Even Strength Shooting Percentage

To what extent is team-to-team variation in even strength shooting percentage the product of random variation? I'm not sure what the answer is, but I suspect that the contribution is substantial. I've included several graphs below in order to illustrate this. The table below the first graph contains the data upon which each distribution is based.



The first graph. The yellow line is the actual spread in EV ( note: 5 on 5 only) shooting percentage that exists among NHL teams at this point in the 2008-09 NHL season.

The X-axis contains the percentage 'categories' in which the figure listed is the midpoint value of the category.

They Y-axis is the relative frequency of each individual percentage 'category'.

As an example, 6 teams in the NHL this year currently have an EV shooting percentage that is between 0.08 and 0.085. As there are 30 teams in the league, the relative frequency is 0.2 ( as 6/30 = 0.2). The midpoint value for this category is 0.0825. Therefore, the relative frequency of the '0.0825' category is 0.2.

The pink line shows the predicted spread in EV shooting percentage if each team had the exact same underlying shooting percentage at ~0.085 ( i.e. the league average 5-on-5 shooting percentage). This was determined through the following.

1000 "seasons" were simulated.
For each "season", each team has an artificial shooting percentage.
This percentage is the number of goals that a team scores over x number of trials.
The number of trials is equivalent to the number of EV shots that the team has taken through this point in the season.
The probability of "scoring" in each individual trial is the same for every team at 0.085.
Therefore, any team-to-team variation will be the product of randomness.

A specific example will hopefully make this clear.

Philadelphia has taken 984 shots at EV at this point in the 2008-09 season. Therefore, Philadelphia has 984 trials. The probability of scoring in each individual trial for Philadelphia is the league average EV shooting percentage at ~0.085. In Philadelphia's first "season", they scored 107 times. As 107 / 984= ~0.109, Philadelphia's EV shooting percentage for their 1st "season" is 0.109. I then did this for every team and repeated the process 100 times (i.e. simulated 100 seasons). Here's how the first 48 or so shaped out:




Even though the probability of a goal on any given "shot" is 0.085, the artificial shooting percentage will necessarily differ from 0.085 due to insufficient sample size. While it goes without saying, as the sample size (number of trials) increases, any given team's artificial shooting percentage will more closely approximate 0.085. Therefore, for teams that have taken more shots through this point in the 2008-09 season will have more "trials". The spread in shooting percentage for these teams will be lower due to them having a greater number of trials. For example, the standard deviation for Detroit's 100 seasons is ~0.007. By comparison, the same value for Pittsburgh is ~0.009.

The same rules regarding the x and y axes that apply to the yellow (actual) distribution also apply to the pink (random) distribution. The relative frequency for the pink distribution is the proportional representation of each artificial shooting percentage category. As an example, as there were 100 "seasons" and 30 teams, the entire sample consisted of 3000 artificial shooting percentages. 601 artificial percentages fell between 0.08 and 0.085. The relative frequency for the '0.0825' category is therefore ~0.2, as 601/3000 = ~0.2.


As many will note, the spread between the worst ( NYI at 0.069) and best ( BOS at 0.108) teams appears to be sizable, as is indicated by the breadth of the yellow distribution.

However, the pink distribution is itself fairly broad. In fact, it very closely resembles the yellow distribution. As would be anticipated, the yellow distribution is slightly broader than than its counterpart, but the difference is not large. This suggests that much of the inter-team variation in EV shooting percentage is the result of randomness.


The second graph, shown above, contains a 'smoothed' version of the actual distribution, which is represented by the dark line. The average shooting percentage in the league is currently ~0.085, as has been mentioned. The standard deviation is currently ~0.01. The dark graph is simply a normal distribution (bell curve) with a mean of 0.085 and standard deviation of 0.01.

The light line is merely the pink distribution reproduced. Again, the two distributions are very similar to one another.

The fact that the actual distribution is somewhat broader than the expected distribution shows that teams do indeed differ in their underlying shooting percentage at EV. Nonetheless, this variation is only very slightly larger than what would be predicted by chance alone. The underlying differences appear to be minimal.

Vic Ferrari has done a lot of excellent, excellent work over at his site that is similar to this. Much of his work has examined the ability of individual players to influence shooting and save percentage while on the ice. His findings are comparable in that the vast majority of inter-individual variation seems to be due to random variation.

EDIT: I've included some supplementary data tables for the purposes of clarity.

I should mention that the data I used for this post was obtained at behindthenet -- an awesome site that I highly recommend. Without it, this post wouldn't have been possible.

The Bruins

The Bruins have been one of the surprise teams this year, what with them having the best record in the league at the halfway point (few would have predicted this to be so). They also have the best goal differential, so it's not as if they've been lucky in the conventional sense by winning a lot of close games. However, just because a team's record is proportional to its goal differential doesn't necessarily mean that it hasn't been lucky.


This is a chart showing how the Bruins have fared in various game situations so far this season. The numbers are as of 01/07/08. A couple things can be said about these numbers:

1. The Bruins success appears to largely be a product of the percentages. They have the best shooting percentage in the league, as well as the best save percentage. This also holds true at even strength.

2. For a team with such a good record and goal differential, the Bruins are anomalous in that they're pretty average with respect to shot differential. In fact, they get outshot on average.

The Percentages

The problem for Boston is that there isn't a great deal of repeatability in terms of the percentages, particularly at even strength. This post by Tyler at mc79hockey demonstrates how the sum of a team's even strength shooting percentage and its even strength save percentage tends to regress to 100 as the season progresses. The Bruins currently sit at ~105. If I was a betting man, I'd place money on that figure significantly decreasing by April.

Are Boston's percentages at all sustainable?

We know from past posts that, while fluctuations in the percentages do indeed have little sustain in the future, a team is able to reliably influence its shooting/save through shot quality. Shot quality is moderately correlated with the percentages and is substantially reliable. Thus, over a sufficiently large sample of games, there would still likely be team-to-team variation in the percentages, with this effect being mediated by shot quality.

In past seasons, the team that leads the league in shot quality for typically has a shot quality index of roughly 1.1. That is, that team takes shots that, on average, are 10% more likely to result in a goal than the average team.

Conversely, the team that leads in the league in shot quality against typically has a shot quality index of roughly 0.9. That is, that team allows shots that, on average, are 10% less likely to result in a goal against than the average team.

If we make the very conservative assumption that Boston currently leads the league in both shot quality for and shot quality against, then we can estimate the Bruins' expected shooting percentage based on these adjustments.

Expected shooting percentage = shot quality for index * league average shooting percentage
Expected save percentage = 1- ( shot quality against index * league average shooting percentage)

League average shooting%: 0.0917

Boston's expected shooting percentage: 1.1*0.0917 = 0.10
Boston's expected save percentage: 1-(0.9*0.0917)= 0.917

Boston's actual shooting percentage: 0.118
Boston's actual save percentage: 0.93

Therefore, even if we assume that Boston currently leads the league in both shot quality for and shot quality against, the Bruins' have still outperformed their expected shooting percentage and expected save percentage. While far from constituting definitive proof of good luck, it is suggestive of it.

In actuality, the Bruins have not been leading the league in either shot quality for or in shot quality against. Hockeynumbers tabulates data on shot quality that is periodically updated throughout the season. While the data is only available for specific game situations (EV, PP, SH), figures for overall shot quality can be obtained by dividing each team's expected goals for/goals against by their corresponding shots for/shots against total, and then expressing the resulting figure relative to the league average.

In addition to having a negative shot differential, the Bruins are below average in both shot quality for and shot quality against, thus making it even less likely that they'll replicate their impressive shooting/save percentage in second half. Indeed, Boston is in the red in terms of its expected goal differential, as is nicely illustrated here

Boston's 'true' even strength shooting/save percentage

As displayed in the table at the beginning of the post, the fact that Boston has managed to lead to the league in both shooting and save percentage is largely tied to even strength play -- that is, the overall percentages are largely being driven by the exceptional even strength percentages. Therefore, the sustainability of Boston's overall percentages is critically contingent upon sustaining its high percentages at even strength. Boston's shooting/save percentage at even strength will likely fall to something more reasonable by the time the season has ended. At the same time, however, it's unlikely that its EV shooting/save percentage is merely average.

To illustrate this, assume that Boston's true underlying even strength shooting percentage is exactly league average (~0.084), with the same holding true for its even strength save percentage (~0.916). The Bruins have taken 890 shots at even strength so far this season, while allowing 920. If a team with a true EV shooting percentage of 0.084 takes 890 shots, the probability of shooting 0.109 or better by chance alone is remote (about 4 times per thousand). Likewise, if a team with a true EV save percentage of 0.916 has 920 shots against, the probability of having a save percentage better than or equal to 0.939 by chance is equally minuscule (about 5-6 times per thousand). Thus, Boston's underlying EV shooting/save percentage -- while almost certainly lower than what they've attained thus far -- is probably above average. Therefore, a complete regression to the mean is unlikely.

The First Goal

In hockey, scoring the first goal is important. Last season, every single team in the league had a better record in games where they scored first compared to games where they did not.

However, one has to wonder: is scoring the first goal as important it's made out to be? For example, hockey media types love to harp on the importance of scoring first, invariably citing team A's record when managing to do, or how team B's losing streak is explicable through its tendency to surrender the lead early in the game. Not only does this emphasis conflate cause and effect, but it's insufferably repetitive and trite. One would intuitively expect the team that scores first to have a higher probability of winning, and it's fairly obvious that such a relationship exists. In fact, I suspect that the probability of winning when scoring first is not significantly different than what would otherwise be expected on a mathematical basis.

Moreover, scoring in general is important, whether it be the first goal of the game or the last one. Any given goal is more or less significant and potentially determinative of the game's outcome. To make the distinction between the first goal and any other goal scored during the game just smacks of arbitrariness. Scoring first is probably more correlated with winning than, say, scoring second, but I'd be surprised if the difference was large, and shocked if it was large enough to warrant the special attention.

Thus, this post seeks to answer two questions:

1. When a team scores scores first,what percentage of the time does it win the game? Is this value any different from what probability theory predicts it to be?

2. How much more important is the first goal than the second goal?

The first question can be answered through application of the poisson distribution. As Alan Ryder explains in this paper, goal scoring in hockey is essentially a poisson process. Ryder has determined that a team's probability of winning by z goals in regulation at any particular point during the game can be found through application of the following formula in Microsoft excel.

Pr(Win by z) = EXP(-(mt+vt)) * (mt/vt)^(z/2) * BESSELI(2*SQRT(mt*vt),ABS(z))

where:

m = that team's average goals for per game*
v = that team's average goals against per game*
t = the time remaining in regulation divided by 60
z = the margin of victory

* - Adjusted goals ought to be used here as they provide the best measure of a team's true ability to score and prevent goals.

Through use of this formula, the theoretical probability of winning in regulation for a team that scores first can be determined.

In the 2007-08, there were 1222 games that had at least one goal scored in regulation. On average, the first goal was scored just prior to the 12 minute mark of the 1st period. Thus, our value for t is 0.803.

The m and v values are, for the purposes of the formula, 2.639 and 2.545. These figures are adjusted to reflect the following:

1. That the team that scores first is, on average, slightly better than the average team.
2. That the team that gives up the first goal is, on average, slightly worse than the average team.
3. That the home team is more likely to score the first goal.

For determining the probability of winning, the z value ranges from 0 to sufficiently high n (~10), as the team that scores first must only maintain the existing margin -- or increase it -- in order to win. For the probability of losing, z is -2 to sufficiently low n (~-10), as the trailing team must outscore the opposition by 2 or more goals in order to win. For the probability of a tie, z is -1, as this will restore the original margin of zero.

Thus, all of the necessary input variables having been determined, the theoretical probability of winning when scoring first can be computed. Below is a comparison of the theoretical probability against the actual probability.



As can be seen, the actual and theoretical probabilities more or less mirror one another, save for the fact that probability theory predicts ties to occur less frequently than they actually do. This reflects the fact that teams do, to some degree, play to the score, particularly as the end of regulation nears. The upshot of there being more ties in reality is that the first goal is somewhat more valuable than what the values expressed in the chart would otherwise indicate. For example, while the actual probability of winning is nominally lower than its theoretical counterpart (0.599 vs 0.615), if one examines only those games resolved in regulation, the actual probability of winning is slightly higher than the theoretical probability (0.764 vs 0.744). Nonetheless, the important part is that the theoretical and actual values are essentially equivalent to one another. If the actual probability of winning was substantially higher than the theoretical probability, the large amount of emphasis placed on scoring first may be justifiable. However,the fact that they are virtually the same means that the advantage conferred by scoring first is neither surprising nor contrary to expectation, thus making it unworthy of mention.

What about the importance of the second goal vis-a-vis the first goal?



Scoring second is very nearly as highly correlated with winning as scoring first. And yet, it is the latter that -- rather unfairly -- receives all of the attention. In this sense, the emphasis that's placed on scoring first seems more than a little arbitrary. It's simply not very accurate to accord the first goal special status when, in actual fact,the vast majority of goals scored throughout the course of a hockey game are significant.

Worst post-67 Cup Winning Team?

The columns in the above list show, from left to right,  the season,  the cup winning team during that season, that team's adjusted winning percentage (AW%) during the regular season, and how that team ranked in the league in terms of AW% during that season.   The teams that I've highlighted are teams that I feel are arguably the worst post-67 teams to win the cup,  or teams that are generally included in that discussion by others.

A few general comments:

1.  Those Hab teams of the late 1970s were very,  very good.
2.  The Oilers dynasty teams,  despite putting up some gaudy offensive totals,  don't appear to be much better than the average cup winning team.
4.  The 89' Flames were probably the best non-dynasty team of all time.

Now, the analysis:

The 91' and 92' Penguins

 While their AW% is pretty unspectacular for a team that managed to win the cup two consecutive years,  a lot of this probably has to do with the fact that Lemieux only managed to play 90 regular season games in total during those two seasons.    That probably explains the regular season success/playoff success discrepancy.    With a healthy Lemieux,  neither of those teams are close to being the worst post-67 to win it all.   Not even remotely.

The 86' Canadiens

Contrary to popular belief,  the 86' Canadiens were not a mediocre team that Roy carried to the cup.   Despite receiving average goaltending for the majority of the regular season (sv%=0.873),  they had the third best AW% in the league.   Considering that they,  rather fortuitously,  managed to avoid playing both the Oilers and the Flyers during their road to the cup,  it's not really surprising that they managed to win.    Not the worst post-67 team to win the cup.

The 95' Devils

Admittedly,  their regular season numbers were pretty underwhelming,  finishing 10th in a 26 team league in AW%.   However,  a lot of this,  I think,  had to do with bad luck.   They were averaging 30.1 SF/G and 25 SA/G during the regular season and, despite playing tight defensive hockey,  had a team save percentage of only .901.   Presumably,  they just weren't getting the bounces.   All of this was to change in the playoffs,  though.   They went 16-4,  scored 67 GF while allowing 33,  averaged 30.4 SF and 23.2 SA,  all the while starting every series on the road against tough competition (DET, PHI, PIT, BOS).   Highly impressive.

The 93' Canadiens

Like the 86' team,  the 93' Habs benefited from not having to play the truly elite teams during their cup run (PIT, DET, CAL, BOS).   The difference is that the 93' team was much more reliant on goaltending and luck (12-1 in one goal games) to do it.    Also,  their regular season was fairly mediocre by cup-winning standards.    Still,  they only managed to lose 4 games en route to winning.    Probably not the worst post-67 cup winner,  but we're getting warmer.

The 90' Oilers

I don't know too much about this team,  but the fact that Ranford won the Conn Smythe suggests that they,  like the 93' Canadiens,  were pretty dependent on goaltending.   However,  they were still the 5th best team that year and were only one year removed from their dynasty.    Not the best post-1967 cup winner by any stretch of the imagination, but not the worst ceither.

The 04' Lightning

The 04' Lightning were one of the best teams in the league during the regular season.   While one might point out that they played in the league's worst division that year,  AW% takes schedule difficulty into consideration.   They were the 4th best team despite regularly playing the likes of FLA,  ATL,  WAS,  and CAR.    Their shot differential was impressive too (30 SF/G, 25.3 SA/G),  so it's not as if their success was being driven by the percentages.   Why,  then,  have I chosen to include them in the discussion?    Well,  that team was extraordinarily fortunate on the injury front that year.   They only lost some 35 man games to injury that year,  the majority of which belonged to Andre Roy.    It can be safely assumed that this had a lot to do with their success that season,  and the fact that they were fairly average in both the following and preceding seasons lends support to this.   Still,  they're not the worst.

The 06' Hurricanes

It's no secret that the Hurricanes were the recipients of tremendous good fortune in terms of their opponents sustaining bizarre and debilitating injuries to key players all throughout the postseason.   Injuries to Koivu,  Roloson,  and virtually the entire Sabres defence contributed more to that victory than any single Hurricane player.   What will surprise most,  though,  is how ordinary Carolina was during the regular season that year.    While their 112 points might give the impressive that they were an elite team,  this was largely the product of: a) playing one of the easiest schedules in the league b) doing well in the shootout and c) outperforming their goal differential by winning close games.   Their AW% was 13th in the league --  barely above average.   This was,  without question,  the worst post-expansion team to win the cup.


EDIT: As requested,  here are the Top 50 post-expansion teams according to AW%.

Parity

Since the 2005-06 season,   there’s been a lot of talk in the media about the amount of parity that currently exists in the NHL. While I’m inclined to agree with this,   I have a feeling that people are simply looking at the (presumably diminished) spread in point totals and making their conclusions on that basis.    This is,   of course,  completely misguided and incorrect.

Points totals themselves are not necessarily indicative of reduced parity.    For in order to measure parity,   you first have to measure team strength,   and point totals do not adequately measure team strength.

To be sure,   point totals are correlated with team strength.    Hockey would be a very strange game if this were not true.    However,  there are certain problems with point totals that preclude its use as a proxy for team quality.

For one,   points totals are influenced by overtime and shootout success,   and I would argue that overtime and shootout success have very little to do with how strong a team is.    When I use the term ‘team quality’,   I’m referring to how good a team is at actually playing hockey.    And when I use the term ‘actually playing hockey’,  I’m basically referring to how good a team is at winning in regulation.    The distinction between regulation and extra-regulation results might seem arbitrary at first,   but there's good reason for it.    For one,  overtime and shootout success has almost nothing to do with regulation success.    Observe:



Moreover,  extra-regulation results are not very repeatable across seasons, especially compared to regulation results.



The fact that extra-regulation results have virtually nothing to do with regulation results and have little to no repeatability suggests that they are largely the product of randomness.    If something is largely random,   then it cannot be thought of as an underlying ability.    And if something cannot be thought of as an underlying ability,  then it ought not to be part of a metric that ostensibly measures team strength.    And yet,   shootout and overtime success does have a sizable affect on point totals. Hence,  my reluctance to use point totals as a metric for team strength.

However,   the inadequacy of point totals goes much deeper than this.   Even before the advent of 4-on-4 overtime and the shootout, points were not the best metric for team strength.    The reason for this is that point totals only reflect wins and losses while completely ignoring the margin of victory.    If there are two teams with similar point totals, one of them tending to win convincingly and lose narrowly,   the other tending to win narrowly and lose convincingly,   then the former team is,   in almost all cases,   the better team.    The concept is an intuitive one.    If you disagree with the assertion that a team’s goal differential better conveys its ability relative to its point total or place in the standings,  then you’re probably at the wrong site.

Granted,  goal differential per se,   while better than points,   is not the best available metric.    Several corrections need to be made to it for this to be true.    Firstly,   shootout and empty net goals should be excluded from the totals,   as they provide no useful information.   Secondly,  raw goal differential is problematic in that not all teams play identical schedules.   Some teams,   usually by virtue of playing in a stronger division or conference,   are burdened with a more difficult schedule than average.    If you thought that the 2005-06 Phoenix Coyotes and the 2005-06 Carolina Hurricanes had equally difficult schedules,  then you would be mistaken.    Thus, some attempt should be made to correct for schedule difficulty.   Finally,  it is not so much a team’s absolute goal differential that is important,   but its GF-GA ratio.    A team that scores 200 goals and concedes 100 is better than one that scores 400 and gives up 300.    Furthermore,  simple goal differential is too sensitive to scoring context for it to provide any useful information on league parity,   as it would lead to the spurious conclusion that there was less parity in higher scoring seasons.    These two problems are avoidable by using each team’s Pythagorean expectation instead–  essentially,  its theoretical winning percentage determined through the following calculation:

(Adjusted goals for)^2 / [(adjusted goals for)^2 + (adjusted goals against)^2]

The resulting metric can be termed adjusted winning percentage.

AW% is important as provides us with a suitable metric for assessing team strength.   By computing the standard deviation in AW% in any particular season,  we’re essentially measuring parity.

What,  then,  does AW% tell us about the amount of parity in the NHL over the last ten years?


A few comments.    Firstly,  parity in the pre-lockout NHL was pretty invariant on a year to year basis (mean: 0.094, ST DEV: 0.008).    Only 1996-97 is anomalous,   with all of the remaining values falling between 0.092 and 0.101.    Secondly,   there is clearly more parity (read: the standard deviation in AW% is smaller) in the post-lockout NHL relative to the pre-lockout NHL.    The difference may not seem like much,   but the 2005-06 and 2006-07 values are separated by one SD from the pre-lockout mean.    The value for 2007-08 is 4 SD(!) from the pre-lockout mean.    That's a fairly significant difference.

Parity in the new NHL seems to be more reality than fiction.    Teams really are less separated in ability now compared to five or ten years ago.    I find this interesting as the purpose of having the shootout and three point games seems,  to me,  like a ploy designed by the NHL with the intention of creating the illusion of parity.   However,  the fact that the new NHL is characterized by genuine parity has in some sense obviated this purpose.    That considered,   perhaps the NHL should do away with three point games and the shootout.   I certainly wouldn't complain.

Shot Quality Part Two: Shot Qualify For

In previous post,   I analyzed the utility of shot quality against [hereafter SQA].   The purpose of this post is to explore the utility of its counterpart -- shot quality for [SQF hereafter].   Now, if the data for SQA is reliable and valid,  then the data for SQF ought to be as well.    It just wouldn't make sense otherwise.    Nonetheless, I think that it's necessary to show that SQF is a legitimate construct in its own right,   not only to further affirm the utility of shot quality as a whole,   but to allow SQF to be identified as one of the main components of team offence.

The fact that shot quality is fraught with arena bias is no less relevant to SQF than it is to SQA.    One way to get around this is to simply use the data on road shot quality.    The problem is that, of the three seasons of data for SQF (2003-04, 2005-06, 2006-07),  only one decomposes the information into home and road situations.   Nonetheless,   the 2006-07 data allows for estimations of the degree of bias present at each arena.    These estimations of bias can then be used to adjust the 2003-04 and 2005-06 data so as to reduce the distortion.    The result is what I've termed 'adjusted shot quality' -- a relatively bias-free estimation of the relative dangerousness of shots that a team directs at the opposition net.

Adjusted SQF is a clearly superior metric to unadjusted SQF.    For one,  it has more construct validity.    If the shot quality data is actually measuring what it purports to,   then there should be a positive correlation between SQF and shooting percentage at the team level.   As the graph below shows,  the correlation between save percentage and SQF is higher for the adjusted SQF data than it is for the unadjusted data.



But what about reliability?    It goes without saying that the values for unadjusted SQF are going to be reliable on a year to year basis due to the element of arena bias.    However, will the same be true of adjusted SQF?    That is to say, once the effect of arena bias is removed, is SQF still a reliable phenomenon?


[In the event that the information that the graph is supposed to convey isn't apparent,   the blue columns show the correlation between a team's SQF in 2005-06 and a team's SQF in 2006-07.    The red columns show the same correlation, except for the years 2003-04 and 2005-06.   The columns on the right are for unadjusted SQF;  those on the left are for adjusted SQF.]

The answer is clearly yes.    The interyear reliability for adjusted SQF is almost as high as that for unadjusted SQF.    Thus, SQF is a substantially reliable metric, and this effect is largely independent of measurement bias.

Thus,   just as SQA is a reliable and valid component of team defence,   so too is SQF a valid and reliable component of team offence.

Sources:

2003-04 shot quality data -- study done by Ken Krzywicki

2005-06 shot quality data -- study done by Ken Krzywicki

2006-07 shot quality data -- study done by Alan Ryder

Shotblocking and Save Percentage

In a recent post by the Contrarian Goaltender,  he collectively analyzes data from 1999-00 to 2007-08 in order to determine the relationship between shot attempts against and save percentage    One of his findings was that there was a negative correlation between blocked shots and save percentage over this period.

To what extent,  however,  is this effect mediated by shot quality,  as measured by Alan Ryder?   On the one hand,  if the majority of blocked shots are those coming from the point and other peripheral areas of the offensive zone,  then the shots that "get through" would tend to be those from areas closer to the net.   One would expect this to effect to be reflected by the shot quality data.   On the other hand,  if blocking shots has the effect of interfering with the sightlines of the goaltender, then this effect would not necessarily be reflected in the shot quality data.   Both of these effects, if real, could account for the fact that teams that block more shots tend to have lower save percentages, on average.  However, without analyzing the date, it's unclear to what extent each process is operative.

I think that it goes without saying that both processes are,  to some degree,  at work here.   It would be unreasonable to suspect that the relationship between save percentage and shot blocking can be entirely accounted for by one single factor;  indeed, most causal relationships that exist in complex phenomena, hockey included, are multifactorial.   What I'd like to determine,  however,  is how much of the correlation can be accounted for by the shot quality data.

What I did was analyze the relationship between team blocked shots,  team save percentage,  team shot quality,  and team shot quality neutral save percentage for each season between 2002-03 and 2007-08.  I determined the correlations for each season individually because the total number of blocked shots is not uniform over time. Specifically, the pre-lockout values are significantly lower than the post-lockout values, and the 2002-03 values are much lower than that for 2003-04 (and every other season, for that matter).  Thus,  analyzing the data as a whole would preclude interpreting the results with any degree of confidence.   It is worth noting that 2002-03 is no arbitrary cutoff -- there is simply no data on shot quality prior to this season.   As is the case with the shot quality data,  there is a clear arena bias with respect to the recording of blocked shots.   This is evidenced by the fact that the standard deviation in home blocked shots is much higher than for that for road blocked shots.   The data:


What I did,  then ,  is incorporate the figures for both total blocked shots and road blocked shots.   Presumably,  road blocked shots would provide a better indication of the 'true' number of shots blocked as it less subject to arena bias.   Here are the inter-variable correlations:
It should be noted that the shot quality data for 2006-07 and 2007-08 is for road shot quality only -- thus,  I was unable to include the correlations between road shot quality neutral save percentage and road blocked shots for any season prior to 2006-07.   The correlations between road save percentage and road blocked shots were also excluded for these seasons.

Several points:

1.   There are slight,  yet consistently negative,  correlations between total blocked shots and save percentage.  Although I doubt that any of the specific correlations are statistically significant,  the fact that all of them are in the same direction is suggestive of an underlying relationship.    Also,  this accords with The Contrarian Goaltender's finding of a correlation of -0.30 between blocked shots and save percentage over a larger data sample.

2.  The correlations between total blocked shots and shot quality neutral save percentage show a similar trend in that for every season the relationship is mild yet inverse.  Thus,  shot blocking seems to have an effect on goaltender save percentage that is residual and cannot be accounted for by the shot quality data.  Of course, it's also clear that shot quality is also partly driving the relationship.    For one, there seems to be somewhat of a positive correlation between blocked shots and shot quality against.   Additionally, the fact that the relationship between blocked shots and SQN % is not as strong as the relationship between blocked shots and save percentage proper also shows how shot quality partially mediates the correlation.  If it did not, these two groups of correlations would be near identical.

3. The correlations for the 'road' data are generally weaker than the overall correlations,  which is counterintuitive as both road shot quality and road blocked shots are less distorted by bias.   This can probably be explained by the attenuated sample size for road games in each individual season (41 vs 82 games),  which diminishes the resolution of the data.    Indeed,  this also occurred in previous posts where the shot quality figures themselves were analyzed.