I actually had one player, in particular, in mind - Mario Lemieux. I was talking to Eric T over at Broad Street Hockey today about playmakers driving their teammates shooting percentages and it occurred to me that if anyone could create better chances for his teammates, it would be Lemieux. Gretzky, too, obviously, but we don't have game logs from his prime yet, and Lemieux's injury record makes it easier to see what happened when he wasn't playing.

First, let's look at Pittsburgh's record with and without Lemieux from 1989-90 to 1993-94, a stretch when he played between 22 and 64 games per season:

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Frequent commenter, hockey analyst and friend-of-the-site Mike Schuckers has a number of pieces about what he calls "Defense-Independent Goaltender Ratings." This is essentially a metric that backs out any variation in shot location that different goaltenders face. I built a system very similar to Mike's and I put the results in yet another stats sheet - here's a link for 2007-08 to 2010-11 results:

http://behindthenet.ca/goalie_shot_quality.php?sort=12&mingp=1000

Columns are:

2-4) Shots against, Goals Against and Opponent Shooting Percentage @ 5v5

5-6) Expected Goals Against and Expected Opponent Shooting Percentage @ 5v5

-> This captures the locations of shots against

7) Delta between Expected and Actual Save Percentage @ 5v5

-> This shows how much a goalie has over-performed (or under-performed) an average goalie who saw shots from the same locations

As you continue on, you see all shots, then 5v5 results and all shots for the last four seasons.

Let me know if you have any questions. And, as always, if you enjoy the stats we produce here and make use of them, please consider making a donation to our favorite charity, Education in Need El Salvador.

I know many of you think that statistical analysis views all players as automatons, but we really only do that as an approximation to reality. One place where the approximation deviates somewhat from reality is in shooting.

I took every player who took 200 or more even-strength shots over the last six seasons and posted an 8% or higher shooting percentage. I then looked at the distribution of goal-scoring in every 100-shot stretch they had and compared this to the simulated distribution of goal-scoring assuming every player's shooting talent was always equal to his observed shooting percentage. What we see are fatter tails in the real distribution:

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I put together a massive undocumented stats sheet that, if you go through the pain of figuring it out before I document it, will let you look at individual and on-ice shooting and shot quality. One simple but interesting result at 5-on-5:

That jives with previous estimates of the impact of shot quality. But don't take my word for it - you have all the data you need to prove it and to figure out, at the margins, where shot quality matters. I hope we can move on to the other 89% of what drives goal-scoring and goal-prevention.

I added a handful of features to the "shot quality" tables, specifically on-ice shot quality (for and against) at 5v5, excluding empty net goals. Here are a few links:

1. Shot quality for, on-ice: http://behindthenet.ca/shot_quality.php?sort=11&mingp=200

2. Finishing performance, relative to shot quality: http://behindthenet.ca/shot_quality.php?sort=12&mingp=200

3. Shot quality against, on-ice (higher is higher-quality shots): http://behindthenet.ca/shot_quality.php?sort=17&mingp=200

4. Finishing performance against, relative to shot quality: http://behindthenet.ca/shot_quality.php?sort=18&mingp=200

I have a few more features to add and then I'll document the methodology...

Just dashing this off before I have to start pretending to cook Thanksgiving dinner...Here's a first cut at individual shot quality numbers. It's totally not self-explanatory, I realize, but in brief, this shot quality model factors in shot distance, man-advantage and rebounds. Empty-net shots (either team with an empty net) are excluded for the moment, but it includes all other man-advantage states. Rink results are also adjusted to equalize shot quality - otherwise the Rangers look like the worst shooters in the world.

I'll add a few other data columns plus on-ice shot quality (ie - all shots when a player's on the ice) and team-level data when I get a chance. And I'll document the model, simplistic as it is. Let me know if you have any questions.

People sometimes ask me what the single-most useful statistic is in hockey. Obviously if we had a reliable way to measuring how many wins a player contributed to his team, that would be our choice, but the computational complexity of such a measure limits its value to someone who's looking for a simple yet powerful stat.

My vote for the simplest yet most-useful statistic is PDO (the name is short for its inventor, also named PDO.) PDO is nothing more than shooting percentage plus save percentage. But this simple statistic captures several complex and powerful concepts:

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The notion of "Shot Quality" is hardly a new one. Alan Ryder probably published his first analysis of it ten years ago and he published his recall of it - because it's hopelessly overwhelmed by arena bias - over four years ago. So what have we learned in ten years?

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