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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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 percentage of them end up as goals). Let me reiterate the main point here. When a team has a lead they effectively give up more shots but those shots are,

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