Quote:
Originally Posted by transplant99
Pinder is a smart guy and he is going to be a star in Canadian radio and maybe even further one day.
However, he is also way way to invested in both analytics and ignoring what he sees to form opinions, and then his pig-headedness takes over when it is pointed out how wrong he is.
He really doesn't value defensive hockey whatsoever and that was proven in spades when he finally stopped his endless chirping of Engelland after seeing exactly how valuable he was this past season. He had been told repeatedly for a couple years that Engellands contributions werent going to be found in some made up chart, but he refused entirely to listen. He would get very very smarmy about it on top of that.
Once he gets over that kind of stubborness, he will be fine though.
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Let me preface this by saying I am NOT dismissive of analytics in hockey at all. Believe me, I know how valuable they are in sports but hockey is a little trickier. That said, I think most people understand that analytics give more information but are not giving the entire picture.
What I do see are people who put too much into analytics and the data really drives their perceptions. I think this data has flaws.
To put it more eloquently here is some one from MIT and the Sloan conference:
Quote:
Can Derived Hockey Statistics Accurately Value Players?
To answer the following question: “Can derived hockey statistics accurately value players?”, it will be useful to examine the reason derived baseball statistics do reasonably well at determining the value of baseball players. Baseball is played in a discrete manner with each team separately playing offense and defense. Each discrete event (i.e., pitch, hit) generates observable data available for analysis. The manner in which baseball is played creates an environment where basic statistical assumptions are not violated when data are aggregated. Therefore, derived statistics aggregated over many events can potentially provide accurate estimates of a baseball player’s contribution to winning.
Hockey is played in a continuous manner with constant flow and continuous player interactions with each team simultaneously playing offense and defense. Only some events (e.g., shots, goals) generate data that can be made available for analysis. As a result, the manner in which hockey is played creates an environment where basic statistical assumptions are violated when data are aggregated. Therefore, derived statistics are not likely to provide accurate estimates of a hockey player’s contribution to winning. Hockey managers are aware of the complex nature in which hockey data are generated and are among the first group of people to dismiss player valuations based solely on derived statistics.
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http://www.sloansportsconference.com...g-not-the-end/
It may sound like I'm bashing analytics and that's not the case at all. I just think they are not sophisticated enough to base opinions/perceptions/narratives on players the way that some do. I'm positive it will grow and improve and it will become highly illustrative.
Furthermore, I'm not interested in debating the merits of analytics mostly because I do value them and because it's been done.
Here is an example of the beginning of the sophistication I think is coming:
Quote:
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Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics. Across these sports, scoring tempo—when scoring events occur—closely follows a common Poisson process, with a sport-specific rate.
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https://arxiv.org/pdf/1310.4461.pdf
It's really a big data or complexity science thing with dynamic systems like hockey.
http://www.slate.com/articles/sports...ockey_are.html