More on Evaluating Defense by Rank on Team

The other day I wrote a post on evaluating forwards and defensemen based on their rank on their team. The purpose of that post is to show the value in breaking down performance beyond just Corsi but into Corsi For/Against and shooting and save percentages. I wanted to expand on that by looking at past seasons to see if there are trends that emerge. In the previous post I ranked players on their team based on their total ice time because that is what Travis Yost did in his post on TSN.ca. I don’t believe this is the best methodology

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Forward vs Defense Player Evaluation

Today Travis Yost of TSN.ca put up an interesting post where he ranked forwards and defensemen based on their ice time with their team (1-12 for forwards and 1-6 for defensemen) and then looked at each group (1-12) average to see how the forwards performed. To illustrate this, I took the average Corsi% for every forward who led his team in 5-on-5 ice-time, then repeated the same through the twelfth forward. You’ll notice how the first line looks great, the second and third lines look average, and the fourth line looks poor.   When I do this I get the following

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More on anecdotes, evidence and Corsi

Last night Tyler Dellow criticized me for using ‘anecdotes’ to come up with an idea (not a conclusion or a proof, more of a hypothesis) that by improving Corsi it might have a negative impact on your shooting percentage. Also last night James Mirtle retweeted an April tweet of his looking at the relationship between possession and Corsi and it was retweeted several times. Here is that tweet: On the surface it sounds like pretty resounding evidence. Corsi is king! Corsi rules the NHL! Long live Corsi! The problem is, this is solely backward looking and is only focused on

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Anecdotes, evidence, and open mindedness

Within a couple of minutes of posting my last article on the Corsi and shooting percentages of Carolina and the Maple Leafs there were a couple of back-handed attacks to the post from Tyler Dellow. First, clearly Dellow didn’t fully comprehend the article because I didn’t include any direct commentary on the goal scoring of the Hurricanes and in no way did I imply anything had a correlation of -0.98 with goal scoring at 5v5. Specifically, the correlation was between CF% and shooting percentage and both those statistics include a component that factors into goal scoring. In fact, CF% and shooting

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Corsi and Shooting Percentage of Hurricanes and Maple Leafs

I have frequently wondered if there is an inverse correlation between Corsi and Shooting percentage and have written about it several times in the past specifically with how coaching changes impact these statistics. For example, last season and investigated how coaching changes impacted the teams Corsi and shooting percentage. In the summer though I looked at three teams that we know employed analytics at the coaching level – Toronto, Carolina and Edmonton. In that article I showed that each of those teams improved their Corsi in 2014-15 over 2013-14 but that they also all saw a drop in their team shooting percentage.

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A word on NHL.com’s Enhanced Stats

For those that follow hockey analytics you are probably fully aware of Travis Yost’s recent comments on the enhanced stats pages on NHL.com. Today Greg Wyshynski chimed into the debate with a summary of the situation along with more comments from Yost as well as from Chris Foster of the NHL. The comments from Foster has generated a fair bit of buzz from the hockey analytics community and in particular his comments about “close” stats which Yost railed on earlier. “Years ago, smart people recognized that simply throwing out data for the sake of correcting for score effects was inefficient.

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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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Analytics, Coaching Tactics, and Observable Results

Today the Carolina Hurricanes officially announced that Eric Tulsky has been hired in the position of “Hockey Analyst” with his role being defined as follows: As Hockey Analyst, Tulsky will provide and analyze data to assist the hockey operations department and coaching staff. The official announcement also mentioned that Tulsky had worked for the team last season on a part-time basis which we already knew. For me the interesting thing here is that we learn that Tulsky will be working with the coaching staff and thus I think it is safe to assume that he probably did some of that

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Order from Randomness?

If you want to claim that a piece of data is random, then there must be no identifiable patterns within it for if there are, then the data is not random. For example one can easily look at a long-term list of forwards sorted by on-ice shooting percentage and clearly see that it is not random. The top of the list is dominated by everyone we would identify as elite offensive forwards and the bottom of the list is dominated by 3rd and 4th liners. Even with just 2 years of data the list is fairly well sorted with a range/standard deviation not much

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