Last week Tyler Dellow had a post titled “Two Graphs and 480 Words That Will Convince You On Corsi%” in which, you can say, I was less than convinced (read the comments). This post is my rebuttal that will attempt to convince you on the importance of Sh% in player evaluation.

The problem with shooting percentage is that it suffers from small sample size issues. Over small sample sizes it often gets dominated by randomness (I prefer the term randomness to luck) but the question I have always had is, if we remove randomness from the equation, how important of a skill is shooting percentage? To attempt to answer this I will look at the variance in on-ice shooting percentages among forwards as we increase the sample size from a single season (minimum 500 minutes ice time) to 6 seasons (minimum 3000 minutes ice time). As the sample size increases we would expect the variance due to randomness to decrease. This means, when the observed variance stops decreasing (or significantly slows the rate of decrease) as sample size increases we know we are approaching the point where any variance is actually variance in true talent and not small sample size randomness. So, without going on any further I present you my first chart of on-ice shooting percentages for forwards in 5v5 situations.

Variance decline pretty much stops by the time you reach 5 years/2500+ minutes worth of data but after 3 years (1500+ minutes) the drop off rate falls off significantly. It is also worth noting that some of the drop off over longer periods of time is due to age progression/regression and not due to reduction in randomness.

What is the significance of all of this? Well, at 5 years a 90th percentile player would have 45% more goals given an equal number of shots as a 10th percentile player. A player one standard deviation above average will have 33% more goals for given an equal number of shots as a player one standard deviation below average.

Now, let’s compare this to the same chart for CF/20 to get an idea of how shot generation varies across players.

It’s a little interesting that the top players show no regression over time but the bottom line players do. This may be because terrible shot generating players don’t stick around long enough. More importantly though is the magnitude of the difference between the top players and the bottom players. Well, a 90th percentile CF20 player produces about 25% more shots attempts than a 10th percentile player and a one standard deviation above average CF20 player produces about 18.5% more than a one standard deviation below average CF20 player (over 5 years). Both of these are well below (almost half of) the 45% and 33% we saw for shooting percentage.

I hear a lot of ‘I told you so’ from the pro-corsi crowd in regards to the Leafs and their losing streak and yes, their percentages have regress this season but I think it is worth noting that the Leafs are still an example of a team where CF% is not a good indicator of performance. The Leafs 5v5close CF% is 42.5% but their 5v5close GF% is 47.6%. The idea that CF% and GF% are “tightly intertwined” as Tyler Dellow wrote is not supported by the Maple Leafs this season despite the fact that the Maple Leafs are the latest “pro-Corsi” crowds favourite “I told you so” team.

There is also some evidence that the Leafs have been “unlucky” this year. Their 5v5close shooting percentages over the past 3 seasons have been 8.82 (2nd), 8.59(4th), 10.54(1st) while this year it has dropped to 8.17 (8th). Now the question is how much of that is luck and how much is the loss of Grabovski and MacArthur and the addition of Clarkson (who is a generally poor on-ice Sh% player) but the Leafs Sh% is well below the past few seasons and some of that may be bad luck (and notably, not “regression” from years of “good luck”).

In summary, generating shots matter, but capitalizing on them matters as much or more.

### 2 Responses to “Two Graphs and 665 words that will convince you on Shooting %”

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I know I’m a bit “johnny-come-lately” to this post but I had a few questions if you are still around and willing to answer:

1. Based on your description of the data used and the looks of your resulting charts it seems like the individual players making up the percentile bands are different from season to season (i.e. Player A could be in 95th %tile in season 1, then 80th in 2 and 4, and 90th in 3 and 5) so that you are not looking at the variance in on-ice sh% for given players over time but instead the variance in league-wide (subject to your sample criteria constraints) on-ice sh%. Is this correct or am I misinterpreting?

2. By focusing on comparing on-ice shooting% to CF/20, instead on just individual shooting% to CF/20, are you arguing that individual players are consistently capable of improving the finishing ability of their linemates? While I can conceptually understand how a player can be a driver of team possession and shooting (i.e. making their linemates better in terms of getting them higher numbers of shots via good vision and passing) I find it hard to believe that a player can actually increase another player’s sh% reliably. On the other hand, I can believe that individuals have “true talent” sh% levels which, while varying over time, play a very important factor in scoring differentials. In fact when I look at the data on your site and calculate linemate shooting percentages over 6 seasons time I find them to be relatively independent of the individual sh% over the same time frame which implies that it is the individual players’ ability to shoot well which drives the on-ice% rather than their ability to make their linemates shoot better. Therefore shouldn’t the variance (particularly from year to year) on just an individuals sh% instead on on-ice% be the focus of any of these analyses, particularly since the individuals sh% will be far more variable from year to year than on-ice sh%?

1. Yes, this is correct. The theory is, for a variable that is largely ‘random’ as we increase sample size the spread should shrink and approach zero. So, by looking at the variance using 1 year, 2 years, 3 years, etc. of data and how it changes with increasing sample size we can get an idea of whether it is truly random or whether true talent exists and to what extent. The fact that shooting percentage variance seems to level off and approach something other than zero indicates it isn’t truly random and the level it ‘levels off’ to gives an indication of talent level.

2. We know that some players shoot better than other players but a significant part of that is how they play. Mike Knuble used to consistently post really higher shooting percentages not necessarily because he had a great shot but because we always stood within 10 feet of the net chipping in easy rebounds. This is somewhat easy to measure and is not disputable but you can’t expect good results by having 5 players standing in front of the net. In essence you can only have one Knuble on the ice at a time. Knuble’s individual shooting percentage is at the expense of his team mates who have to play and take shots from more difficult locations. When evaluating players we need to evaluate his overall impact to team results, not individual contribution (which may be at the expense of his teammates results). This is why I (and many others) believe “on-ice” stats are as or more important than individual stats.

Now, I am not arguing that individual player can influence his line mates finishing ability any more than the player can influence his line mates ability to take shots. When evaluating a player one needs to try to isolate their own individual ability from the abilities of his line mates. A line mate that has a high on-ice shooting percentage might have that because he players with players with really good finishing ability. But of course the same can be said about CF20. I do believe that players (i.e. good playmakers) can improve the shooting percentage of their line mates but I am not going to say that the whole variance in on-ice shooting percentage is due to that alone.

My goal with this post was to show that shooting percentage is a significant factor in goal production. We can debate the why (individual shooting talent, ability to boost teammates finishing ability, QoT, QoC, score effects, style of play, etc.) but the fact that on-ice shooting percentage varies significantly is not debatable.

Also, to be clear, I would never condone evaluating players based on shooting percentage alone. There is no reason to do that. I promote using a goal based analysis provided sufficient data exists.