Jan 282012
 

I have been having a discussion as to whether shot quality exists over at Pension Plan Puppets and more precisely whether certain players can drive a teams shooting percentage while they are on the ice.  As part of the discussion I brought up the on-ice shooting percentage differences between Scott Gomez and Michael Cammalleri and decided that it would be useful to present that comparison as a post here.

First off, let me define shot quality as how I see it.  Shot quality is an ability for players to systematically drive (or suppress) shooting percentages when they are on the ice.  To me it doesn’t matter whether they can drive shooting percentages because they can get more shots from better shooting locations, or are better shooters, or are better playmakers setting up  changes with the goalie out of position.  Those are interesting things to investigate, but investigating them isn’t necessary to show shot quality exists.  Shot quality, in my mind, is all about a players being able to drive (or suppress) shooting percentage when they are on the ice, regardless of how.

In the past I have used examples such as Henrik Sedin vs Travis Moen and some comments I got were “but those are extreme cases” which is an interesting comment because in essence they person making that argument is admitting that shot quality exists but only in extreme cases.  So, I decided that it might be useful to take a look at two players who generally speaking play similar roles.  Scott Gomez and Michael Cammalleri.  Both Gomez and Cammalleri are top six forwards generally thought of as more offensive players.  What is also interesting is they over the past 4 1/2 seasons they both have switched teams and they have both spent a couple years playing on the same team, sometimes on the same line.    Let’s take a look at their 5v5 on-ice shooting percentages over the past 4 1/2 seasons.

 Sh% Gomez Cammalleri Difference
2007-08 7.09 8.15 1.06
2008-09 6.15 9.25 3.10
2009-10 7.89 9.66 1.77
2010-11 4.50 7.07 2.57
2011-12 7.96 8.11 0.15

In each and every season Cammalleri has had a higher shooting percentage, sometimes much higher.  Only this season have they been close in their on-ice shooting percentages.  If that isn’t a systematic ability by Cammalleri and his linemates to get a higher shooting percentage than Gomez and his linemates, I don’t know what is.  They can do it every singles season.

Now, let’s take a look at their offensive fenwick rates.  Here are their fenwick for per 20 minutes of 5v5 ice time rates.

 FF20 Gomez Cammalleri Difference
2007-08 15.86 14.3 -1.56
2008-09 16.76 15.38 -1.38
2009-10 14.21 13.4 -0.81
2010-11 16.4 14 -2.4
2011-12 16.8 12.06 -4.74

Well now, that tells us a different story.  Gomez and his line mates take far more shots than Cammalleri and his line mates, and they do it every single season.  Gomez and his line mates seem to have a much better skill at taking shots, but Cammalleri and his line mates seem to have a much better skill at capitalizing on shots.  The question now is, which skill results in more goals.  Here are their 5v5 goals for per 20 minute stats.

 GF20 Gomez Cammalleri Difference
2007-08 0.792 0.801 0.009
2008-09 0.757 1.020 0.263
2009-10 0.837 0.927 0.090
2010-11 0.534 0.713 0.179
2011-12 0.854 0.756 -0.098

Now that is interesting.  Cammalleri and his line mates have out produced Gomez and his line mates every year until this season.  Based on this one example, being able to drive shooting percentage resulted in more goals being scored than being able to drive shots.  If you were down by a goal in the third period, who would you rather have on the ice, Gomez and his line mates or Cammalleri and his line mates?

And the above is a perfect example of why I don’t like pure corsi/fenwick based evaluation of players.  If you just look at corsi/fenwick, Gomez looks like a very good player (see here and here), and Cammalleri does not.  But, if you look at goals, over the past 2 seasons 54.1% of all goals scored while Cammalleri was on the ice were for the Canadiens while just 47.2% of all goals scored while Gomez was on the ice were for the Canadiens.  Who is the better player, and who would I rather have on my team?  Cammalleri by a country mile.

Let’s take it one step further and how they played when they were on the ice together and when they were apart over the past 2 seasons.

Together Cammalleri Gomez
GF% 54.8% 53.9% 45.4%
Corsi% 52.3% 47.9% 51.6%

Wow, that is dramatic.  When they play together can an drive shots (corsi) and goals.  When Cammalleri is not playing without Gomez he can drive goals, but not shots (corsi) and when Gomez is playing without Cammalleri he can drive shots (corsi) but not goals.  Again, who would you rather have on your team?  For me, I’ll take the guy who can drive goals thank you very much.

And that my friends, is a perfect example of when a corsi based analysis will fail.

 

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%
MARIAN GABORIK 40.6%
SIDNEY CROSBY 36.3%
ALEX TANGUAY 33.1%
HENRIK SEDIN 32.8%
BOBBY RYAN 32.5%
EVGENI MALKIN 31.9%
DANIEL SEDIN 31.6%
ILYA KOVALCHUK 30.6%
NATHAN HORTON 29.6%
J.P. DUMONT 29.4%
GREGORY CAMPBELL -12.4%
RYAN CALLAHAN -13.9%
RADEK DVORAK -15.6%
CHRIS DRURY -16.8%
SEAN BERGENHEIM -19.4%
SCOTT GOMEZ -19.7%
MARTIN HANZAL -21.5%
MIKE GRIER -21.5%
DANIEL WINNIK -24.5%
TRAVIS MOEN -32.1%

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
HENRIK ZETTERBERG 24.7%
ALEX OVECHKIN 21.7%
PAVEL DATSYUK 20.6%
TOMAS HOLMSTROM 19.9%
NICKLAS BACKSTROM 19.8%
ERIC STAAL 19.7%
RYANE CLOWE 18.8%
ALEXANDER SEMIN 18.3%
SCOTT GOMEZ 18.0%
ZACH PARISE 17.9%
MARTY REASONER -6.5%
ANDREW COGLIANO -6.5%
ANTTI MIETTINEN -6.7%
KYLE BRODZIAK -7.3%
CHRIS KELLY -8.6%
ILYA KOVALCHUK -9.8%
JAY MCCLEMENT -10.4%
MICHAL HANDZUS -14.4%
JOHN MADDEN -14.5%
TRAVIS MOEN -15.6%

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.

 

Aug 212011
 

I have just updated my stats site (stats.hockeyanalysis.com) to include a number of new features.  The added features are:

1.  I have added a new situation – 5v5close.  5v5close is when the game is tied or within 1 goal in the first and second period or tied in the third period.  This is what I would call normal play where teams are more or less (depending on talent or game play/coaching style) equally interested in  playing offense or defense.  When teams get a larger lead or lead late in the game teams adjust their style of play to either protect that lead or go all out to score a goal to catch up.  It is probably better to use this than 5v5tied and maybe better than 5v5 (all 5v5 game score situations).

2.  I have included zone start data in the form of OZOF%, DZOF% and NZOF%.  OZOF% is the percentage of face offs taken in the offensive zone when the player is on the ice and DZOF% and NZOF% are the same for defensive zone and neutral zone faceoffs.  When we look at these by situation we can get an idea of how a players use gets changed by game score.  For example, last year Manny Malholtra had 38.8% of his 5v5 face offs in the defensive zone (29.1% offensive zone and 32.1% neutral zone) but when the Canucks were up by a goal his defensive zone faceoffs rose to 41.6% and when the Canucks were up by 2 goals they rose to 48.4%.

3.  I have once again put up with/against statistics for each player.  I had this data up a few years ago but when I re-designed my website I removed it but it is back.  Each player page (i.e. the Malhotra one linked to above) has a set of links at the top of the page to with/against statistics for each season (and multi-seasons) for 5v5 and 5v5 close situations for both goal and corsi data.  Each page shows how the player played with each teammate as well as how they played when they were not playing together as well as how the player performed against each opponent and how well the player and the opponent performed when not playing together.  These tables can give you an indication of which players are playing together and which players play well together as well as who a player plays against the most.  As an example, take a look at Manny Malhotra 5v5 goal with/against data for this past season and you will see he played the most with Raffi Torres (even more than with Roberto Luongo!) but it seems both players had better on ice results when apart.

4.  If you hadn’t noticed yet, a while back I added on ice shooting percentage (Sh%) and on ice opposition shooting percentage (OppSh%, subtract from to get on ice save %) which can be found with the goal data (but not with corsi, fenwick and shot data).

All totaled, there is well over 10 gigabytes of html, php and data base files of statistics (90% of which is in the with/against tables) so be warned, if you really wanted to you could spend days looking at it all.

Jul 122011
 

Over the past couple of weeks I have had several comment discussions regarding some of my recent posts on player evaluation and Norris and Hart trophy candidates which centered around which is a better method for evaluating players:  corsi vs goal based evaluation.  A lot of people, maybe the majority of those within the advanced hockey stat community, seem to prefer corsi based analysis while I prefer goal based analysis and I hope to explain why with this post.  I have explained much of this previously but hopefully this post will put it all into one simple easy to understand package.

There are two main objectives for a player when the coach puts him on the ice:  1.  Help his team score a goal.  2.  Help his team stop the opposing team from scoring a goal.  Depending on the situation and the player the coach may prioritize one of those over the other.  For example, a defensive player may be tasked primarily with shutting down an opposing teams offensive players and scoring a goal is really a very minor objective.  Late in a game when a team is down a goal the opposite is true and the primary objective, if not sole objective, is to score a goal.

I think we can all agree on the previous paragraph.  Goals are what matter in hockey so right there we have the #1 reason why goals should be used in player evaluation.  The problem is, goals are a relatively rare event and thus ‘luck’ can have a serious impact on our player analysis results due to the small sample size that goals provide.  This brought on the concept of corsi which is nothing more than shot attempts and is used as a proxy for scoring chances.  The benefit of corsi is that shot attempts occur about 10 times often as goals which gives us a larger sample size to evaluate players.

Continue reading »

Jun 012011
 

There seems to be some confusion, or lack of clarity, about my post on corsi vs shooting percentage vs shooting rate the other day so let me clear it up in as straight forward a way as I can.

“Hawerchuk” over at BehindTheNetHockey.com writes the following:

“I’m not totally sure what he’s getting at. People use Fenwick because it’s persistent, and PDO because it’s not. Over the course of a single season, observed shooting and save percentage drive results, but they are not persistent.”

Dirk Hoag over at OnTheForecheck.com writes:

“Here’s an example of when NOT to use correlation as a tool in statistical analysis (when the variables in question are linked by definition). David makes a bad blunder here, by looking at scoring leaders, seeing a bunch of high shooting percentages, and concluding that shooting percentage is the true “talent”. The problem is that shooting percentage swings wildly from season to season, whereas shooting rates are much more consistent.”

The great advantage of corsi/fenwick has over goals as an evaluator of talent is the greater sample size associated with it.  The greater the sample size the more confidence we can have in any results we conclude from it and the less chance that ‘luck’ messes things up.  Year over year shooting percentage fluctuates a lot, but that doesn’t necessarily mean that it isn’t a talent or doesn’t have persistence, it could mean that the sample size of one year is too small.  The four year shooting percentage leader board seems to identify all the top offensive players so it can’t be completely random.  So what happens if we increase the sample size?  Here are correlations of fenwick shooting percentages while on ice in 5v5 even strength situations for forwards:

Year(s) vs Year(s) Corrolation
200708 vs 200809 0.249
200809 vs 200910 0.268
200910 vs 201011 0.281
200709 vs 200911 (2yr) 0.497

As you can see, there isn’t a lot of persistence year over year but for 2 years over 2 years we are starting to see some persistence.  Still not to the level of corsi/fenwick, but certainly not non-existant either, and the greater correlation with scoring goals makes fenwick shooting percentage on par with fenwick as a predictor of future goal scoring performance when we have 2 seasons of data as I pointed out in my last post.

For the record, year over year correlation for fenwick for rate is approximately 0.60 depending on years used  and 2 year vs 2 year correlation is 0.66.

But as I pointed out in my previous post, you would probably never use shooting percentage as a predictor because you may as well use goal rate instead which has the same sample size limitations as shooting percentage but also factors in fenwick rate.  Year over year correlation of GF20 (goals for per 20 minutes) is approximately 0.45 depending on years used and the 2 year vs 2 year correlation is 0.619 so GF20 has persistence and has a 100% correlation with itself making it as reliable (or more) a predictor of future goal scoring rates as fenwick rate with just one year of data and a better predictor when using 2 years of data.  Let me repost the pertinent table of correlations:

Year(s) vs Year(s) FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.396 0.386
200809 vs 200910 0.434 0.468
200910 vs 201011 0.516 0.491
Average 0.449 0.448
200709 vs 200911 (2yr) 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.479 0.527

The conclusion is, when dealing with less than a years worth of data, fenwick/corsi is probably the better metric to identify talent and predict future performance, but anything greater than a year goals for rate is the better metric and for one years worth of data they are about on par with each other.

Note:  This is only true for forwards.  The same observations are not true about defensemen where we see very little persistence or predictability in any of these metricts, I presume because the majority of them don’t drive offense to any significant degree.

May 302011
 

The general consensus among advanced hockey statistic analyzers and is that corsi/fenwick stats are the best statistic for measuring player and team talent levels.  For those of you who are not aware of corsi and fenwick let me give you a quick definition.  Corsi numbers are the number of shots directed at the goal and include shots, missed shots and blocked shots.  Fenwick numbers are the same except it does not included blocked shots (just shots and missed shots).  I generally look at fenwick and will do that here but fenwick and corsi are very highly correlated to the results would be similar if I used corsi.

The belief by many that support corsi and fenwick is that by looking at fenwick +/- or fenwick ratio (i.e. fenwick for /(fenwick for + fenwick against)) is an indication of which team is controlling the play and the team that controls the play more will, over time, score the most goals and thus win the most games.  There is some good evidence to support this, and controlling the play does go a long way to controlling the score board.  The problem I have with many corsi/fenwick enthusiasts is that they often dismiss the influence that ability to drive or suppress shooting percentage plays in the equation.  Many dismiss it outright, others feel it has so little impact it isn’t worth considering except when considering outliers or special cases.  In this article I am going to take an in depth look at the two and their influence on scoring goals on an individual level.

I have taken that last 4 seasons of 5v5 even strength data and pulled out all the forwards that have played at minimum 2000 minutes of 5v5 ice time over the past 4 seasons.  There were a total 310 forwards matching that criteria and for those players I calculated the fenwick shooting percentage (goals / fenwick for), fenwick for rate (FenF20 – fenwick for per 20 minutes of ice time) and goal scoring rate (gf20 – goal for per 20 minutes ice time) while the player was on the ice. What we find is shooting percentage is more correlated with goal production than fenwick rate.

Shooting % vs GF20 R^2 = 0.8272
FenF20 vs GF20 R^2 = 0.4657
Shooting % vs FenF20 R^2 = 0.1049

As you can see, shooting percentage is much more highly correlated with goal scoring rate than fenwick rate is which would seem to indicate that being able to drive shooting percentage is more important for scoring goals than taking a lot of shots.

Here is a list of the top 20 and bottom 10 players in fenwick shooting percentage and fenwick rate.

Rank Player FenSh% Player FenF20
1 MARIAN GABORIK 8.07 HENRIK ZETTERBERG 16.7
2 SIDNEY CROSBY 7.83 ALEX OVECHKIN 16.3
3 ALEX TANGUAY 7.64 PAVEL DATSYUK 16.15
4 HENRIK SEDIN 7.63 TOMAS HOLMSTROM 16.05
5 BOBBY RYAN 7.60 NICKLAS BACKSTROM 16.05
6 STEVE DOWNIE 7.58 ERIC STAAL 16.04
7 EVGENI MALKIN 7.57 RYANE CLOWE 15.91
8 DANIEL SEDIN 7.55 ALEXANDER SEMIN 15.85
9 ILYA KOVALCHUK 7.49 SCOTT GOMEZ 15.8
10 NATHAN HORTON 7.44 ZACH PARISE 15.8
11 J.P. DUMONT 7.43 ALEXEI PONIKAROVSKY 15.79
12 JASON SPEZZA 7.39 JOHAN FRANZEN 15.78
13 PAUL STASTNY 7.36 JIRI HUDLER 15.74
14 PAVOL DEMITRA 7.33 DAN CLEARY 15.71
15 DANY HEATLEY 7.30 SIDNEY CROSBY 15.71
16 RYAN MALONE 7.29 JUSTIN WILLIAMS 15.68
17 JONATHAN TOEWS 7.28 CHRIS KUNITZ 15.61
18 THOMAS VANEK 7.24 MIKHAIL GRABOVSKI 15.56
19 SERGEI KOSTITSYN 7.24 JOE PAVELSKI 15.43
20 DREW STAFFORD 7.24 MIKAEL SAMUELSSON 15.39
301 BLAIR BETTS 4.20 CHUCK KOBASEW 11.34
302 ERIC NYSTROM 4.12 TRAVIS MOEN 11.31
303 SAMUEL PAHLSSON 4.10 IAN LAPERRIERE 11.23
304 SHAWN THORNTON 3.99 ERIC NYSTROM 11.21
305 TRAVIS MOEN 3.89 ROB NIEDERMAYER 10.94
306 TODD MARCHANT 3.88 TODD MARCHANT 10.91
307 NATE THOMPSON 3.75 SAMUEL PAHLSSON 10.87
308 FREDRIK SJOSTROM 3.70 JERRED SMITHSON 10.76
309 CRAIG ADAMS 3.52 JAY PANDOLFO 10.74
310 STEPHANE VEILLEUX 3.49 BLAIR BETTS 10.67

For both lists, the players are the top of the list are for the most part considered top offensive players and the players at the bottom of the list are not even close to being considered quality offensive players.  So, it seems that both shooting percentage and fenwick do a reasonable job at identifying offensively talented players.  That said, the FenF20 list includes 7 players (Zetterberg, Datsyuk, Holmstrom, Franzen, Hudler, Cleary and Samuelsson) who have played mostly or fully with the Detroit Red Wings and it seems unlikely to me that 7 of the top 20 offensive players are Red Wing players.  Furthermore, the fenwick list also includes guys like Ponikarovsky, Samuelsson, Hudler, Cleary, Williams, etc. who would probably be considered secondary offensive players at best.  From just this cursory overview it seems to confirm what we saw with the correlations – Shooting Percentage is a better indicator of offensive talent than Fenwick For rates.

It is actually no surprise that the Red Wings dominate the fenwick rate leader board because the Red Wings organizational philosophy is all about puck control.

“It’s funny because our game looks at numbers just like other games,” says Red Wings general manager Ken Holland, “but as much value as we assign to puck possession and how essential it is to winning, we really don’t have a numerical value for it that everyone can agree on. Remember when [A's general manager] Billy Beane started emphasizing on-base percentage in baseball? It wasn’t just a curious number; it changed the game. It redefined the type of player you wanted on your team. It’s coming in hockey; we just have to figure out how.”

This got the pro-corsi crowd riled up a bit as they said “Umm, yeah, we have that stat and it is called corsi” and were a bit bewildered at why NHL GMs didn’t make that recognition.  But anyway, what the above shows is that an organization that focuses on puck control dominates the corsi for statistic so I guess what that shows is that corsi/fenwick probably is a good measure of puck control.  But, as we have seen, fenwick (i.e. puck control) doesn’t automatically translate into goals scored.  There are no Red Wing players among the top 20 in fenwick shooting percentage and Datsyuk is the only Red Wing player in the top 20 in goals for per 20 minutes so while they take a lot of shots (or at least shot attempts), they aren’t the best at converting them into goals.

For me, and I am sure many others, the above is enough to conclude that shooting percentage matters a lot in scoring goals, but for the staunch corsi supporters they will argue that corsi is more persistent from season to season and thus is a better predictor of future performance.  So which is the better predictor of future performance?  The following table shows the correlation between shooting percentage and fenwick rate with the following seasons goal scoring rate.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20
200708 vs 200809 0.253 0.396
200809 vs 200910 0.327 0.434
200910 vs 201011 0.317 0.516
Average 0.299 0.449
200709 vs 200911 (2yr) 0.479 0.498
200709 vs 200910 (2yr vs 1yr) 0.375 0.479

Note:  For the above season(s) vs season(s) correlation calculations, only players with at least 500 5v5 even strength minutes in each of the four seasons are included.  This way the same players are included in all season(s) vs season(s) correlation calculations.

As you can see, when dealing with a single season of data the correlation with GF20 is much better for fenwick rate than for fenwick shooting percentage.  The gap closes when using 2 seasons as the predictor of a single season and is almost gone when using 2 seasons to predict the following 2 seasons.  It seems that the benefit of using corsi over shooting percentage diminishes to near zero when we have multiple seasons of data and though I haven’t tested it shooting percentage probably has an edge in player evaluation with 3 years of data.

Of course, you would never want to use shooting percentage as a predictor of future goal scoring rate when you could simply use past goal scoring rate as the predictor.  Past goal scoring rate has the same ‘small sample size’ limitations as shooting percentage (both use goals scored as it sample size limitation) but scoring rate combines the prediction benefits of shooting percentage and fenwick rate.  The table below is the same as above but I have added in GF20 as a predictor.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.253 0.396 0.386
200809 vs 200910 0.327 0.434 0.468
200910 vs 201011 0.317 0.516 0.491
Average 0.299 0.449 0.448
200709 vs 200911 (2yr) 0.479 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.375 0.479 0.527

The above table tells you everything you need to know.  When looking at single seasons both GF20 and FenF20 perform similarly at predicting next seasons GF20 with fenwick shooting percentage well behind but when we have 2 years of data as the starting point, GF20 is the clear leader.  This means, when we have at least a full seasons worth of data (or approximately 500 minutes ice time), goal scoring rates are as good or better than corsi rates as a predictor of future performance and beyond a years worth of data the benefits increase.  When dealing with less than a full season of data, corsi/fenwick may still be the preferred stat when evaluating offensive performance.

So what about the defensive side of things?

Year(s) vs Year(s) FenA20 to GA20 GA20 to GA20
200708 vs 200809 0.265 0.557
200809 vs 200910 0.030 0.360
200910 vs 201011 0.120 0.470
Average 0.138 0.462
200709 vs 200911 (2yr) -0.037 0.371
200709 vs 200910 (2yr vs 1yr) 0.000 0.316

Defensively, fenwick against rate is very poorly correlated with future goals against rate and it gets worse, to the point of complete uselessness, when we consider more seasons.  Past goals against rate is a far better predictor of future goals against rate.

Where it gets interest is unlike offense correlation drops when you consider more seasons which seems a bit strange.  My guess is the reason we are seeing this is because I am just looking at forwards and defense is more driven by goaltending and defensemen and as more time passes the greater the difference are in goalie and defensemen teammates.  Furthermore, forward ice time is largely driven by offensive ability (and not defensive ability) so many of the quality defensive forwards may be removed from the study because of the 500 minute per season minimum I am using (i.e. the group of players used in this study are biased towards those that aren’t focusing on defense).  Further analysis is necessary to show either of these as true though but the conclusion to draw from the above table that, for forwards at least, goals against rates are by far the better indicator of defensive ability.

In summary, it should be clear that we cannot simply ignore the impact of a players ability to drive or suppress shooting percentage in the individual player performance evaluation and so long as you have a full year of data (or > 500 or more minutes ice time) the preferred stat for individual player performance evaluation should be goal scoring rate.  Corsi/fenwick likely only provide a benefit to individual performance evaluation when dealing with less than a full year of data.

Dec 152010
 

I have been pretty quiet here recently not because of a lack of things I want to write about but because I needed to get my stats site up and running first so I can reference it in my writings.  Plus, getting my stats site up has been on my todo list for a real long time.  There will be a lot more stats to come including my with/against on ice pairing stats which I had up a season or two ago and many of you found interesting as well as team stats but for now let me explain what is there.

What you will find there now is my player rating system which produces the following ratings:

HARD – Hockey Analysis Rating – Defense

HARO – Hockey Analysis Rating – Offense

HART – Hockey Analysis Rating – Total

HARD+ – Hockey Analysis Rating – Defense

HARO+ – Hockey Analysis Rating – Offense

HART+ – Hockey Analysis Rating – Total

HARD is the defensive rating and is calculated by taking expected goals against while on the ice and dividing it by actual goals against while on the ice.  The expected goals against is calculated by taking the average of a players team mates goals against per 20 minutes (TMGA20) and averaging it with the players opposition goals for per 20 minutes (OppGF20).  Similarly HARO is calculated by taking a players actual goals for while on the ice and dividing it by the expected goals against while on the ice.  For both, a rating above 1.00 means that the player helped the team perform better than expected when he was on the ice where as a rating below 1.00 means the player hurt the teams performance when he was on the ice.  HART is just an average of HARD and HARO.

HARD+, HARO+ and HART+ are enhanced ratings which result from an iterative process that iteratively feeds HARD and HARO ratings into an algorithm to refine the ratings.  For the most part this iterative process produced a nice stable state but sometimes the algorithm goes haywire and things fail (i.e. for a particular season or seasons).  For this reason I am calling the + ratings experimental but if you don’t see anything wacky (i.e. large differences in every players ratings) they should be considered reliable and probably better ratings than the straight HARD, HARO and HART ratings.  Anything better than 1.00 should be considered better than the average player and anything less than 1.00 should be considered below average.

Continue reading »

Dec 032010
 

(Updated to include 3 seasons of data as I now realize that more luck data was available)

The other day there was a post on the Behind the Net Blog which used betting odds to estimate how lucky a team was during the 2009-10 season.  In many ways it is quite an ingenious way to evaluate a teams luck and I recommend those who have not read it go take a look.  Last night I was watching, sadly, the Leafs-Oilers game and thinking about luck in a hockey game and whether a team has any control over the luck they experience.   It got me thinking, does a team which controls the flow of the play mean that team is more likely to have more ‘good luck’ stuff happen to them than ‘bad luck’ stuff.

I defined luck as being how many standard deviations their actual point totals were from their expected point totals as defined in the document referenced in the Behind the Net blog post and in an updated document with 4 years of data.  I have only included 3 seasons in this analysis since I have only been working with 3 seasons of data recently and I was too lazy to go back and calculate a fourth season right now.

The most used stat to indicate how well a team controls the play is corsi or fenwick percentage which is basically the number of shots a team directs at the goal divided by the number of shots that they and their opponents teams directed at the goal.  I’ll be using Fenwick % here which includes shots and missed shots but not blocked shots.  So how does Fenwick % correlate with luck?

The correlation is fairly low but a correlation exists.  Maybe good teams can generate their own luck.  Here is a table of a teams luck and fenwick% for 2009-10.

Team Luck Fen%
Chicago Blackhawks 0.777 0.578
Detroit Red Wings 0.395 0.541
Boston Bruins -0.534 0.536
Pittsburgh Penguins -0.156 0.530
Toronto Maple Leafs -1.282 0.528
New Jersey Devils 0.459 0.522
St. Louis Blues 0.186 0.519
Phoenix Coyotes 2.092 0.515
Nashville Predators 1.225 0.514
Calgary Flames -0.590 0.513
Washington Capitals 1.883 0.512
San Jose Sharks 1.020 0.512
Philadelphia Flyers -1.157 0.511
Ottawa Senators 0.083 0.508
Los Angeles Kings 1.040 0.498
Buffalo Sabres 0.302 0.496
Atlanta Thrashers -0.347 0.496
New York Rangers -0.753 0.495
Vancouver Canucks 0.471 0.495
Carolina Hurricanes -0.555 0.491
New York Islanders -0.201 0.490
Columbus Blue Jackets -0.855 0.488
Dallas Stars -0.212 0.480
Anaheim Ducks -0.087 0.467
Tampa Bay Lightning -0.604 0.466
Florida Panthers -0.726 0.465
Montreal Canadiens 0.052 0.464
Minnesota Wild -0.486 0.459
Colorado Avalanche 0.599 0.449
Edmonton Oilers -1.993 0.446

When I was looking through the table something caught my attention.  Of the bottom 15 teams in Fenwick%, only four teams had positive luck.  These were Buffalo, Vancouver, Montreal and Colorado.  Generally speaking, these four teams had good to very good goaltending.  Of the top 15 teams in Fenwick%, only five teams had negative luck.  These were Boston, Pittsburgh, Toronto, Calgary and Philadelphia.  Boston and Calgary had good to very good goaltending (especially once Boston switched mostly to Rask) but Philadelphia, Pittsburgh and Toronto had mediocre to poor goaltending.  That got me to wondering whether goaltending correlated with luck at all so I took a look at the correlation between 5v5 game tied shooting and save percentages with luck.

Like fenwick%, there is an indication of a small correlation between shooting percentage and luck and there is a bit more of a correlation with save percentage.  Next I looked at combining all three factors.  Initially I was going to look at combining all three through some sort of average but then decided to look at goals for percentage instead (goals for divided by goals for plus goals against) since that basically encompasses everything anyway and we find that combined we get a relatively strong correlation with luck.

Now we are getting into correlation that might actually mean something, but what does it all mean?  To be honest, I am not sure.  Regardless of what ‘skill’ we look at there does seem to be a small positive correlation between how good a team is and how good their luck is (as calculated from the betting lines).  Does this mean that a bad team and especially a team with bad goaltending opens itself up to more bad luck than good teams or teams with good goaltending, or does it mean that luck manifests itself mostly in bad goals against or does it simply mean that the people who bet on hockey games trend towards betting the underdog which would push their expected winning percentage up and good teams expected winning percentage down which would result in a poor estimation of luck?  I am not sure how you determine what the exact cause of the correlation is but if it is the latter I have a word of advice, always bet the favourite.

Nov 222010
 

There are two things that must occur to score a goal.  The first way is to get an opportunity to score and the second is to capitalize on that opportunity to score.  There are a number of statistics that we can use as a proxy for opportunity to score but one of the most common is Fenwick numbers which are shots + missed shots (some call this Corsi but I define Corsi as shots + missed shots + blocked shots).  We can then define the ability to cash in on opportunities as shooting percentage, or in this case fenwick shooting percentage.  So let me define the following:

Opportunity Generation = Fenwick shots per 20 minutes of ice time.

Capitalization Ability = Fenwick Shooting Percentage = Goals Scored / Fenwick shots

So the question I pose today is this:  What is more important in scoring goals, generating opportunities or the ability to capitalize on those opportunities.  To answer this I calculated each teams Fenwick per 20 minutes (opportunity generation) and each teams Fenwick Shooting Percentage (capitalization ability) and compared them to the number of goals they generated per 20 minutes of ice time and I did this for each of the past three seasons (I only considered even strength five on five data).  I also did this for both the offensive and defensive ends of the ice for a total of 90 data points offensively and defensively.

First for the offensive end of the game:

As you can see, shooting percentage (opportunity capitalization) has a much stronger relationship with scoring goals than getting shots (opportunity generation).  What about the defensive end of the game?

Again, opposition capitalization rates are much more correlated with scoring goals than opportunity generation.  In fact opportunity generation appears to have no correlation with giving up goals at.

The conclusion we can draw from these four charts is when it comes to scoring goals, having the ability to capitalize on opportunities (shots) is far more important than having the ability to generate opportunities (getting shots).  Controlling the play and generating shots does not mean you’ll score goals (just ask any Maple Leaf fan), having the talent to capitalize on those opportunities is what matters most.  From my perspective, this means the usefulness of ‘Corsi Analysis’ to be minimal, at least for the purpose of evaluating players and teams.  For evaluating goaltender workload, as it was initially intended by its originator former NHL goalie and Buffalo goalie coach Jim Corsi, it still has merit.