Forward vs Defense Player Evaluation

Today Travis Yost of 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….

December 10, 2015

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…

November 22, 2015

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…

November 21, 2015

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….

November 21, 2015

A word on’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 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…

November 17, 2015

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,…

October 12, 2015

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…

September 21, 2015

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…

August 20, 2015

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…

August 10, 2015

Score Effects, Save Percentage and Brandon Sutter

Score effects are a well known and well understood observation in hockey analytics. Essentially what score effects tell us is teams play differently depending on the score and in turn the resulting statistics are altered because of it. To keep this simple, in general teams that are leading give up more shots, but a smaller percentage of them end up as goals (they also take fewer shots but a higher…

August 9, 2015