Goals, Corsi, and Weighted Shot Differential

Yesterday ‘Tangotiger’ introduced a new hockey metric that got the hockey twitter world all excited. Go read the articles for the methodology and rational behind the metric but in short he conducted first half season vs second half season regression and discovered that goals and shot attempts that didn’t result in goals should be weighted differently. The final result was that for his weighted shot differential goals should be given a weight of 1.0 and shot attempts that didn’t result in goals (saved, missed the net or blocked) should be given a weight of 0.2. Although he concluded that because

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Does higher Corsi Against rates boost Save Percentage?

Yesterday I wrote an article for MapleLeafsHotStove.com looking at the Leafs performance so far this season in comparison to previous seasons. In it I showed a chart comparing the Leafs CA/60 rate in comparison with their Save% and it was quite astonishing how they rose and fell in lock-step. Here is that chart:   Very rarely in hockey analytics do you get a chart that looks as “nice” as that one so it is something that really draws my attention. Essentially what this is saying is that the more shot attempts you give up the higher the goalies save percentage

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Boosting save percentage…

Last night on twitter I posted some GF%RelTM statistics which resulted in a number of comments but notably some from Stephen Burtch about how players cannot be blamed for GF% and is nothing more than a fancy +/- stat and how players can’t be blamed or given credit for things such as save percentage. @hockeyanalysis @mlse Polak .910 on-ice sv% at 5v5 close past 4 years. Robidas .905 on-ice sv%. We should totally blame them. — Stephen Burtch (@SteveBurtch) September 26, 2014 .@hockeyanalysis @mlse Carl Gunnarsson had a .926 5v5 close on-ice sv% which was totally caused by his skill.

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Relative importance of Corsi and the other 42%

Today apparently there was some discussion about the Avalanche and their non-interest in hockey analytics. In that discussion Corey Pronman wrote the following tweet: @ThomasDrance @adater point is Corsi explains 35% of wins. Luck explains about 40%. If something’s better than Corsi, not much room left. — Corey Pronman (@coreypronman) September 24, 2014   I have seen the above logic from time to time. I think it dates back to something Gabe Desjardins wrote many years ago. I find the logic very odd though. Let me explain. Let’s assume that the numbers are true. According to my math, that leaves

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Evaluating defensive ability

A short while ago I aksed the question of who the best defensive defensemen in the NHL are to my twitter followers and it became clear to me that I am not certain people really know how to evaluate players defensive ability. I’ll explore that further in a bit but first here are some of the answers I received. Vlasic Seabrook Chara Muzzin Fayne Giordano Stralman Andy Greene Rozsival Paul Martin Shea Weber Hjalmarsson Phillips and probably a few more I missed It also spawned a lot of talk about corsi%, CorsiRel and players CF% with and without certain players. This really

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TSN Analytics Team, Even Strength Play and Marc-Edouard Vlasic

Earlier this week TSN announced the creation of an Analytics team consisting of long-time TSN contributor Scott Cullen along with new TSN additions of Globe and Mail’s James Mirtle and hockey blogger Travis Yost. I am all for main stream media jumping on board with hockey analytics but once you go from independent hockey blogger to a significant contributor to TSN I think it opens the door to higher expectations and higher standards.  Scott Cullen has a long track record with TSN and I am confident James Mirtle will bring some intelligent insight as we are all familar with and respect his

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Team Zone Entry Data and Predicting Standings

I am sure many of you are aware that Corey Sznajder (@ShutdownLine) has been working on tracking zone entries and exits for every game from last season. A week and a half ago Corey was nice enough to send me the data for every team for all the games he had tracked so far (I’d estimate approximately 60% of the season) and the past few days I have been looking at it. So, ultimately everything you read from here on is thanks to the time and effort Corey has put in tracking this data. As I have alluded to on

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Rush Shots and Defensive Zone Play of Maple Leaf Defensemen

The other day over at PensionPlanPuppets.com there was a post by Draglikepull looking at zone exits by Maple Leaf defensemen for the first half of last season. If you haven’t seen it yet, definitely go read it. I wanted to compare the zone exit data to my rush shot data which I have calculated from play by play data as explained here. If we can find good correlations between zone entry/exit data and my rush shot data that would be an excellent finding because the zone entry/exit data need to be manually recorded and is very time consuming. Thankfully this

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Leafs, Kings and Devils – Rush goals and shooting percentage

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

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Team 5v5 road Rush Shot Statistics – 7 year and 3 year

There was some discussion of rush shots over at Pensburgh.com and I realized I haven’t really provided much in the way of team rush stats other than some in chart form. So, here are some tables with 7 year and 3 year data in 5v5 road situations (sorted by RushSh% or RushSv%). 7 year Shooting Percentages Team RushSh% OtherSh% Rush% Toronto 11.02% 7.75% 24.44% Los Angeles 10.37% 5.93% 23.16% Anaheim 10.26% 7.95% 22.57% Chicago 10.20% 7.31% 21.78% Winnipeg 10.19% 7.47% 24.00% St. Louis 10.11% 7.22% 24.24% Nashville 10.05% 7.65% 25.13% Columbus 10.01% 6.70% 24.48% Washington 9.89% 7.59% 23.93% Philadelphia 9.85% 7.53%

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