I get that there’s a lot to be said for leaving statistics as raw as we possibly can, to make it more accessible and relatable to the actual watching the game. There’s also a lot to be said about making our stats better, and pushing the frontiers of what analytics can tell us. So hear me out! I have a better way of calculating corsi.

We put a player’s shifts into 3 buckets: shifts that started with an offensive zone faceoff, a neutral zone faceoff and a defensive zone faceoff. Then the offensive and defensive zone corsi for percentages are weighted equally, with the percentage of neutral zone faceoffs (and therefore the weight of it’s statistic) remaining the same. Let’s use Dion Phaneuf as an example.

In the first pie graph, Phaneuf’s abysmal corsi of 30.5 in defensive zone starts outweighs his corsi in offensive zone starts by 15%. Once adjusted, the defensive zone started shifts have the same amount of weight as offensive zone starts in the calculation.

For most players, the effect is marginal.

On the very extremes we see a couple of percentage points in difference between this corsi and the normal way of calculating it, but the overwhelming majority of players have less then a percentage of difference in their numbers. I think this gels well with other recent research on how much zone starts affect a players numbers.

There have been a lot of great attempts at adjusting for zone starts in the past (Driving Play, Vic Ferrari, NHL Numbers) but they all were approximations of the effect based on league averages. With these numbers, we don’t adjust a players corsi by how OZ% effects a players corsi on average, we adjust for how it affects *that* player. All of these numbers are still the players, we’re just adjusting the weight distribution.

These numbers are now available at Hockey Prospectus’ own stats page. Team relative stats are also adjusted on the off ice part of the equation as well. You can still find unadjusted CF%, FF% and GF% in the back.

I decided against actually listing a statistic for the percent zone start effect, but if anyone would like to see it or see me write more about it I’d be happy to.

Pingback: Toronto Maple Leafs News & Notes: March 12, 2014 - Editor In Leaf - A Toronto Maple Leafs Fan Site - News, Blogs, Opinion and More

While the adjusted corsi is good to have, I’m actually really interested in the CF% (and all relative/competition/teammates derivatives) breakdown by zone start. I think that would be a very interesting to see, and will give a little more indication to which players are good D-zone performers versus O-zone performers. I think the comparison of that breakdown to their deployment will yield good info about whether coaches are using particular players well. I was thinking about doing this myself, but if you already have the data, I would love to see it.

I actually had these stats up before I made the new calculation. I didn’t realize there was interest when I took them down. I agree that there definitely is a lot of valuable information to be cleaned from corsi by zone start.

I started thinking about this actually while reading about some of the leading candidates for the Selke this year: Bergeron, Toews and Kopitar. Only Bergeron has <50% Ozst%. I was a pretty happy with that since I'm a big Bergeron fan and am always gunning for him. But then I realized that on the Kings, no one has a <50% Ozst%, so that's not really fair to hold against Kopitar and say that he's not being deployed in a defensive capacity. So then just for the three teams I looked at the relative Ozst% of players versus their team. Bergeron still had Kopitar beat in terms of how much more negative he was, but in terms of order on their respective teams, they were both in the top two most negative forwards. Toews was positive. I also looked at what percentage of a team's d-zone start are each player on the ice, and again Bergeron and Kopitar were each the top 2 forwards on their team, and Toews was further down the list. Obviously, and we knew this from previous years too, Toews is not being deployed as a defensive forward. But then I thought, ok, just because Bergeron and Kopitar are on average being deployed more defensively, doesn't automatically mean that they're better than Toews defensively. What would actually be more informative is to look at who does well in a defensive situation, and not just who is often put in a defensive situation and does pretty good on average. Maybe Bergeron and Kopitar do so well in the offensive zones that they average to their leading possession numbers in the league. Which then led me to think that there must be interesting information to look at any number of performance measures broken down by zone start. The great thing about this is that zone start is discrete, so there's going to be data for all players for each of the three zones (though maybe there is a question of samples size for players deployed at extremes). And you can somewhat more definitively compare across players given the same situation, and not just some % of situation or some arbitrary weighting of the situations.