Thoughts on New Conference Format

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Dec 062011

In general, I really like the new conference format.  Well, really, I didn’t like the previous conference format so I am glad that is gone.  First off, let me mention a few things that I don’t like about the current setup.

1.  Reduces chances of rivalries forming/developing.  Essentially under the current system if you make the playoffs you could meet any one of 14 other teams in the first round.  Rivalries are primarily built through competing for playoff spots and meeting in the playoffs.  Under the current system you are far less likely to meet the same team in the playoffs in back to back years and you are less likely to meet your natural geographical rivals in the playoffs (Toronto vs Montreal, Anaheim vs Los Angeles, Pittsburgh vs Philadelphia, etc.).  This, in my opinion, is bad for the NHL.

2.  Unbalanced schedule.  The current schedule isn’t quite as unbalanced as a few years back but it is still unbalanced and that means two teams competing for the same playoff position do so by playing different schedules with different strengths of difficulty.  This has generally favoured teams in weaker divisions, particularly the generally very weak southeast division.  Just look at the standings right now.  Four of the top eight and five of the top 10 teams are north east division teams while just one of the top eight teams is from the southeast and three of the bottom five teams are from the southeast.  With an unbalanced schedule that sees teams play a heavier within-division schedule, all the teams in the southeast have a much easier schedule than the teams in the northeast and yet those teams are competing for the same playoff spots.

The new conference set up fixes both of these problems.  The mini-conference playoff structure means a greater chance of rivalries developing and geographical rivals meeting in the playoffs.  Also, while the schedule is still unbalanced between conferences you are only competing for a playoff spot with your conference rivals who all more or less play an equal schedule.

Some people have raised some concerns though.  First and foremost they don’t like that some conferences have 7 teams and some have 8 meaning some teams have a 4 in 7 chance of making the playoffs and other teams have a 4 in 8 chance.  There is some validity to this, but fear not, I am certain Bettman and the owners have a plan to address this in the upcoming years.  Expansion to 32 teams making each conference having 8 teams.  Or, we could dream and they contract to 28 teams, but that is unlikely.

The second concern people have is that a 5th place team in one conference will be better than a 4th place team in another conference but the 5th place team misses the playoffs and the 4th place team makes the playoffs.  This too is a valid concern, but is more or less equivalent to the unbalanced schedule issues I pointed out above not to mention the 9th place team in the west has generally been better than the 8th place (and sometimes 7th and 6th place) team in the east for the past several years.  Nothing is perfect.

To me, the greater development of rivalries far outweighs any negatives with the new system.  Rivalries are what can turn casual fans into enthusiastic fans and anything that can be done to enhance rivalries.  I thought going to the conference playoff system was a mistake so I am glad they fixed that.


Nov 222011

I hate to keep beating the “Shooting Percentage Matters” drum but it really dumbfounds me why so many people choose to ignore it, or believe it is only a small part of the game and not worth considering and instead focus their attention on corsi/fenwick, and corsi/fenwick derived stats as their primary evaluation too.

It dumbfounds me that people don’t think players have an ability to control shooting percentage yet we all seem to agree that shooting percentage is affected by game score.  Rob Vollman wrote the following in a comment thread at arctic ice hockey.

<blockqote>The score can affect the stats because teams behave differently when chasing or protecting a lead…</blockquote)

He isn’t specifically referring to shooting percentage, but shooting percentage varies based on game score and I think most people accept that.  So, while people freely accept that teams can play differently depending on score, they seemingly choose not to believe that players can play different depending on their role, or skillset.  Or rather, it isn’t that they don’t believe players can play differently (for example they realize there are defensive specialists) they just choose not to accept that a players style of play (in addition to their talents, which often dictates their style of play) will affect their stats, including shooting percentage.  An example, which I brought up at The Puck Stops Here is Marian Gaborik vs Chris Drury.  Both Gaborik and Drury played the past 2 seasons on the NY Rangers but Gaborik played an offensive role and Drury generally played a more defensive/3rd line role.  As a result, here are their offensive stats at 5v5 over the past 2 seasons.

Gaborik Drury Gaborik’s Edge
Team Fenwick For per 20min WOI 13.8 12.8 +8%
Team Sh% For WOI 10.26% 6.18% +66%
Team Goals For per 20 min WOI 1.031 .575 +79%

Shooting percentage took what was a slight edge for Gaborik in terms of offensive fenwick for and turned it into a huge advantage in goals for.  Part of that is Gaborik and his line mates better skill level and part of it is their aggressive offensive style of play, but regardless of why, we need to take shooting percentage into account or else we will undervalue Gaborik at the offensive end of the rink and over value Drury.

It isn’t just Gaborik and Drury whose offense is significantly impacted by shooting percentage.  It happens all the time.  I took a look at all players that had 2000 5v5 even strength on-ice offensive fenwick events over the past 4 seasons.  From there I calculated their expected on-ice goals scored based on their ice time using league-wide average  on-ice fenwick for per 20 minutes (FF20) and league-wide average fenwick shooting percentage (FSH%).

I next calculated an expected goals based on the league-wide FF20 and the players FSH% as well as an expected goals based on the players FF20 and the league-wide average FSH%.  When we compare these expected goals to the expected goals based solely on the league-wide average we can get an idea of whether a players on-ice goal production is driven mostly by FF20 or FSH% or some combination of the two.

The following players had their on-ice 5v5 goal production influenced the most positively or most negatively due to their on-ice 5v5 FSH%.

Player Name %Increase from FSH%
J.P. DUMONT 29.4%

And the following players had their on-ice 5v5 goal production influenced the most positively or most negatively due to their on-ice 5v5 FF20.

Player Name %Increase from FF20

Some interesting notes:

  1.  The range in the influence of FSH% is significantly larger than the range of influence of FF20 indicating that shooting percentage is more important than shot generation in terms of scoring goals.
  2. The FSH% list is not random.  The list is stratified.  Offensive players at the top, non-offensive players at the bottom (plus Scott Gomez who gets offensive minutes, but sucks).  What you see above is not luck.  There is order to the list, not randomness.
  3. Speaking of Gomez, he sucks at on-ice FSH%, but has a very good FF20, though that is partly due to offensive zone start bias.
  4. Ilya Kovalchuk is the anti-Gomez.  He has a great FSH%, but is horrible at helping his team generate shots.
  5. The standard deviation of the FSH% influence is 14.5% while it is 8.3% for FF20 influence so it seems FSH% has a much greater influence on scoring goals than FF20.  This is not inconsistent with some of my observations in the past or observations of others.

So, what does all this mean?  Shooting percentage matters, and matters a lot and thus drawing conclusions based solely on a corsi analysis is flawed.  It isn’t that generating shots and opportunities isn’t important, but that being great at it doesn’t mean you are a great player (Gomez) and being bad at it doesn’t make you a bad player (Kovalchuk).  For this reason I really cringe when I see people making conclusions about players based on a corsi analysis.  A corsi analysis will only tell you how good he is at one aspect of the game, but is not very good at telling you the players overall value to his team.  My goal is, and always will be, to try and evaluate a players overall value and this is why I really dislike corsi analysis.  It completely ignores a significant, maybe the most significant, aspect of the game.  Furthermore, I believe that offensive ability and defensive ability should be evaluated separately, which many who do corsi analysis don’t do or only partially or subjectively do.

I really don’t know how many different ways I can show that shooting percentage matters a lot but there are still a lot of people who believe players can’t drive or suppress shooting percentage or believe that shooting percentage is a small part of the game that is dwarfed by the randomness/luck associated with it (which is only true if sample size is not sufficiently large).  The fact is corsi analysis alone will never give you a reliable (enough to make multi-million contract offers) evaluation of a players overall ability and effectiveness.  Shooting percentage matters, and matters a lot.  Ignore at your peril.


Oct 272011

There has been a fair bit of discussion going on regarding shot quality the past few weeks among the hockey stats nuts.  It started with this article about defense independent goalie rating (DIGR) in the wall street journal and several others have chimed in on the discussion so it is my turn.

Gabe Desjardins has a post today talking about his hatred of shot quality and how it really isn’t a significant factor and is dominated by luck and randomness.  Now, generally speaking when others use the shot quality they are mostly talking about thinks like shot distance/location, shot type, whether it was on a rebound, etc.  because that is all data that is relatively easily available or easily calculated.  When I talk shot quality I mean the overall difficulty of the shot including factors that aren’t measurable such as the circumstances (i.e. 2 on 1, one timer on a cross ice pass, goalie getting screened, etc.).  Unfortunately my definition means that shot quality isn’t easily calculated but more on that later.

In Gabe’s hatred post he dismisses pretty much everything related to shot quality in one get to the point paragraph.


Alan’s initial observation – the likelihood of a shot going in vs a shooter’s distance from the net – is a good one.  As are adjustments for shot type and rebounds.  But it turned out there wasn’t much else there.  Why?  The indispensable JLikens explained why – he put an upper bound on what we could hope to learn from “shot quality” and showed that save percentage was dominated by luck.  The similarly indispensable Vic Ferrari coined the stat “PDO” – simply the sum of shooting percentage and save percentage – and showed that it was almost entirely luck.  Vic also showed that individual shooting percentage also regressed very heavily toward a player’s career averages.  An exhaustive search of players whose shooting percentage vastly exceeded their expected shooting percentage given where they shot from turned up one winner: Ilya Kovalchuk…Who proceeded to shoot horribly for the worst-shooting team in recent memory last season.

So, what Gabe is suggesting is that players have little or no ability to generate goals aside from their ability to generate shots.  Those who follow me know that I disagree.  The problem with a lot of shot quality and shooting percentage studies is that sample sizes aren’t sufficient to draw conclusions at a high confidence level.  Ilya Kovalchuk may be the only one that we can say is a better shooter than the average NHLer with a high degree of confidence, but it doesn’t mean he is the only one who is an above average shooter.  It’s just that we can’t say that about the others at a statistically significant degree of confidence.

Part of the problem is that goals are very rare events.  A 30 goal scorer is a pretty good player but 30 events is an extremely small sample size to draw any conclusions over.  Making matters worse, of the hundreds of players in the NHL only a small portion of them reach the 30 goal plateau.  The majority would be in the 10-30 goal range and I don’t care how you do your study, you won’t be able to say much of anything at a high confidence level about a 15 goal scorer.

The thing is though, just because you cannot say something at a high confidence level doesn’t mean it doesn’t exist.  What we need to do is find ways of increasing the sample size to increase our confidence levels.  One way I have done that is to use 4 years of day and instead of using individual shooting percentage I use on-ice shooting percentage (this is useful in identifying players who might be good passers and have the ability to improve their linemates shooting percentage).  Just take the list of forwards sorted by on-ice 5v5 shooting percentage over the past 4 seasons.  The top of that list is dominated by players we know to be good offensive players and the bottom of the list is dominated by third line defensive role players.  If shooting percentage were indeed random we would expect some Moen and Pahlsson types to be intermingled with the Sedin’s and Crosby’s, but generally speaking they are not.

A year ago Tom Awad did a series of posts at Hockey Prospectus on “What Makes Good Players Good.”  In the first post of that series he grouped forwards according to their even strength ice time.  Coaches are going to play the good players more than the not so good players so this seems like a pretty legitimate way of stratifying the players.  Tom came up with four tiers with the first tier of players being identified as the good players.  The first tier of players contained 83 players.  It will be much easier to draw conclusions at a high confidence level about a group of 83 players than we can about single players.  Tom’s conclusions are the following:

The unmistakable conclusions from this table? Outshooting, out-qualitying and out-finishing all contribute to why Good Players dominate their opponents. Shot Quality only represents a small fraction of this advantage; outshooting and outfinishing are the largest contributors to good players’ +/-. This means that judging players uniquely by Corsi or Delta will be flawed: some good players are good puck controllers but poor finishers (Ryan Clowe, Scott Gomez), while others are good finishers but poor puck controllers (Ilya Kovalchuk, Nathan Horton). Needless to say, some will excel at both (Alexander Ovechkin, Daniel Sedin, Corey Perry). This is not to bash Corsi and Delta: puck possession remains a fundamental skill for winning hockey games. It’s just not the only skill.

In that paragraph “shot quality” and “out-qualitying” is used to reference a shot quality model that incorporates things like shot location, out-finishing is essentially shooting percentage, and outshooting is self-explanatory.  Tom’s conclusion is that the ability to generate shots from more difficult locations is a minor factor in being a better player but both being able to take more shots and being able to capitalize on those shots is of far greater importance.

In the final table in his post he identifies the variation in +/- due to the three factors.  This is a very telling table because it tells it gives us an indication of how much each factors into scoring goals.  The following is the difference in +/- between the top tier of players and the bottom tier of players:

  • +/- due to Finishing:  0.42
  • +/- due to shot quality:  0.08
  • +/- due to out shooting:  0.30

In percentages, finishing ability accounted for 52.5% of the difference, out shooting 37.5% of the difference and shot quality 10% of the difference.  Just because we can’t identify individual player shooting ability at a high confidence level doesn’t mean it doesn’t exist.

If we use the above as a guide, it is fair to suggest that scoring goals is ~40% shot generation and ~60% the ability to capitalize on those shots (either through shot location or better shooting percentages from those locations).  Shooting percentage matters and matters a lot.  It’s just a talent that is difficult to identify.

A while back I showed that goal rates are better than corsi rates in evaluating players.  In that study I showed that with just 1 season of data goal for rates will predict future goal for rates just as good as fenwick for rates can predict future goal for rates and with 2 years of data goal for rates significantly surpass fenwick for rates in terms of predictability.  I also showed that defensively, fenwick against rates are very poor predictors of future goal against rates (to the point of uselessness) while goals against rates were far better predictors of future goal against rates, even at the single season level.

The Conclusion:  There simply is no reliable way of evaluating a player statistically at even a marginally high confidence level using just a single year of data.  Our choices are either performing a Corsi analysis and doing a good job at predicting 40% of the game or performing a goal based analysis and doing a poor job at predicting 100% of the game.  Either way we end up with a fairly unreliable player evaluation.  Using more data won’t improve a corsi based analysis because sample sizes aren’t the problem, but using more data can significantly improve a goal based analysis.  This is why I cringe when I see people performing a corsi based evaluation of players.  It’s just not, and never will be, a good way of evaluating players.


Follow up on recent conversations…

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Oct 132011

Earlier this week I participated in several conversations elsewhere and thought I’d post a followup to a few of them.

“The Bet”

The other day I posted an article about on ice shooting percentage being a talent and referenced a discussion I had with Gabe Desjardin’s of Arctic Ice Hockey at  As part of that discussion we were negotiating a “bet” of sorts.  In it I suggested that as a group Crosby, Gaborik, Ryan, St. Louis, H. Sedin, Toews, Heatley, Tanguay, Datsyuk, Horton would have an average on-ice shooting percentage over 10% this season while Gabe suggested that the group of them would regress significantly to the mean.  The problem was, we couldn’t agree on what the mean was that we were regressing to.

I suggested we use 7.94% which is league wide-shooting percentage (all goals divided by all shots at 5v5 over the last 4 seasons) but Gabe suggested we use only players who were on the ice for 1000 shots at 5v5 over the last 4 seasons which resulted in a much higher 9.12%.  He suggested we need to do this because we don’t want to include players who are “sub-replacement” level.  This seems odd to me because he believes that variation in shooting percentage is largely random so using that theory the sub-replacement level player is just as likely to have an elevated shooting percentage than a below average one.

So, in the end, we never seemed to agree on what mean on-ice shooting percentage to use but I wanted to make it known that I still do believe players can drive shooting percentage and thus corsi derived player analysis will never tell us the whole story.  So, with that said I’ll predict the 10 players above will end the season with an on-ice 5v5 even strength shooting percentage above 10% and no more than 2 will have an on-ice shooting percentage below 9.5%.  So, come seasons end lets look back and see what happened and hopefully my prediction will come true and we can all get past the “shooting percentage is random” nonsense.

“Mike Weaver”

Over at Pension Plan Puppets we had another Mike Weaver discussion.  In it I compared how goalies performed when Weaver was on the ice vs when Weaver was not on the ice.  Let me expand on that here.

Year with Weaver without Weaver
Vokoun 2010-11 2.14 2.79
Clemmensen 2010-11 1.70 2.63
Mason 2009-10 1.44 2.58
Conklin 2009-10 1.85 2.72
Mason 2008-09 1.68 2.01
Legace 2008-09 2.38 3.15
Luongo 2007-08 1.45 2.14

The table shows the goals against average of each goalie that Weaver has played at least 200 minutes in front of in each of the past 4 seasons along with the goalies goals against average when Weaver is on the ice in front of them and when Weaver is not on the ice in front of them.  As you can see, in every instance the goalie has had a significantly better goals against average when Weaver is on the ice than when he is not.  This should put to rest some of the “but Weaver has played in front of excellent goalies” argument that came out of my previous Mike Weaver is an excellent defensive defenseman post.

Separating Goalie Performance from Defensemen

As a followup to the Mike Weaver discussion @so_Truculent asked me how I separate out goaltender play in my goal based analysis.  i.e. how do you account for a player who plays against, on average worse goalies, or a player who plays in front of sub-par goalies.  How I do it is a somewhat complex process but it is exactly the same as how I account for quality of competition and quality of teammates for forwards and defensemen.  Let me describe the process as simply as I can.

I assign every player an offensive and defensive rating based on their GF20 (goals for while on ice per 20 minutes) and GA20 (goals against while on ice per 20 minutes) stats.  Now, lets use an example.  Let’s assume we are evaluating Mike Weaver defensively.  I will then calculate an estimated GA20 based on an average GA20 of all the players who played with Mike Weaver (weighted by ice time played with Mike Weaver) and based on the average GF20 of all the players who Mike Weaver played against (weighted by ice time played against Mike Weaver).  This gives me an estimated GA20 based on the players Mike Weaver plays with and against.  I then assign Mike Weaver a new defensive rating equivalent to Mike Weavers estimated GA20 divided by his actual GA20.  So, if Mike Weaver gives up fewer goals against than the average of his teammates and opposition, then Weaver gets a better than average rating and if he gives up more then he gets a below average rating.

I do this for every player in the league for both offensively and defensively.  This included forwards, defensemen and goaltenders (defensively only, I don’t factor goalies into offensive ratings).  That’s the beauty of this system.  It works for all players the same way.  If a goalie make his teammates GA20 ratings better and the oppositions GF20 ratings worse then the goalie gets a better than average rating.

Now, after we do this once we have improved information about the players offensive and defensive abilities and new offensive and defensive ratings.  So, I take these new numbers and run them through the process again.  And again.  And again.  Eventually it comes to a stable state where each iteration has very little change in the players ratings.  At this point we have the HARO+ and HARD+ ratings you see at  The HART+ ratings are just an average of each players HARO+ and HARD+ ratings.  Players with a HARO+ rating above 1.00 are above average offensively (though the median is something below 1.00) and players with a HARD+ rating above 1.00 are above average defensively (again, the median is something below 1.00 though).

I don’t know for sure if these are perfect ratings, but generally speaking I am very happy with the results, and I am very happy that the iterative process converges on a ratings solution as opposed to going completely haywire as some iterative processes do.  I also really like the fact that I can include goalies in the process because separating goalie performance from the players in front of them is probably one of the hardest questions to answer in hockey, especially if you are like me and believe that players can suppress shooting percentage (meaning save % is a team stat as much as it is a goalie stat).


On-ice Shooting Percentage as a Talent

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Oct 102011

There has been an interesting discussion of on-ice shooting percentage at Tyler Dellow’s  I have argued that we need to look at on-ice shooting percentage as a talent, and not something that just happens randomly while others have largely dismissed it.  One person in particular is Gabe Desjardin’s who has a followup post on his blog largely dismissing its importance.

In his blog post Gabe first discusses Gaborik’s value just considering his on-ice shooting percentage.

So are these totals 75% skill then?  Let’s do a quick check on how many goals that skill would be worth: 1000 on-ice shots/season * 2.5% above mean * 75% = 18.75 goals above average.  Double that to get to an approximate replacement level of 37.5 goals or just over six wins.  The current price for one win on the free agent market is roughly $3M, so we’d estimate Gaborik’s offensive value at more than $18M.

Gabe doesn’t believe any one player could be worth $18M based just on shooting percentage so he tries to shoot a hole in that by looking at 2yr vs 2yr regression.

Needless to say, anytime you come up with a metric that says a player should get paid $18M, you have to go back and check your math.  I did that by splitting the last four years into two two year periods (2007-08/2008-09 vs 2009-10/2010-11) and comparing on-ice shooting percentage among players who had 1000+ on-ice shots in each period.  I found that player on-ice shooting regressed 80% to the mean from the first set to the second, which puts Gaborik’s apparent talent closer to $5M.

Ok, so we have Gaborik’s value down to $5M.  That still seems pretty large to me but Gabe dismisses that further by suggesting some of that $5M has to be attributed to his linemates, arena bias, and strangely, a players opponents (particularly at home).

This is a key point, of course, and one that may not come through when we talk about team-level effects or try to figure out the value of individual top six forwards: when a #1 line plays against a #4 line, their shooting percentage goes up relative to when they’re playing power-vs-power.

Here is the thing.  The whole reason I participate in these debates is to suggest that players do in fact have the ability to drive or suppress shooting percentage and thus we must consider shooting percentage, in addition to corsi, when evaluating players.  So, it amazes me when I am debating someone that is trying to minimize the ability to drive/suppress shooting percentage that they bring up such observations that a players shooting percentage will go up when they are playing weaker players (i.e. the fourth line) who I presume can’t suppress shooting percentage as well as the stronger players.   There is clearly something wrong with the logic there.  Players don’t have the ability to suppress shooting percentage, but fourth liners are worse at suppressing shooting percentage than first liners??

The other thing I want to discuss is Gabe’s calculation that shooting percentage regresses 80% to the mean based on his 2yr vs 2yr calculation.  I won’t dispute his math because it is probably true, but I will suggest that I don’t believe that a league-wide observation can be applied to individual players.  There are a number of factors that influence a players on-ice shooting percentage.

1.  The quality of his linemates.

2.  The quality of opposition.

3.  Style of play (i.e.  aggressive offensive game vs defensive style).

4.  Score effects

Team building generally revolves around a small number of players.  Pittsburgh has Crosby, Malkin and Staal as their core forwards, everyone else is pretty much interchangeable.  Pretty much every team is like this.  A lot of those interchangeable parts move from team to team or even line to line on the same team or get asked to play different roles on the same team.  An injury to Kunitz and Pascal Dupuis gets bumped from the third line to playing with Crosby.  For these mostly interchangeable parts there can be a lot of variation in who they play with, the team they play on, and who they play against, and the roles they are asked to play.  All these factors are at play when Gabe calculates his 80% regression to the mean.  The good players who have well-defined roles don’t see near the same variation in their on-ice shooting percentages.  Look at the Crosby’s and Gaborik’s.  They are consistently at the top of the list.  Look at the Moen’s and Marchant’s and Pahlsson’s, they are consistently near the bottom of the list.  The players we perceive as good offensive players are at the top of the list.  The players we perceive as weak offensive players, or defensive minded players, are at the bottom of the list.  That’s a talent that we must consider.



Oct 052011

The Leafs traded for David Steckel yesterday and while this is by no means a significant trade my first reaction to it was a very positive one.  A fourth round pick is almost worthless and Steckel is a more than useful quality defensive third/fourth line guy who can kill penalties, something the Leafs desperately need.  Upon further review of the stats, I still like the trade because of it’s low risk but my thoughts on Steckel are a little more mixed than I first believed.

The Good

On the surface, Steckel looks like a premiere defensive forward.  Over the past 4 years, Steckel has the 9thth lowest on ice goals against per 20 minutes of the 250 forwards with 1500 5v5close minutes of ice time and he has been consistently very good at keeping the puck out of his own net at even strength.  His four year HARD+ rating is 1.152 and his HARD+ ratings for the past 4 seasons are 1.112, 1.262, 1,102 and 1.094.  All of these things point to Steckel being a good, or maybe very good, defensive forward.

The Bad

Throwing a damper on everything I just said, his quality of competition is quite weak.  His OppGF20 (opposition goals for per 20 minutes) ranks 227th of 250 and surprisingly he has over the past 4 seasons had slightly more offensive zone starts than defensive zone starts.  Now this isn’t all bad.  His opponents on average scored at a rate of 0.766 goals per 20 minutes of 5v5close ice time while Steckel and his teammates held them to 0.499 goals per 20 minutes but I would have more confidence in his defensive numbers if he was playing against top level opponents.

The Ugly

One of the key roles the Leafs likely acquired Steckel for is to provide some desperately needed help to their woeful penalty kill.  The problem is, Steckel’s PK numbers are quite woeful as well.  Of the 63 forwards with 500 4v5 PK minutes over the past 4 seasons, Steckel ranks in 48th in goals against per 20 minutes though he is a much better, but still average, 28th in fenwick against per 20 minutes.  Furthermore, the quality of his opponents on the PK hasn’t been all that great either as he ranked 57th of 63 in OppGF20 and 60th of 63 in OppFenF20.  Add it all up and it is quite likely that Steckel has been a below (maybe well below) average PK guy over the past four seasons.  That isn’t good news for the Leafs PK in 2011-12.

The Skinny

Although the numbers cast some doubts as to whether Steckel will live up to my initial reaction when I heard the trade, I still like the trade because it is a low risk trade and adds some defensive minded depth and size to the Leafs lineup.  I’ll take a wait and see attitude with regards to Steckel being a quality addition to the Leafs penalty kill unit but at the very least he’ll be a quality addition to the fourth line.  A fourth line that includes Steckel along side Mike Brown and Colton Orr could at the very least be a physically intimidating energy line that hopefully is more than responsible defensively and that isn’t all bad.


Sep 272011

Yesterday I posted my 100% statistics based predictions for the eastern conference, today here are the predictions for the western conference.  See the eastern conference predictions for more details on how these predictions are calculated but generally speaking I use my player rating system and combine them with my estimates for ice time for every player to come up with a predicted goals for and against for every team.  I haven’t converted the goal differentials to won-loss records because I actually think looking at the predicted goals for and against and goal differential provides better insight into the strengths and weaknesses of each team.

Predicted Last Season
Chicago 235.6 205.0 30.6 252 220 32
Vancouver 238.7 213.0 25.8 258 180 78
San Jose 228.3 208.2 20.1 243 208 35
St. Louis 233.3 217.9 15.4 236 228 8
Calgary 234.1 223.2 11.0 241 230 11
Detroit 241.7 233.9 7.8 257 237 20
Los Angeles 216.6 211.5 5.0 209 196 13
Nashville 214.0 210.3 3.7 213 190 23
Anaheim 237.2 234.7 2.5 235 233 2
Dallas 221.7 221.6 0.1 222 226 -4
Phoenix 210.5 217.9 -7.4 226 220 6
Minnesota 210.7 230.1 -19.4 203 228 -25
Columbus 216.9 239.4 -22.5 210 250 -40
Colorado 205.4 239.5 -34.1 221 287 -66
Edmonton 204.9 252.0 -47.1 191 260 -69

As I mentioned in the eastern conference predictions, while I think the above standings seem for the most part reasonable I think there will be more spread in the goals for column.  The top offensive teams will probably end up scoring 20+ goals more than is predicted above.  Last season Vancouver had 258 goals to lead the conference and that was a low total for a conference leader.  The prior 2 seasons the leader had 268 and 289 goals scored.

As far as surprises go, seeing St. Louis fourth and Calgary fifth were definitely surprises but then Calgary’s goal differential is predicted to be the same as last season and the Blues goal differential only rises moderately from +8 to +15.4 based mostly by reducing the goals against.  These teams weren’t that far from making the playoffs either so while a little surprising on the surface, might not be all that unreasonable of a prediction.  Los Angeles being predicted to score only 5 more goals than they give up is a surprise too.

Team GF Team GA
Detroit 241.7 Chicago 205.0
Vancouver 238.7 San Jose 208.2
Anaheim 237.2 Nashville 210.3
Chicago 235.6 Los Angeles 211.5
Calgary 234.1 Vancouver 213.0
St. Louis 233.3 St. Louis 217.9
San Jose 228.3 Phoenix 217.9
Dallas 221.7 Dallas 221.6
Columbus 216.9 Calgary 223.2
Los Angeles 216.6 Minnesota 230.1
Nashville 214.0 Detroit 233.9
Minnesota 210.7 Anaheim 234.7
Phoenix 210.5 Columbus 239.4
Colorado 205.4 Colorado 239.5
Edmonton 204.9 Edmonton 252.0

It looks like it could be another tough year for fans in Edmonton and Colorado as they are predicted to be the bottom 2 teams in goals scored as well as be the bottom 2 teams in goals allowed.  I am sure the fans in Washington are smiling since they have Colorado’s first round pick which they acquired in the Varlamov trade.  Based on the predictions above, I’d say there is a more than decent chance it is a top 5 pick overall.

The final interesting thing is that these predictions predict the eastern conference to have a better goal differential than the western conference.  This is a change from recent seasons when the west has generally been the better conference.  Not sure if this will become reality or not but it is worth watching.  There were a number of quality players that moved from the west to the east this summer (Brad Richards, Ilya Bryzgalov, Brian Campbell, Christian Ehrhoff, Robyn Regehr, Tomas Fleishmann, Scottie Upshall, Steve Sullivan, Matthew Lombardi, Joel Ward, etc.) which probably weren’t fully offset by the players going west (Carter, Richards, Wisniewski, etc.).  Whether the shift is enough to make the east as good or better than the west we’ll have to wait and see.


Sep 262011

A week or two ago I presented a prediction of the eastern conference using a purely statistics based analysis.  There were a number of limitations with the process which I outlined at the beginning of the post but I have fixed some of those so this is version 2.0 of the prediction algorithm.  Let me summarize the process.

  1.  I took each teams current rosters and estimated the amount of even strength, power play and shorthanded ice time each player on the roster would play.  For veteran players, the estimates were loosely based on previous years ice time which should give us a pretty accurate number for the majority of the players, serious injuries aside.
  2. I then combined the ice time data with my 3-year 5v5close, 5v4 power play and 4v5 shorthanded HARO+ and HARD+ ratings.  I used 3-year ratings because I think they more reliably reflect each players true abilities where as one year, and even two year, ratings have significant margins of error associated with them.
  3. For rookies and other relatively un-established players I had to take guestimates at their ratings and their ice times.  Most rookies or players with little NHL experience to develop ratings with I guestimated them to be below average players, except for players who are premiere prospects in which case I rated them more like an average player.   It is actually somewhat rare for rookies to perform significantly above average, especially defensively.
  4. Unlike my previous ratings, I did make adjustments for strength of schedule.
  5. Also, unlike my previous ratings, I did make adjustments for teams that might get more or less than an average number of power play or penalty kill opportunities.  To do this I used each teams total power play and short handed situations over the past 2 seasons and compared them to the league average.  For teams which more powerplays than the average team had their power play goal production increased and those with less had their power play goal production decreased accordingly.  The same was done for the penalty kill.  Of course, if a team changes their playing style to take or draw more or fewer penalties than in the previous 2 seasons the reliability of the predictions will be degraded somewhat.

As with the previous post, I haven’t converted goals for/against into points in the standings but this gives you an indication of how the numbers seem to view the teams talent levels.  So, with that said, here are your eastern conference predictions.

Predicted Last Season
Boston 227.1 203.5 23.5 244 189 55
Pittsburgh 242.0 219.9 22.1 228 196 32
Buffalo 235.4 217.7 17.6 240 228 12
Washington 236.3 218.9 17.3 219 191 28
Philadelphia 239.3 222.1 17.2 256 216 40
Toronto 245.3 235.6 9.6 213 245 -32
Tampa Bay 233.6 224.4 9.3 241 234 7
NY Rangers 217.5 210.8 6.8 224 195 29
Montreal 226.5 225.6 0.9 213 206 7
Carolina 227.5 231.0 -3.5 231 234 -3
Florida 211.4 216.6 -5.2 191 222 -31
New Jersey 202.3 210.1 -7.8 171 207 -36
NY Islanders 227.4 240.5 -13.1 225 258 -33
Winnipeg 210.3 235.5 -25.2 218 262 -44
Ottawa 189.5 251.8 -62.3 190 245 -55

Before getting into some team specific observations, a first observation worth noting is that the goals for and against predictions seem to be more compressed than what typically occurs in the NHL standings.  The predicted goals for totals range from a high of 245 to a low of 189.  The low of 189 is perfectly reasonable (the lows from the previous 3 seasons are 171, 196 and 190) but the high of 245 is well below the high totals of previous years.  Last season the Canucks scored a high of 258 goals, the previous season the Capitals led with 313 followed by the Canucks with 268 and in 2008-09 the Red Wings led with 289 goals.  I am not sure if this is evidence of increased parity or whether it is a flaw within the ratings system and/or the prediction algorithm.

Team GF Team GA
Toronto 245.3 Boston 203.5
Pittsburgh 242.0 New Jersey 210.1
Philadelphia 239.3 NY Rangers 210.8
Washington 236.3 Florida 216.6
Buffalo 235.4 Buffalo 217.7
Tampa Bay 233.6 Washington 218.9
Carolina 227.5 Pittsburgh 219.9
NY Islanders 227.4 Philadelphia 222.1
Boston 227.1 Tampa Bay 224.4
Montreal 226.5 Montreal 225.6
NY Rangers 217.5 Carolina 231.0
Florida 211.4 Winnipeg 235.5
Winnipeg 210.3 Toronto 235.6
New Jersey 202.3 NY Islanders 240.5
Ottawa 189.5 Ottawa 251.8


The teams with the largest predicted improvements in goal differential are the Leafs (42 points), the Devils (28), Panthers (26), Islanders (20), and Jets (19) while the teams predicted to fall back the most in terms of goal differential are Boston (-31), Philadelphia (-23) and the Rangers (-22).   The predicted top 6 scoring teams in the east are Toronto, Pittsburgh, Philadelphia, Washington, Buffalo and Tampa while the lowest scoring teams are predicted to be Ottawa, New Jersey, Winnipeg and Florida.  The teams with the predicted worst defense are Ottawa, Islanders, Toronto, Winnipeg and Carolina and the predicted best defensive teams are Boston, New Jersey, NY Rangers, Florida and Buffalo.  While there are a couple of surprises in there, most of those seem quite reasonable.  Now for some team specific observations.

Washington Capitals – The Capitals played a different game last season from the previous two seasons.  In 2008-09 they scored 268 goals but gave up 240, in 2009-10 they scored 313 and gave up 227.  Last season they improved significantly defensively giving up just 191 goals but their offense also suffered as they scored just 219.  The predictions are predicting the offense will come back next season but will cost them a little defensively.  Mathematically speaking it makes sense, but in reality it is difficult to say whether they will change their playing style back to a more offensive game or not at the cost of defense.  We’ll have to wait and see.

Toronto Maple Leafs – One of the biggest surprises in these predictions is the offense of the Maple Leafs.  They are predicted to score the most goals of any team, eastern or western conference.  A big reason for this is both Joffrey Lupul (who played just 28 games with the Leafs) and Tim Connolly have very good HARO+ ratings as do many of the returning Leaf forwards including Kessel, Kulemin, Grabovski, and MacArthur.  Even projected third line players Armstrong and Bozak have solid HARO+ ratings.  If the ratings are true, scoring goals shouldn’t be a problem for the Leafs and in fact the late season surge last year was predominantly a result of increased goal production and not solely due to the play of James Reimer.  The Leafs problematic defensive ability is still an issue for the Leafs though.

New York Rangers – It is difficult to fathom how a team that added Brad Richards will see their goal production drop from 224 to about 217.  This is a little dumbfounding, but the Rangers did lose 16 goals from Frolov and Prospal and the algorithm is certainly not predicting another 21 goals from Brian Boyle (his previous career high was 4) so it is certainly possible that Richards won’t dramatically increase the Rangers offensive output.  We’ll see.

Philadelphia Flyers – Unless some of the younger players really step up their games it is difficult to see them being as good a team as the Flyers from last season.  They are predicted to score 16 fewer goals but give up 6 more (despite Bryzgalov).

New Jersey – The Devils will be a dramatically better team this year, but they still may not be a very good one.  They have some highly talented forwards (Parise, Zajac, Kovalchuk) but they depth is weak and they will produce very little offense from the back end and who knows what Brodeur has left in the tank.

Florida Panthers – They brought a lot of players in this past off season and they should have an improved team but like the Devils it might be a stretch for them to make the playoffs.

Tomorrow we’ll take a look at the predictions for the western conference standings.


Mike Weaver – Premiere Defensive Defenseman

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Sep 152011

Over on Pension Plan Puppets there was a brief discussion of some of the top defensive defensemen and I suggested that Mike Weaver has to be considered among the top few defenders in the NHL.  The response was generally along the lines of ‘Mike who?’ and then followed with “he only looks good because he plays in front of Vokoun who may be the best goalie in the NHL.”  My thoughts on Vokoun being over rated aside, the numbers really do support Weaver as being a premiere level defensive defenseman.  Let’s look at some Mike Weaver numbers.

Over the past 4 seasons Mike Weaver played one season in Vancouver, 2 seasons in St. Louis and last season he was in Florida.  During that time there have been 173 defensemen who have played >1500 5v5 close (within 1 goal in first or second period or tied in third period) minutes and of those 173 defensemen Weaver ranks fourth in on ice goals against per 20 minutes of ice time.  He only trails Bryce Salvador, Sean O’Donnell and Paul Martin (3 other under rated defenders IMO).  Ranking 4th is a pretty good argument for why he is a great defender.  So what about the typical excuses for why he might rank so highly?

1.  Goalies make him look good.  Not really.  In the past 4 years he has played 2455:08 minutes of 5v5close ice time, 798:24 (32.5%) in front of Chris Mason, 587:45 (23.9%) in front of Vokoun, 390:58 (15.9%) in front of Luongo, 297:47 (12.1%) in front of Clemmensen, 185:04 (7.5%) in front of Conklin and some time in front of a few other lesser goalies.  At best you can argue he has played 45% of his time behind premiere level goalies (Vokoun and Luongo) with the remaining 55% behind second tier starters (Mason) or third tier starters and backups (Conklin, Clemmensen, etc).  In his year in Vancouver, the Canucks ranked a solid 7th in team goals against average but his 2 years in St. Louis the Blues ranked 12th and 11th and last year the Panthers ranked 14th so while he hasn’t played on any bad defensive teams he hasn’t played on any elite defensive teams either.  It’s difficult to make the case he has had an unusually significant benefit from playing in front of elite goalies or on elite defensive teams.

2. He Plays Easy Minutes.  Not really.  Over the past 4 seasons he ranks 45th of 173 defensemen with 34.1% of his face offs in the defensive zone and last season he started 36.9% of the time in the defensive zone or 21st highest of 157 defensemen with 500 5v5close minutes.  Over the past 4 seasons his opposition goals for per 20 minutes ranks 31st of 173 defensemen so he is seemingly playing against quality offensive forwards.  Last season the forwards he played most against were Ovechkin, Backstrom, Knuble, St. Louis, and Stamkos so yeah, that’s pretty good competition.  Over the past 2 seasons only Chris Phillips and Jay Bouwmeester have played more time on the 4v5 penalty kill than Weaver.  He is trusted playing tough minutes against top competition so the easy minutes argument is not valid.

While we are at it, Mike Weaver is another example why I do not like corsi/fenwick stats.  While Weaver has the 4th best on-ice 5v5close goals against per 20 minutes, he ranks a far less impressive (though still a little above average) 47th in fenwick against per 20 minutes.  The main reason why Weaver is so good defensively is he suppresses shot quality really well.  He ranks 3rd in shooting percentage against (or save percentage) while he is on the ice and he has been consistently above average over the past 4 seasons (6th of 176 in 2007-08, 63rd of 147 in 2008-09, 13th of 154 in 2009-10 and 22nd of 157 in 2010-11).  Three of the past 4 seasons he has been a top 25 defenseman in terms of shooting percentage against and the fourth and worst season he was still in the top half.  Sorry, but there is no ‘regressing to the mean’ there.

Mike Weaver is a premiere, and vastly under rated and under paid ($900,000), defensive defenseman.


Predicting the Eastern Conference

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Sep 132011

I have spent a lot of time and effort putting together player ratings so I decided it was time to finally put them to good use and attempt to use them to predict results for the upcoming season.  This is my attempt at the eastern conference and time permitting I’ll tackle the western conference in the future.

To accomplish this goal I used my 3-year (2008-11) offensive (HARO+) and defensive (HARD+) ratings at 5v5close, 5v4 powerplay and 4v5 penalty kill situations and combined all of the ratings for all of the players on each team and then converted them back to goals to come up with a predicted goals for and goals against for each team.  In doing this I estimated the ice time of every player so first and second line players will have a greater weight than third and fourth line players as well as reserve players and I also estimated how I believe each team will use their players on the power play and penalty kill.  I did this largely on each players PP and PK ice time last season.

I have made a few assumptions in these predictions.  First, teams will not suffer significant injuries.  Generally speaking, I cannot predict injuries so I have to go with this assumption.  There are a few exceptions though.  For example, I predicted that Sidney Crosby would miss the start of the season and miss about 1/4 of the season. There were one or two other players (Matthew Lombardi comes to mind) that I did this for but none were of the talent level of Crosby so the effects on the results will not be dramatic.  In any event, any significant injuries that occur will have an impact on results.

The second assumption I made was how to rate rookies and second year players that may not have a lot of ice time and thus not have reliable ratings.  For rookies, for the most part I rated them as slightly below average but it varied a bit depending on whether they are a big time prospect or not.  That said, if a team has a rookie or two that has an exceptional season it could affect the accuracy of my predictions.  For second year players or players without a significant history to develop ratings from I manually adjusted their ratings if they seemed to be out of whack (i.e. I manually regressed their ratings to the mean).  Some got their ratings bumped up, some bumped down.  For the most part these guys are not going to be key players to a team so errors in their guestimates are not likely to have a significant impact on overall team predictions.

The final assumption I made was that all teams will spend an equal amount of time on the power play and on the penalty kill.  This does not happen in reality and I am sure some teams are more prone to taking penalties (and drawing penalties) than other teams but I haven’t spent any time to attempt to predict that so for now I haven’t factored it in at all.

Oh, just remembered another assumption so this is the final final assumption I want to mention.  I have not factored in quality of competition.  If a team plays in an easier or more difficult division than another team this will affect their results somewhat.

So with all that said, here are the eastern conference predictions for the 2011-12 season.

Predicted 2011-12 Actual 2010-11
Washington Capitals 248.0 224.6 23.4 224 197 27
Boston Bruins 236.1 216.2 19.9 246 195 51
Pittsburgh Penguins 241.4 222.3 19.1 238 199 39
Buffalo Sabres 242.1 225.3 16.8 245 229 16
Philadelphia Flyers 242.7 226.7 16.0 259 223 36
Montreal Canadiens 229.2 225.1 4.1 216 209 7
Tampa Bay Lightning 230.7 231.6 -0.9 247 240 7
NY Rangers 219.4 223.0 -3.6 233 198 35
Toronto Maple Leafs 241.0 248.4 -7.4 218 251 -33
NJ Devils 214.2 225.4 -11.2 174 209 -35
Florida Panthers 216.2 227.5 -11.4 195 229 -34
NY Islanders 230.5 244.6 -14.1 229 264 -35
Carolina Hurricanes 220.4 242.1 -21.7 236 239 -3
Winnipeg Jets 212.7 244.4 -31.7 223 269 -46
Ottawa Senators 195.5 258.1 -62.6 192 250 -58

As you may have noticed, I haven’t predicted won-loss records, just goals for and against which correlates fairly well with won-loss records.  I have also included last years goals for and against for reference.  Generally speaking, the good teams are at the top and the bad teams are at the bottom.  If my predictions are reasonably accurate the Capitals, Bruins, Penguins, Sabres and Flyers look like they should make the playoffs fairly easily while the Hurricanes (a bit of a surprise maybe), Jets and Senators are likely on the outside looking in come playoff time.  That leaves Montreal, Tampa, NY Rangers, Maple Leafs, Devils, Panthers and maybe the Islanders fighting for the final 3 playoff spots.  Generally speaking, that makes sense to me.

Let’s take a look at this data in a slightly different way.  Lets look at who has the greatest improvement in goal differential (GF-GA) from last season to m predictions for this upcoming season.


2011-12 2010-11
Team GF-GA GF-GA Diff
Toronto Maple Leafs -7.4 -33 25.6
NJ Devils -11.2 -35 23.8
Florida Panthers -11.4 -34 22.6
NY Islanders -14.1 -35 20.9
Winnipeg Jets -31.7 -46 14.3
Buffalo Sabres 16.8 16 0.8
Montreal Canadiens 4.1 7 -2.9
Washington Capitals 23.4 27 -3.6
Ottawa Senators -62.6 -58 -4.6
Tampa Bay Lightning -0.9 7 -7.9
Carolina Hurricanes -21.7 -3 -18.7
Pittsburgh Penguins 19.1 39 -19.9
Philadelphia Flyers 16.0 36 -20.0
Boston Bruins 19.9 51 -31.1
NY Rangers -3.6 35 -38.6

Generally speaking the teams that have the highest predicted improvement were teams that had poor seasons last year and the teams with the greatest predicted fall back are teams that had good years last year.  There is probably a regression to the mean happening here.  The good teams last year probably had some luck going their way and the teams at the bottom of the standings probably had some bad luck.

For the gainers, the Devils potential gain is fully understandable.  They had a horrendous first half of last season but played much better in the second half.  They should be closer to their second half performance this upcoming year.  The Florida Panthers spent a lot of money on free agents and should have an improved team, but still may not make the playoffs.  The Maple Leafs, Islanders and Jets are probably more in the had some bad luck last season and will regress to the mean category though their young players should be a bit better too.

The Rangers predicted fall back is a bit of a surprise considering they signed Brad Richards but they lost Drury, Frolov, Gilroy, McCabe and Prospal.  Their projected defense looks potentially very weak.  After Staal and Girardi you have Sauer, McDonagh, Erixon, Del Zotto, and Eminger all of whom are very young with little or no experience or in the case of Eminger a one time quality prospect that never really established himself as an NHL regular.

The table below shows the predicted top offensive and defensive teams.

Team GF Team GA
Washington Capitals 248.0 Boston Bruins 216.2
Philadelphia Flyers 242.7 Pittsburgh Penguins 222.3
Buffalo Sabres 242.1 NY Rangers 223.0
Pittsburgh Penguins 241.4 Washington Capitals 224.6
Toronto Maple Leafs 241.0 Montreal Canadiens 225.1
Boston Bruins 236.1 Buffalo Sabres 225.3
Tampa Bay Lightning 230.7 NJ Devils 225.4
NY Islanders 230.5 Philadelphia Flyers 226.7
Montreal Canadiens 229.2 Florida Panthers 227.5
Carolina Hurricanes 220.4 Tampa Bay Lightning 231.6
NY Rangers 219.4 Carolina Hurricanes 242.1
Florida Panthers 216.2 Winnipeg Jets 244.4
NJ Devils 214.2 NY Islanders 244.6
Winnipeg Jets 212.7 Toronto Maple Leafs 248.4
Ottawa Senators 195.5 Ottawa Senators 258.1

It is probably not a surprise that the Capitals, Flyers, Sabres, Penguins, Bruins and Lightning are among the top offensive teams but it is interesting to see the Maple Leafs move up the offense list.   It is a common belief that the Leafs late season success last season was because of the play of goalie James Reimer and Reimer did play a part, but in reality, much of the reason for the success was actually due to the fact that the Leafs scored a lot of goals.  Add Connolly and Liles into the mix and the Leafs can put out three lines who can score so while they may not have the elite offensive players some of the other teams have, they have depth (not unlike the Bruins actually whose top point producer was Krejci with just 62 points – Kessel had 64 for the Leafs).  Defensively it seems the Leafs may continue to struggle.  They are not a good defensive team and they desperately need to figure out how to improve their penalty kill.  Defense could be a problem, even with improved goaltending (which may or may not be reality – Reimer had success over a somewhat small sample size and Gustavsson has never performed well).

It is probably worth saying a word or two about the Ottawa Senators.  It seems they will struggle to score and will struggle to keep the puck out of their own net.  The Senators may be in for a tough season but it will be a season of evaluation of young players and hopefully (for Sens fans) progress.  On any given night they will potentially have 6-8 rookies in the lineup.  Expect to see rookie forwards Bobby Butler, Mika Zibanejad, Erik Condra, Colin Greening, Zack Smith, Nikita Filatov and Stephane Da Costa in the line up through out the season as well as defensemen Jared Cowan, David Runblad, Patrick Wiercioch.  If some of these guys are truly ready to become solid NHL regulars they might not be as bad as the above tables suggest, but they will still likely be competing for the first overall draft pick (which is probably a good thing for them anyway)

Finally, let me suggest that you not all take these too seriously.  While I do think there is some merit to these predictions, if you think your team is ranked too low or another team is ranked too high, no need to have a fit over it.  I really don’t know how accurate they are and a lot can happen to alter what really happens anyway.  I wanted to post these in part to generate a discussion but also in part so I can track these predictions as the season progresses and come the end of the season look back see how well this unbiased, mostly mathematical prediction system performs.