Summary of RIT Hockey Analytics Conference

This past weekend I attended and spoke at the Rochester Institute of Technology Hockey Analytics Conference and because it was such a great conference I wanted to write up some of my thoughts on the event. First up was a panel discussion on the State of Hockey Analytics with Timo Seppa, Sam Ventura, Andrew Thomas and Matt Pfeffer. Three of these guys now work with NHL teams (Ventura with Penguins, Thomas with Wild and Pfeffer with Canadiens) but they didn’t divulge very much information about the inner workings of their respective organizations. A number of topics were discussed which were

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What are Rel and RelTM stats?

This came up in a twitter conversation today and since I will be referencing both of these as part of my RIT Hockey Analytics Conference talk it might be a good idea to re-introduce them to anyone who are not familiar with them. At their core, Rel and RelTM stats both attempt to account for the quality of teammates a player plays with. The problem with Corsi%, as Paul Bissonette points out, is that it is heavily driven by the players one plays with. Ultimately, teams don’t draft based on Corsi or possession numbers. You draft a player because you’ve

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The Value of Outliers

Ryan Stimson has been doing some valuable work tracking passes and this morning he posted an interesting analysis of the data he (and others) have collected thus far. It is a very interesting article and definitely worth a read. It is a valuable contribution to shot quality research but the article created some twitter discussion regarding one of the techniques that Ryan used. In particular, when Stimson was looking at the correlation between two variables (i.e. passing ability vs shooting percentage) he noticed that there was often an outlier team and he would subsequently look at the correlation between the two variables while

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Estimating Standard Deviation in On-ice Shooting Percentage Talent

I have tackled the subject of on-ice shooting percentage a number of times here but I think it is a subject that has been under researched in hockey analytics. Historically people have done some split half comparisons found weak correlations and written it off as a significant or useful factor in hockey analytics. While some of the research has merit, a lot of the research deals with too small of a sample size to get any really useful correlations. Split-half season correlations with majority of the players is including players that might have 3 goals int he first half and

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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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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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Columbus Blue Jackets and Rush Shots

Before I get into rush shots of individual players I am going to look at some teams. I am starting with the Columbus Blue Jackets which was suggested for me to look at by Jeff Townsend who was interested to see impact the decline of Steve Mason and then the transition to Bobrovsky had. Before we get to that though, let’s first look at the offensive side of things (and if you haven’t read my introductory pieces on rush shots read them here, here and here). The League data is league average over the past 7 seasons. There is a lot of randomness happening

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Rush Shots Leading vs Trailing and Home vs Road

Yesterday I introduced the concept of rush shots which are basically any shot we can identify as being a shot taken subsequent to a rush up the ice which can be determined by the location of previous face off, shot, hit, giveaway or takeaway events. If you haven’t read the post from yesterday go give it a read for a more formal definition of what a rush shot is. Today I am going to take a look at how rush shots vary when teams are leading vs trailing as well as investigate home/road differences as arena biases in hits, giveaways

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Introducing “Rush” shots

I have been pondering doing this for a while and over the past few days I finally got around to it. I have had a theory for a while that an average shot resulting from a rush up the ice is more difficult than a shot than the average shot that is generated by offensive zone play. It makes sense for numerous reasons: The rush may be an odd-man rush The rush comes with speed making it more difficult for defense/goalie to defend. Shots are probably take from closer in (aside from when a team wants to do a line

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Why can’t players boost a goalies save percentage?

The other day I put up a post on Mike Weaver’s and Bryce’s Salvador’s possible ability to boost their goalies save percentage and I followed it up with a post on the Maple Leafs defensemen where we saw Phaneuf, Gunnarsson, Gleason and Gardiner all seemingly able to do so as well while Robidas had the reverse effect (lowering goalie save percentage). This got some fight back from the analytics community suggesting this is not possible. My question to them is, why not? Their answer is that if you do year over year analysis of a players on-ice save percentage or

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