Jul 232014
 

Tyler Dellow has an interesting post on differences between the Kings and Leafs offensive production. He comes at the problem from a slightly different angle than I have explored in my rush shot series so definitely go give it a read. These two paragraphs discuss a theory of Dellow’s that is interesting.

That’s the sort of thing that can affect a team’s shooting percentage. To take it to an extreme, teams shot 6.2% in the ten seconds after an OZ faceoff win this year; the league average shooting percentage at 5v5 is more like 8%. Of course, when you win an offensive zone draw, you start with the puck but the other team has five guys back and in front of you.

I wonder whether there isn’t something like that going on here that explains LA’s persistent struggles with shooting percentage (as well as those of New Jersey, another team that piles up Corsi but can’t score – solving this problem is one of the burning questions in hockey analytics at the moment). It’s a theory, but one that seems to fit with what Eric’s suggested about how LA generates the bulk of their extra shots. It’s hard for me to explain the Leafs scoring so many more goals in the first 11 seconds after a puck has been carried in, particularly given that I suspect that LA, by virtue of their possession edge, probably enjoyed many more carries into the offensive zone overall.

Earlier today I posted some team rush statistics for the past 7 and past 3 seasons. Let’s look in a little more detail how the Leafs, Kings and Devils performed over the past 3 seasons.

Team RushGF RushSF OtherGF OtherSF RushSh% OtherSh% Rush%
New Jersey 45 540 103 1675 8.33% 6.15% 24.4%
Toronto 66 523 128 1675 12.62% 7.64% 23.8%
Los Angeles 53 609 112 1978 8.70% 5.66% 23.5%

The Leafs scored the most goals on the rush despite the fewest rush shots due to a vastly better shooting percentage (nearly 50% better than the Devils and Kings) on the rush. They do not generate more shots on the rush, but do seem to generate higher quality shots.

The Kings generate by far the most shots in non-rush situations but have the poorest shooting percentage and thus do not score a ton of goals. The Devils don’t generate many non-rush shots and don’t have a great non-rush shooting percentage either and thus posted the fewest goals. The Leafs have had the same number of shots as the Devils but a significantly higher shooting percentage than the Devils and thus scored significantly more non-rush goals.

The Leafs scored 34% of their goals on the rush compared to 32% for the Kings and 30% for the Devils.

Are the Leafs a good rush team? Well, only Boston has scored more 5v5 road rush goals than the Leafs so probably yes but it is mostly because of finishing talent, not shot generating talent. They are 4th last in 5v5 road rush shots.

The Ducks have very similar offense to the Leafs. They don’t get many rush shots but post a really high rush shooting percentage. Anaheim generate a few more non-rush shots than the Leafs but they are very similar offense.

The Kings are a slightly better rush team than the Devils but neither are good and both are weak shooting percentage teams regardless of whether it is a rush or non-rush shot. The Kings make up for this though by generating a lot of shots from offensive zone play where as the Devil’s don’t.

 

Jul 092014
 

I have been pondering doing this for a while and over the past few days I finally got around to it. I have had a theory for a while that an average shot resulting from a rush up the ice is more difficult than a shot than the average shot that is generated by offensive zone play. It makes sense for numerous reasons:

  1. The rush may be an odd-man rush
  2. The rush comes with speed making it more difficult for defense/goalie to defend.
  3. Shots are probably take from closer in (aside from when a team wants to do a line change rarely do they shoot from the blue line on a rush).

To test this theory I defined a shot off the rush as the following:

  • A shot within 10 seconds of a shot attempt by the other team on the other net.
  • A shot within 10 seconds of a face off at the other end or in the neutral zone.
  • A shot within 10 seconds of a hit, giveaway or takeaway in the other end or the neutral zone.

I initially looked at just the first two but the results were inconclusive because the number of rush events were simply too small so I added giveaway/takeaway and hits to the equation and this dramatically increased the sample size of rush shots. This unfortunately introduces some arena bias into the equation as it is well known that hits, giveaways and takeaways vary significantly from arena to arena. We will have to keep this in mind in future analysis of the data and possibly consider just road stats.

For now though I am going to look at all 5v5 data. Here is a chart of how each team looked in terms of rush and non-rush shooting percentages.

Rush_vs_NonRush_ShootingPct_2007-14b

So, it is nice to see that the hypothesis holds true. Every team had a significantly higher shooting percentage on “rush” shots than on shots we couldn’t conclusively define as a rush shot (note that some of these could still be rush shots but we didn’t have an event occur at the other end or neutral zone to be able to identify it as such). As a whole, the league has a rush shot shooting percentage of 9.56% over the past 7 seasons while the shooting percentage is just 7.34% on shots we cannot conclusively define as a rush shot. Over the 7 years 23.5% of all shots were identified as rush shots while 28.6% of all goals scored were on the rush.

In future posts over the course of the summer I’ll investigate rush shots further including but not limited to the following:

  • How much does the frequency of rush shots drive a teams/players overall shooting/save percentages?
  • Are score effects on shooting/save percentages largely due to increase/decrease in rush shot frequency?
  • Are there teams/players that are better at reducing number of rush shots?
  • Can rush shots be used to identify and quantify “shot quality” in any useful way?
  • How does this align with the zone entry research that is being done?

 

 

Jun 122014
 

The rumour is out there that Sunny Mehta has been hired as Director of Hockey Analytics of the New Jersey Devils (if true, a big congrats to Sunny). This sparked some twitter discussion about the Devils and analytics and Devils defensemen including Bryce Salvador.

I have been a bit of a fan of Salvador, at least statistically, though clearly there are a lot of Devils fans that do not like him and I think it is because of a focus on corsi. One person tweeted me an image of Salvador’s corsi rel % suggesting it was “pretty ugly”. While maybe true the game isn’t about Corsi it is about goals. Here is what I know about Salvador. In 5v5close situations he led the Devils defensemen in on-ice save percentage last season, the season before, and the season before that. He missed 2010-11 due to injury but in 2009-10 he was second best trailing only Andy Greene, his regular defense partner. Either he is extremely lucky (every year) or he is doing something right.

Lets look at this a different way. Over the past 3 seasons Bryce Salvador has had the third best 5v5close save percentage in the league when he is on the ice despite the Devils ranking 23rd in team save percentage. The two players ahead of him play for Boston (Dougie Hamilton) and Los Angeles (Willie Mitchell) who have significantly better goaltending (3rd and 8th best 5v5close save percentages over past 3 seasons) and again, they played in front of far better goaltending.

In February 2012 I wrote an article attempting to quantify a defenders effect on save percentage and in it I identified Salvador as one of the best defensemen at boosting his teams save percentage. In the 2 seasons since he has done nothing but support that claim.

So, what does this all mean? Well, it takes a player who had a team worst 15.9 CA/20 in 5v5close situations this past season to a team best 0.49 GA/20.  Over the past 3 seasons only Dougie Hamilton (Boston), Willie Mitchell (Los Angeles) and Alec Martinez (Los Angeles) have seen goals scored against them at a lower rate than Bryce Salvador.

I know the majority of people are on the corsi bandwagon these days and some will dismiss any argument that runs counter to it but I think the evidence is clearly on Salvador’s side here. All evidence suggest he is really good as suppressing opposition shot quality and in turn suppressing the number of goals scored against the Devils. If I were the new Director of Hockey Analytics for the Devils I wouldn’t be recommending getting rid of Salvador.

 

Aug 072012
 

I am a firm believer in shot quality.  I have probably looked at it a dozen different ways and it seems pretty clear to me that it exists and yet there are still a lot of doubters out there so I wanted to take yet another opportunity to show that show quality exists and give an opportunity for the shot quality deniers to tell me what I am doing wrong if I am doing something wrong.

First lets start off with a definition of shot quality to make it clear what I am referring to.  Some people refer to shot quality as shot location (i.e. shot distance) but to me shot quality is even more basic than that.  For me for shot quality to exist it must only be shown that one group of shots is more difficult to save than another group of shots after taking into account game situation (i.e. 5v5 vs PK vs PP), score effects, and any factors that might affect shot quality.

So, let me take 2 groups of shots.

Group 1:  All the shots taken by the Penguins when Sidney Crosby is on the ice during 5v5 close zone start adjusted  (eliminating first 10 seconds after a face off) play over the past 5 seasons.

Group 2:  All the shots taken by the Penguins when Tyler Kennedy is on the ice during 5v5 close zone start adjusted  (eliminating first 10 seconds after a face off) play over the past 5 seasons.

I am using 5v5 close situations so that eliminates any situation and score effects that might influence shot quality.  I am also eliminating the shots within 10 seconds of a face off because they have been shown to be far easier shots to save and we wouldn’t want to disadvantage one above groups if it had a disproportionate number of shots immediately after a face off.

Also, since both players have played the past 5 seasons with the same team it eliminates any scorekeeper bias with their shot counts since both have played behind the same score keepers.  So, let’s look at the numbers.

Group GF SF Sh% Chance
Group 1 (Crosby) 150 1292 11.61% 1.65%
Group 2 (Kennedy) 92 1166 7.89% 1.23%
Totals 242 2458 9.85%

The Sh% column looks pretty telling on its own, but the Chance column says it all.  The Chance column is the liklihood that the groups shooting percentage could be what it was based on luck alone using a binomial distribution with a real shooting percentage of 9.85%.  In short, it is very unlikely that Crosby and Kennedy would have those on-ice shooting percentages based on luck alone.

Truth be told, for these two players score effects would have very little effect on their relative stats since they played almost equal time leading as trailing so we could increase our sample size fairly significantly if we looked at 5 year zone start adjusted 5v5 shots and goals.

Group GF SF Sh% Chance
Group 1 (Crosby) 243 1964 12.37% 0.17%
Group 2 (Kennedy) 150 1837 8.17% 0.09%
Totals 393 3801 10.34%

So essentially there is no chance that that happens based on luck alone.  Shot quality of some sort is a factor.  There is definitely show shot quality skill/talent factoring into the equation.

So my question to shot quality deniers is as follows.  If the above is not evidence that shot quality exists, why not, and if it is evidence of shot quality, why are you still in denial that shot quality exists?

 

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.

 

Mar 182011
 

The guys over at Behind the Net have initiated a ‘prove shot quality exists’ competition and in response to that Rob Vollman took a quick and dirty look at shooting percentage suppression.  As I showed the other day, Rob’s logic was a little off.

Rob started off by identifying a number of players with high on ice save percentages over the past 3 seasons.  Some of these guys included low minute players mostly playing on the fourth line against other fourth line caliber players, but there were a handful of players who played relative significant number of minutes and still put up good on ice save percentages.  Let me remind you of a few names that Rob identified:  forwards Marco Sturm, Manny Malhotra, Tyler Kennedy, Travis Moen, Taylor Pyatt, Michael Ryder, defensemen Kent Huskins, Sean O’Donnell, Mike Weaver, Mark Stuart.  I’ll get back to these guys later but I’ll claim that Rob dismissed some of them prematurely by claiming they played against weak competition.

As you may or may not know I have developed offensive and defensive ratings for every player and these can be found at http://stats.hockeyanalysis.com/ Furthermore, I have created these using goals for/against as well as shots for/against, fenwick for/against, and corsi for/against.  For clarification, fenwick is shots + missed shots while Corsi is shots + missed shots + blocked shots.  For this study I decided to use fenwick instead of shots because I had the data handy and I was too lazy to get the shot data in the right format but there shouldn’t be a significant difference (the two are very highly correlated).

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Mar 162011
 

I have posted a few articles here recently about the existence of shot quality, one of which related to last seasons Washington Capitals and one related to how shot quality varies according to game score but there are still shot quality deniers out there.  One of the comments I received from a shot quality denier to those posts was as in depth as “You did it wrong” but offered no further explanation.  So there it stands.

Derek Zona and Gabe Desjardins over at Behind the Net Hockey (mostly shot quality deniers) have put up a $150 prize for anyone who can show that shot quality exists.  One method they suggested one could pursue to prove such a thing was the following:

Are there players or teams with the ability to drive or suppress on-ice shooting percentage?  What are their characteristics?

This prompted Rob Vollman (who I presume is a shot quality denier, my apologies if not) to look into just that and to do so he identified a group of players who had the highest save percentage against while they were on the ice.  The theory is, if shot quality suppression was a talent then there should exist players who experience a very good save percentage for their team while they are on the ice.  The group of players identified varied significantly from George Parros to Kyle Wellwood to Sean O’Donnell to Marco Sturm.  In the end Rob came to the conclusion that these players all had high save percentages while they were on the ice because they mostly played against weaker quality of competition.

But none of them are facing their team’s toughest minutes.  If they truly had the ability to suppress shooting percentage, why would Kesler and Burrows hop out against Ovechkin instead of Malhotra?  Why would Pronger keep an eye on Crosby instead of O’Donnell?  Kudos to each of them for playing their roles very well, but the explanation still appears grounded in Quality of Competition.

And there is the fault in logic.

  • Claim:  Shot quality doesn’t exist.
  • Counter-evidence:  Some players do experience higher save percentages while they are on the ice.
  • Rational:  They do so because they play against weaker quality of competition.
  • Claim Confirmed:  Phew, my claim that shot quality doesn’t exist remains valid.

Now the whole problem with that theory is the rational part because the rational part requires shot quality to be real for it to be true.  The only way you can have a better quality of competition (in terms shooting/save percentage) is to have shot quality exist.  If shot quality didn’t exist all competitors would have the same level of shooting percentage talent.  The claim and rational can’t both be true, so the logic fails.

And that is where identifying shot quality becomes difficult.  Players that are generally good at reducing the quality of shots against are lined up against opponents who are generally good at creating quality shots for.  The net result is their talents cancel each other out to some extent making it difficult to identify shot quality driving/suppressing talent just by looking at the numbers in isolation of who they are playing with and against.

Mar 152011
 

I thought this debate had been fully hashed out already but apparently some people still don’t believe that the game score has an impact on shooting percentage (and shot quality).  The following table shows the shooting percentages by game score over the past 3 seasons (2007-08 to 2009-10) during even strength situations where neither goalie is pulled for any reason (including delayed penalty situations).

Situation Shots Goals SH% Prob<= Prob>
Down2+ 23650 1852 7.83 0.3794 0.6206
Down1 30447 2356 7.74 0.1696 0.8304
Tied 60753 4427 7.29 0.0000 1.0000
Up1 26842 2288 8.52 0.9999 0.0001
Up2+ 19351 1779 9.19 1.0000 0.0000
Overall 161043 12702 7.89 0.5024 0.4976

The Situation, Shots, Goals, and SH% columns are self explanatory.  As you can see, shooting percentage is at its lowest in game tied situations, increases slightly for teams that are trailing and increases significantly for teams that are leading.

The second last column titled Prob<= show the probability (according to a binomial distribution) that that number of goals or fewer would be scored on that number of shots if the expected shooting percentage was 7.89%, the same as the overall 5v5 shooting percentage.  The last column titled Prob> is simply 1-Prob<= and shows the probability of getting more than that number of goals on that number of shots.  So, in down 2+ goal situations, there is a 37.94% chance of their being 1852 or fewer goals scored on 23650 shots which indicates that the down2+ shooting percentage isn’t different from the 5v5 mean at any reasonable confidence level.  The same conclusion can be drawn about down1 situations.  But, the shooting percentages in game tied, up1 and up2+ situations are statistically different at an extremely high confidence level.  Essentially there is zero chance that game tied, up1, or up2+ situations have the same natural shooting percentages as game overall 5v5 situations.  In no way can luck be the sole reason for these differences.

So, does this conclusively tell us that shot quality exists and varies according to game score?  It probably does, but I can’t say it is conclusive as it could mean that teams that trail a lot have bad goaltending (the reason they are trailing) and this results in the team leading having an inflated shooting percentage.  So, what if we looked at shots against a particular team.  Let’s say, for example, against the NY Rangers.  Here is what that looks like.

Situation Shots Goals SH% Prob<= Prob>
Overall 5159 386 7.48 0.5135 0.4865
Up1 843 73 8.66 0.9116 0.0884
Up2+ 485 46 9.48 0.9571 0.0429
Leading 1328 119 8.96 0.9800 0.0200
Tied 2004 138 6.89 0.1658 0.8342

I chose the Rangers because they use predominantly one goalie and that goalie is generally speaking a quality goalie.  As you can see, the confidence levels aren’t quite as strong as league wide mostly because of the smaller sample size but if we combine the up1 and up2+ categories we can say that shot quality against the Rangers when the opposing team is leading is statistically different than shooting percentage against the Rangers overall.

If you are interested in seeing what happens with a team that has had chronically bad goaltending, here is the same table for the Maple Leafs.  We see the same sort of things.

Situation Shots Goals SH% Prob<= Prob>
Overall 5309 491 9.25 0.5120 0.4880
Up1 938 94 10.02 0.8098 0.1902
Up2+ 906 100 11.04 0.9698 0.0302
Leading 1844 194 10.52 0.9712 0.0288
Tied 1985 149 7.51 0.0034 0.9966

So what have we learned.

  1. Shooting percentages vary according to game score.
  2. Those shooting percentage differences can’t be attributed to luck.
  3. Those shooting percentage differences can’t be attributed to goaltending.

That means, it must be the quality of the shots that varies across game scores.  In short, we can conclude that when teams get down in a game they open up and take more chances offensively which in turn gives up higher quality shots against which makes perfect sense to me.

When we combine this with my previous post on the Washington Capitals shooting percentage last season, it is probably safe to assume that shot quality exists and we can’t safely assume that all shots can be treated equal in all situations.

Aug 132010
 

Who is the best Shooter in the NHL?

If you were asked, who is the best shooter in the NHL you might answer Alexander Ovechkin since he has been the most prolific goal scorer since the lockout.  What Ovechkin also always does though is take far more shots than anyone else resulting in a shooting percentage that is for more ordinary.  This past season he was 50th in overall shooting percentage and in 2007-08 he was 46th and those are the only two times he cracked the top 50.  So is Ovechkin a great shooter, or simply great at finding opportunities to shoot?  And if Ovechkin isn’t the best shooter, who is?

Shooting percentage is a very common statistic which essentially is just goals scored divided by shots taken.  We all know and understand that.  Corsi numbers were initially conceived by former NHL goalie and current Buffalo Sabre goalie coach Jim Corsi as a method of evaluating goalie fatigue and has since become a frequently discussed statistic among hockey stat nuts, particularly those at Behind the Net.  Essentially what Corsi takes into account is shots directed at the net, not just shots on the net.  So, Corsi also takes into account missed shots (i.e. shots that go wide) and blocked shots (i.e. shots blocked by a defender).  Corsi numbers are often considered a good indicator of which team controls the play more (if you control the play you will get more shots and shot attempts than your opponent).  Corsi numbers were then revised by Matt Fenwick from the Battle of Alberta blog to not include blocked shots as it was found that including blocked shots in Corsi numbers correlation with winning percentage.  So it came to be that shots plus missed shots are generally referred to as Fenwick numbers and shots plus missed shots plus blocked shots are generally referred to as Corsi numbers.  That is the terminology I will use here.

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