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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Two Graphs and 665 words that will convince you on Shooting %

Last week Tyler Dellow had a post titled “Two Graphs and 480 Words That Will Convince You On Corsi%” in which, you can say, I was less than convinced (read the comments). This post is my rebuttal that will attempt to convince you on the importance of Sh% in player evaluation. The problem with shooting percentage is that it suffers from small sample size issues. Over small sample sizes it often gets dominated by randomness (I prefer the term randomness to luck) but the question I have always had is, if we remove randomness from the equation, how important of

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Measuring persistence, randomness, and true talent

In Rob Vollman’s Hockey Abstract book he talks about the persistence and its importance when it comes to a particular statistics having value in hockey analytics. For something to qualify as the key to winning, two things are required: (1) a close statistical correlation with winning percentage and (2) statistical persistence from one season to another. More generally, persistence is a prerequisite for being able to call something a talent or a skill and how close it correlates with winning or some other positive outcome (such as scoring goals) tells us how much value that skill has. Let’s look at persistence first. The

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Estimating actual randomness in goal data

If you have been following the discussion between Eric T and I you will know that there has been a rigorous discussion/debate over where hockey analytics is at, where it is going, the benefits of applying “regression to the mean” to shooting percentages when evaluating players. For those who haven’t and want to read the whole debate you can start here, then read this, followed by this and then this. The original reason for my first post on the subject is that I rejected Eric T’s notion that we should “steer” people researching hockey analytics towards “modern hockey thought” in

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