Quote:
Originally Posted by Boreal
That being said, the point of analytical information like this is description and somewhat predictive. Like any accurate Bayesian model it will tell you the most at the tails, who is really bad and who is really good. Year after year, we are what we consistently do.
In other words the numbers are an oversimplification to assess a particular part of a players game in context and expose cognitive biases a person may have on a certain part of a certain players game.
|
The key point you make is around "any accurate Bayesian model" - I'm not sure that these are those.
In mathematics, you need to diagnose your model, evaluating the basic assumptions and comparing model approaches, and prepare the model for a proper defense.
The primary arguments when defending are around assumptions. Some of the assumptions added to these models are subjective (e.g. what constitutes a turnover, what is a proper zone entry.) Some make assumptions about specificity (e.g. shot location makes a shot harder or easier to save.) These may seem obvious, but are they statistically significant given the small numbers of minutes of performance?
In short, models can be descriptive (e.g. relative performance of a machine.) or predictive (e.g. economic forecast.) Descriptive models can be tested against current performance, predictive against future events. In order to be validated, they should be tested. Are any of these?
I (unfortunately) somewhat agree with Brian Burke, where he said “Analytics are like a lamppost to a drunk. They’re good for support, but not for illumination.”