of a defensive structured coach than Ron Wilson can turn Schenn’s defensive game around because if he can Schenn could provide the Leafs with a lot of value as a #3/#4 defenseman for many years to come.  If Schenn can’t improve his defensive game he offers very value going forward.

Carl Gunnarsson

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 0.967 1.004 1.221 1.007
HARD+ 0.989 0.856 0.969 0.952
HART+ 0.978 0.93 1.095 0.980

Gunnarsson is one of those defensemen who quietly goes about his business and gets the job done.  He is a perfect low maintenance top 4 defenseman who can generate offense when needed but can also be used in more shutdown situations when needed as well.  He and Phaneuf played quite well together for much of the season in both offensive and defensive roles.

Mike Komisarek

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 0.900 0.965 0.800 1.069 1.109 0.876 0.850
HARD+ 0.676 0.743 1.022 0.945 0.923 0.788 0.875
HART+ 0.788 0.854 0.911 1.007 1.016 0.832 0.862

A lot has been written about the fall off of Mike Komisarek’s game so there isn’t a whole lot more to add.  His defensive numbers over the past couple seasons have been dreadful.  Unlike Schenn, I am not even sure if we can hope he will turn his game around under a more defensive structured coach.

Cody Franson

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 1.074 1.057 1.299 1.094
HARD+ 0.917 1.391 1.625 1.264
HART+ 0.996 1.224 1.462 1.179

It was a bit of an unfortunate season for Cody Franson as he went from a regular role in Nashville to bouncing in and out of the lineup with the Leafs.  In Nashville he was paired mostly with more defensive minded and physical Shane O’Brien and his defensive numbers were extremely good, albeit against somewhat weak competition.  When he came to Toronto I wanted to see what he could do if given a more significant role, particularly defensive role feeling he had been typecast as an offensive specialist.  Unfortunately he was never given that opportunity as when he was in the line up he was paired with Liles or Gardiner.  It should be noted though, that he did make them both better defensively.  When Liles was not with Franson his GA20 was 1.116 but with Franson it was 0.639.  When Gardiner was not with Franson his GA20 was 1.056, when with Franson it was 0.782.    His .917 HARD+ was second best on the Leafs (to Gunnarsson) and I think he deserves to considered an option in more defensive situations.

Jake Gardiner

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARO+ 1.277
HARD+ 0.809
HART+ 1.043

Jake Gardiner had an outstanding rookie season impressing everyone with his offensive skills anchored by strong skating and puck handling abilities.  As with most rookies his defensive game still needs growth, but if he continues to develop his offensive game he has the potential to be an elite offensive defenseman.  On the season he had 30 points in 75 games but had 21 of those 30 points in the 40 games after January.  Although it is just half a season, those are impressive point totals for a rookie defenseman.  His future is extremely promising as an offensive defenseman.

On the whole, the Leafs have a pretty good set of defensemen and you can argue that Phaneuf, Gunnarsson, Liles, Gardiner, and Franson can all be top 4 defensemen on good teams and Schenn has that potential if he can improve the defensive side of his game.  Unfortunately as a group the mix is all wrong.  There is no true #1 defenseman, there is no true defensive shut down pairing, and there are far too many one-dimensional offensive defenseman and a number of them are over paid for their contribution.  There is a lot of youth, but not a lot of veteran leadership (or coaching) to provide defensive guidance to these young players.  Furthermore Schenn was needlessly rushed to the NHL and not given proper instruction and I feel Franson has been unfairly typecast as a uni-dimensional player and thus have not gotten optimal return for his talents.  They need to jettison the contract of Komisarek one way or another (trade unlikely so buyout a possibility).  If they can’t develop Schenn into a shut down defenseman, they need to ship him out and find someone who can fill that role.  It would be nice if they could get better value out of the $6.5M they are paying Phaneuf but I don’t know how they accomplish that.  Related to Phaneuf, they really do need an elite #1 defenseman but with their salary cap restraints I don’t see how you do that this off season though I think there is reason to hope/believe that maybe Gardiner can (may) develop into that role (or at least into a poor mans Scott Niedermayer or Brian Rafalski type) so maybe it is worth waiting and seeing.

Now on to the goaltending.

Jonas Gustavsson

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARD+ 0.808 0.803 0.903 0.850
Save% 89.2 89.7 90.4 89.8

Gustavsson wasn’t very good as a rookie, and has gotten worse since.  At this point there is very little reason to believe he will ever be a reliable starter in the NHL and you even have to question whether he can be a reliable backup except behind elite starters where he is only relied on to play in 20 games.  He won’t be back with the Leafs.

James Reimer

2011-12 2010-11 2009-10 2008-09 2007-08 2009-12 (3yr) 2007-12 (5yr)
HARD+ 0.898 1.035 0.991
Save% 90.6 92.7 91.7

It was a bad season for Reimer, getting injured early and maybe never really fully getting back on track.  I don’t think this season is enough reason to give up hope on him becoming a quality starting NHL goalie but I am a long way from suggesting he is the “Real Deal” as Brian Burke did in his year end press conference.

The Leafs need to add a quality experienced and reliable veteran goalie to support Reimer in his development.  It’s unfortunate they didn’t figure this out last season because they could have signed a guy like Jose Theodore who had a very good season in Florida and would have been a perfect fit for the Leafs.  This off season Vokoun could be an option but he is getting up there in age and has shown signs of slowing down the past couple seasons.  Josh Harding is a little younger and has been a quality backup for several years in Minnesota but has not proven he can handle starters duties (never had more than 34 games played) if needed so there is a level of risk with him.  Otherwise you are looking at second tier starters aged 35 and up or career backups, none of which are very appealing to me so they may have to go the trade route but I have no clue who might be available.

 

 

Feb 092012
 

It has been shown on numerous occasions that players can influence their own teams on-ice shooting percentage be that through their talents or their style of play.  An example is the PDO vs Luck article I posted the other day.  In that article there is a table that clearly shows that shooting percentage varies across players and that players who are given more ice time (presumably because they are better players) have higher shooting percentages.  The same was not true for on-ice save percentage though.  On-ice save percentages were not ‘stratified’ according to ice time. That study looked at forwards and I have since looked at defensemen and have also attempted to see if organizing players according to defensive zone starts percentages would allow for ‘stratification’ of on-ice shooting percentages but to no avail.  But I am stubborn and didn’t give up.

The next thing I chose to do is compare a players on-ice save percentage with the weighted average of the save percentages of all the goalies the player played with.  The weighted average is based on the number of shots against the goalie and the player were on the ice together for.  So, lets say for example Player A was on the ice for 100 shots against, 30 of those shots were when he was on the ice with Goalie A and 70 were when he was on the ice with Goalie B.  When Goalie A is not playing with Player A his save percentage is 91%.  When Goalie B is not playing with Player A his save percentage is 92%.  The weighted average of the two goalies is (91% * 30 + 92% * 70) /100 or 91.7%.  I then compare that goalie save percentage 91.7% to the players on-ice save percentage by dividing the players save percentage by the goalies save percentage.  So, for example, if Player A’s on-ice save percentage is 92% then I calculate 92% divided by 91.7% to get 100.33.  Any numbers above 100 indicate the player improved his goalies save percentage and any numbers below 100 indicate the player hurt the goalies save percentage.

In order to get an indication of whether the player could produce that much of an improvement due solely to luck I employed a binomial distribution estimation of the likelihood that the player would have an on-ice save percentage greater than the one he posted considering the goalies he played in front of.  The results of all of this are below.  Forwards first followed by defensemen and top 25 and bottom 25 for both.  The data I used was 4 year 2007-11 5v5 zone start adjusted data and only using players with 1250 shots against.

Forward Sv% Infl. Chance > Forward Sv% Infl. Chance >
TAYLOR PYATT 101.94% 0.54% MATT STAJAN 98.87% 90.41%
MANNY MALHOTRA 101.95% 1.00% DEREK ROY 98.98% 90.50%
ZACH PARISE 101.86% 1.08% DAVID BACKES 98.90% 90.86%
JEFF CARTER 101.61% 1.32% SAM GAGNER 98.83% 91.74%
LEE STEMPNIAK 101.70% 1.34% HENRIK ZETTERBERG 98.77% 92.60%
JORDAN STAAL 101.50% 2.45% SIDNEY CROSBY 98.83% 92.92%
TEEMU SELANNE 101.51% 2.95% SHANE DOAN 98.98% 93.52%
TRAVIS MOEN 101.30% 3.59% PATRICK KANE 98.76% 93.64%
CORY STILLMAN 101.34% 3.62% DAINIUS ZUBRUS 98.67% 93.73%
RADIM VRBATA 101.34% 4.54% RICK NASH 98.77% 94.30%
TRAVIS ZAJAC 101.22% 5.22% MARTIN HAVLAT 98.64% 94.72%
BRIAN GIONTA 101.11% 6.15% MARTIN ERAT 98.75% 95.04%
SAMUEL PAHLSSON 101.22% 6.30% DAVID BOOTH 98.61% 95.77%
RADEK DVORAK 101.08% 6.99% PAUL STASTNY 98.44% 96.62%
VALTTERI FILPPULA 101.28% 7.14% ANDREW LADD 98.42% 96.99%
JASON POMINVILLE 101.01% 7.72% MARK RECCHI 98.54% 97.07%
WOJTEK WOLSKI 101.07% 8.24% EVGENI MALKIN 98.48% 97.67%
MIKE KNUBLE 101.03% 8.40% ALEXANDER FROLOV 98.16% 97.93%
MARC SAVARD 101.05% 9.02% RYAN KESLER 98.29% 98.12%
CHRIS THORBURN 101.07% 10.39% THOMAS VANEK 98.41% 98.39%
CHRIS DRURY 100.98% 11.55% TODD WHITE 98.05% 98.45%
MICHAEL RYDER 100.88% 11.62% CHRIS KELLY 98.02% 98.63%
RENE BOURQUE 100.98% 11.81% KRISTIAN HUSELIUS 97.85% 99.39%
NICKLAS BACKSTROM 100.87% 12.22% BRANDON DUBINSKY 97.51% 99.89%
MIKKO KOIVU 100.84% 12.65% ILYA KOVALCHUK 97.65% 99.96%

 

Defenseman Sv% Infl. Chance > Defenseman Sv% Infl. Chance >
KENT HUSKINS 102.22% 0.26% AARON WARD 99.21% 81.98%
NICKLAS LIDSTROM 102.09% 0.31% JORDAN LEOPOLD 99.23% 83.79%
ROB SCUDERI 101.78% 0.52% KEVIN BIEKSA 99.13% 84.68%
SEAN O’DONNELL 101.55% 1.26% JAROSLAV SPACEK 99.25% 84.75%
BRYCE SALVADOR 101.87% 1.28% NICK BOYNTON 99.14% 85.31%
SHANE O’BRIEN 101.63% 1.52% DAN BOYLE 99.19% 85.70%
MIKE WEAVER 101.61% 2.15% STEPHANE ROBIDAS 99.13% 87.86%
ROSTISLAV KLESLA 101.60% 3.15% SHEA WEBER 99.22% 87.88%
TREVOR DALEY 101.23% 3.16% JOHN-MICHAEL LILES 98.98% 89.07%
BRYAN MCCABE 101.25% 3.30% LUBOMIR VISNOVSKY 99.04% 90.41%
TIM GLEASON 101.20% 3.55% DENNIS WIDEMAN 99.11% 91.36%
ROB BLAKE 101.48% 3.86% MARK STREIT 98.79% 91.57%
MARC-EDOUARD VLASIC 101.22% 3.95% BRENT SEABROOK 98.94% 92.21%
PAUL MARTIN 101.37% 4.29% SHAONE MORRISONN 98.80% 92.52%
MIKE LUNDIN 101.51% 4.97% SCOTT NIEDERMAYER 98.82% 93.29%
ANDREJ MESZAROS 101.09% 5.88% ANDREJ SEKERA 98.71% 94.38%
NICK SCHULTZ 101.00% 5.96% FILIP KUBA 98.63% 94.44%
KEITH YANDLE 101.00% 6.79% MARTIN SKOULA 98.61% 95.27%
ANDREI MARKOV 101.07% 7.22% DUNCAN KEITH 98.80% 95.91%
MATT GREENE 101.14% 7.30% BARRET JACKMAN 98.73% 95.96%
ROMAN HAMRLIK 100.81% 9.44% DAN GIRARDI 98.69% 97.15%
TONI LYDMAN 100.83% 10.05% ZBYNEK MICHALEK 98.74% 97.37%
DUSTIN BYFUGLIEN 100.98% 10.12% FEDOR TYUTIN 98.53% 97.74%
JAN HEJDA 100.89% 10.14% DAN HAMHUIS 98.63% 97.87%
CHRIS PRONGER 100.89% 10.72% JACK JOHNSON 97.80% 99.95%

There were a total of 172 forwards and 141 defensemen in the study.  What is interesting is that there were 15 defensemen (10.6% of them) that had their binomial chance of posting their on-ice save percentage at 5% or lower when we would expect 7 by chance.  That means there were more than twice as many really really good on-ice save percentages for defensemen than we would expect by chance alone.

For forwards, there were just 10 who had their binomial chance at 5% or lower which equates to 5.81% so not far off of what we would expect.  We had 10 we expected 8.6.  There were 19 forwards with binomial chance <10% when we should expect 17 by chance.  Not a huge difference.  Conversely, there were 14 forwards with binomial chance >95% or 8.1% compared to the expected 8.6 players and there were 25 forwards above 90% when we should expect 17.

It seems the really good defenders are defensemen and the players most apt to hurt their goalies save percentage are forwards.

That was a pure numbers analysis, what if we looked at the players themselves.  Looking at the list of forwards with better than expected on-ice save percentages we see a lot of third line players that primarily play defensive roles (Pyatt, Malhotra, Moen, Pahlsson, Drury, Staal, etc.).  The bottom 25 forwards contain a lot of more offensive oriented players (Kovalchuk, Huselius, Vanek, Frolov, Malkin, Recchi, Stastny, Booth, Havlat, Nash, Kane, Crosby, Roy, etc.).  There is actually only a 0.04% chance (one in 2500) that Kovakchuk’s on-ice save percentage was due to luck alone.

Much the same can be said for the defensemen.  The defensemen that are  best at improving on-ice save percentage are often defensemen we consider to be defensive defensemen (Huskins, Scuderi, O’Donnell, Salvadore, Weaver, Vlasic, Martin, etc.) or elite 2-way defensemen (Lidstrom, Blake, Yandle, Pronger, etc.) and the ones at the bottom of the list are more offensive specialists (J. Johnson, Keith, Kuba, Sekera, Wideman, Liles, Visnovsky, Boyle, Streit, etc.).  Yes, this is more evidence that Jack Johnson is a horrific defensive defenseman.

All things considered, there does seem to be some order in the list and order is the enemy of luck and the binomial analysis indicates that there may be more going on than one would expect purely from luck.  It seems that players can, to some degree, influence on-ice shooting percentage.  We can’t credit, or blame, the goalies all the time.

Continue reading »

Jun 072011
 

As it stands right now the Leafs have six NHL experienced defensemen under contract and another three who are restricted free agents.  Assuming all three of the RFA’s get re-signed it leaves the Leafs with 7 defensemen, five of which will be regulars (Phaneuf, Schenn, Gunnarsson, Aulie and Komisarek) and two that are more along the lines of depth defensemen (Lebda and Lashoff).  Phaneuf and Schenn are the top two guys (though they may not end up playing together) and depending on where you see Gunnarsson and Aulie fitting into the mix the Leafs will be looking for a #3, #4 or #5 type guy.  Depending on how much they end up spending on a first line center, it is probably safe to assume they could allocate anywhere between $2-4M and there are enough UFA defensemen available that they can probably acquire what they want via free agency rather than have to resort to a trade.  Let’s take a look at some of the potential UFA defensemen the Leafs could have interest in.

Definitely Too Expensive

Christian Ehrhoff – Ehrhoff is definitely the top potential UFA defenseman.  The Canucks will definitely want to bring him back and if he ever made it to UFA status I am certain the Red Wings will throw some or all of just-retired Rafalski’s money at him.  Ehrhoff is in line for a $6M paycheck and as much as I would like to see him in a Leaf uniform, he is probably out of the Leafs budget so lets take a look at some of the other free agent defensemen.

Probably too Expensive

Kevin Bieksa – Bieksa really had a breakthrough season this year, particularly in his own zone and he ended the season at +32, tops on the Canucks, and is a +9 in the playoffs, again tops on the Canucks.  His +32 in the regular season trailed only Chara’s +33 among defensemen but Bieksa was +32 in just 66 games.  Bieksa is probably a good 2-way second pairing defenseman but his excellent season might push his salary demands beyond what he deserves (unless this past season is the new norm for him which is unlikely) and out of the Leafs budget.

James Wisniewski – Wisniewski started his career with the Chicago Blackhawks and he just seemed like he was that typical #5/6 guy.  He was a decent enough player who did a number of things well but not necessarily a core guy, but when he was given an opportunity to play a more prominent role with the Ducks, and then with Islanders and Montreal his offensive numbers really jumped and he was a strong PP performer.  He’d probably really help the Leafs PP but there will be enough demand for his services that he’ll probably cost more than the Leafs can afford.

Joni Pitkanen – Pitkanen is one of those guys who had #1 potential but never really took the next step and instead has had a career that some might consider a disappointment because he never really reached his full potential.  Pitkanen is a better offensive guy than a defensive guy and would be a nice fit on the Leafs PP unit.  He earned $4M last season and is probably in line to earn about the same on his next contract which makes him probably out of the Leafs budget and I think he’ll be happier staying in a non-hockey market like Carolina.

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Sep 162010
 

On Monday I outlined an all-encompassing player evaluation model that allows us to evaluate every forward, defenseman and goalie under the same methodology.  In short, the system compares how many goals are scored for and against while a player is on the ice and compares it to how many goals scored for/against one should expect based on the quality of his line mates and opposition.  That model, I believe, makes a reasonable attempt at evaluating a players performance, but it can be improved.

The first method of improvement is to utilize the additional information we have about the quality of a players line mates and opposition once we have run the model.  Initially I use the goals for and against performance of his line mates and opposition when the player being evaluated is not on the ice at the same time as his line mates and opposition.  But now that we have run the model we, at least theoretically, have a better understanding of the quality of his team mates and opposition.  I can then take the output of the first model run and use it as the input of the second model run to get new and better results.  I can then continue doing this iteratively and the good news is that after every iteration the difference between the player rating from that iteration and the previous iteration trends towards zero which is a very nice result.

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