Jul 162011
 

Last night after news came out that Brendan Morrison had re-signed with the Calgary Flames, Kent Wilson tweeted the following:

Morrison back in Calgary. Check out his corsi tied rating fellow stats nerds: http://bit.ly/q1ywUj

The link is to the Calgary Flames 5v5 game tied corsi ratings which show Morrison had a 0.452 corsi rating (Corsi For %) which was dead last on the Flames.  The problem with jumping to the conclusion that Morrison is bad is two fold:

1.  Corsi generally speaking isn’t good at evaluating players.

2.  One year of 5v5 game tied data is not enough to evaluate players, even with corsi.

Lets take a look at Brendan Morrison over the past 4 years and I’ll show you exactly what I mean.  First lets look just at 5v5 any game score situations.

Season(s) CorF% GF%
2010-11 0.484 0.562
2009-10 0.514 0.627
2008-09 0.498 0.569
2007-08 0.430 0.500
2007-11 (4yr) 0.491 0.577

In each and every year the goals for percentage is significantly higher than his corsi for percentage.  His corsi ratings make Morrison look mediocre at best but his goal ratings make him appear to be quite good.  This isn’t a fluke.  It is occurring systematically, every single season, over 4 seasons in which Morrison played for 5 different teams (Vancouver, Anaheim, Dallas, Washington, Calgary).

Now what about 5v5 game tied situations.  Morrison’s 4 year game tied corsi for percentage is 0.482, his 4 year game tied goal for percentage is 0.592 (which ranks 28th of  217 among forwards with at least 1000 5v5 game tied minutes over the past 4 seasons).

Personally, I’d rather have good goal ratings than good corsi ratings.  Morrison is a good signing by the Flames.

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.

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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.

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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.

Jun 092010
 

Behind the Net Blog recently used even strength when game is tied Corsi analysis to take a look at the divisional imbalance since the lockout and came up with an interesting conclusion.

The NW division is slightly better than the SE division against all shared opponents.  But SE division teams outshot NW teams in head-to-head games.  The difference between the two divisions is negligible, though the NW’s stronger showing against the pacific and central suggests that it’s just a little bit better than the SE.

What that essentially implies is that since the lockout the northwest division is only marginally better than the southeast division, which has generally been considered the worst division in hockey since the lockout.

This though is a perfect example of where Corsi analysis fails because that statement is proven downright untrue when you consider each divisions actual won-loss records.  Against the southeast division the northwest has combined for a dominating 64-31-12 and only twice has a northwest team had a losing record against the southeast (2009-10 Wild at 1-2-3 and 2008-09 Flames at 1-3-1).  The 64-31-12 record is the equivalent to a 107 point team over 82 games which is awfully good.  The southeasts record against the northwest is 43-49-15 which is equivalent to a 77 point team.  To put that in perspective, the NW is like Phoenix (107 points) and the SE is like Columbus (79 points) this past season.  That makes the northwest division more than ‘a little bit better’ than the southeast division.

In another analysis at Behind the Net blog they look at Corsi +/- for teams in games against teams in divisions other than their own.  For the northeast division they came up with:

Ottawa +200, Boston +134, Toronto +65, Buffalo -60, Montreal -266.

That would seem to indicate that Toronto has been a halfway decent team but they finished last in the northwest division in 3 of the 5 seasons and never finished better than 3rd.  Montreal finished ahead of the Leafs in 4 of the 5 seasons and accumulated 49 additional points in the standings despite having an outside division even strength when game is tied Corsi +/- a whopping 331 points below that of the Leafs.    The Minnesota Wild had a very dismal -419 Corsi +/- outside the division but had a respectable 134-106-26 record which is equivalent to a 91 point team.  Now a 91 point team is nothing special, but it is a far cry from what the 3rd worst outside division Corsi +/- would indicate their record ought to be.

In both of these posts the use of Corsi analysis has failed to accurately explain what really happened on the ice and it comes down to the fact that even strength when the game is tied Corsi numbers only tell a fraction of the story.  It doesn’t account for goaltending or power play or penalty kill or shooting ability or any number of other factors that influence who wins hockey games so using it as a tool for determining which teams or divisions are better is a pointless exercise because on the ice, all those other things matter.  The better tool to use in evaluating which teams or divisions are better is the much simpler and more universally understood statistic known as win-loss records.  Win-loss aren’t perfect, but they don’t try to tell me that the Leafs have been better than Montreal since the lockout or that the northwest division is only marginally better than the southeast division.

Jun 032010
 

I am planning that over the course of the summer and into next season I will get back into analyzing hockey statistics more in depth again.  Over the past couple of seasons Corsi numbers have become much more prevalent so I thought I would start off by discussing what they are and my thoughts on them.

Corsi numbers were originally created by former NHL goalie and now Buffalo Sabre goalie coach Jim Corsi.  David Staples recently had a good interview with Corsi which goes into his thought process behind developing Corsi numbers.  The interview is definitely worth a read but let me summarize.

In his role as the Sabre’s goalie coach, Corsi was attempting to evaluate the work load his goalies had in a game of play and found that simply shots against were not sufficient.  The goalie can relax whenever the puck is in the oppositions end, but whenever the play is in his own end he can’t relax, regardless of whether a shot was taken or not.  To get a better idea of his goalies workload he summed up shots, missed shots and blocked shots which should give a much better indication of a goalies overall work load.  A goalie needs a certain skill level to successfully save the majority of shots on goal, but a goalie also needs a certain fitness level (both mental and physical) to be able to play under a certain workload level within a single game and over the course of an 82 game season and this is why Corsi invented the Corsi numbers.

More recently others in the hockey community have extended Corsi numbers to evaluate a teams ability to control the play of a game (i.e. does a team play more in the oppositions zone vs their own) and evaluate individual players by looking at their Corsi numbers for and against while they are on the ice and comparing that to their teammates Corsi numbers.  Most notable are Gabe Desjardins of behindthenet.ca and Gabe and everyone else at the Behind the Net blog but there are others too.  Some people, most notably Matt Fenwick of the Battle of Alberta blog only use shots and missed shots and do not include blocked shots as Jim Corsi does resulting in what is typically called Fenwick numbers.  When used in this context Corsi and Fenwick numbers are calculated just as +/- is calculated which is to take the shots+missed shots+ blocked shots for his team and subtracting the shots+missed shots+ blocked shots numbers by the opposition while he is on the ice.

One of the benefits that many people believe that Corsi numbers provide is that since Corsi numbers include more events (i.e. shots+missed shots+blocked shots vs just shots or even just goals as in +/-) the statistical analysis will be far more accurate due to the larger ‘sample size.’

So what do I think of all this?  I do agree with Jim Corsi that using Corsi numbers as a way to evaluate a goalies workload is probably far more valuable than just using shots on goal.  Beyond that, I am pretty sure that Corsi numbers will give a pretty solid indication of a teams control of the play, for whatever that is worth.  I say for whatever that is worth because some teams, when they have the lead, will choose to play in a defensive shell allowing a lot of shots from the point, but not giving up all that many high quality, in close, shots or worse yet, shots on rebounds. Corsi numbers when the game is close (tied, or within one goal with significant time to play such that the team with the lead has not yet gone into ‘protect the lead’ mode) may give us a better indication of a teams capability to control the play, when they want to but even that may be flawed.  Also, a team with a strong set of forwards but a weak defense and goalie may control the play more than a team with a strong defense and top tier goalie but is that team really any better at winning games?

Much of the same arguments can be made when evaluating players.  Defensive minded players are not necessarily on the ice to control the play, they are on the ice to not allow goals against most typically by the oppositions top offensive forwards.  As mentioned above, one way to accomplish this is to go into a defensive shell and just not give up any quality scoring chances against.  A player can have a sub-par Corsi number, but be doing his job perfectly well.

I do believe that Corsi numbers have a use in evaluating a goalies work load and even in showing which teams are controlling the play, but in my opinion using it anywhere beyond that we are making too many assumptions about how important Corsi numbers are with respect to winning games.  Just ask the Washington Capitals how almost completely controlling the play worked for them against Montreal in round two of the playoffs. In the past I have used mostly goals for/against and shot quality (using shot type and distance as a proxy for quality) to evaluate players and while that has its own inherent flaws as well I will most likely continue to do so in the future.