Quote:
Originally Posted by PepsiFree
I think you’re totally missing the point (you’re not alone, as Enoch and a couple others aren’t getting it either). The point isn’t to create the most accurate model by eliminating inputs and reducing the sophistication of the model. The point is creating a sophisticated model with these necessary inputs that is as accurate as possible.
For example. I “fixed” your model by removing the redundant addition of adding half the difference. Got it to 10.2. I “fixed” it further by adding the redundant addition back and reducing it to 1/8 instead of 1/2. Got it to 10.0. So the question is: how useful is the model? What do we learn from it if I can change one arbitrary number we just made up and make it more accurate? The answers are probably “not at all” and “nothing.”
Go back and apply your model to the year previous. 10.7. JFresh? 9.9. You’re solely trying to reverse engineer a model with the lowest error rate by ignoring as many inputs as possible, while he has a model with a laundry list of inputs and simply hopes it’s among the least inaccurate.
The point of the whole thing is the inputs. It’s a reflection of how a team should perform based on all of the inputs you ignored or eliminated. It’s more about the “why” and less about the result. The closer the result, the more we learn about the accuracy of the why and how (not the reverse). Without any why or how there’s really nothing to learn and no point to having developed a model in the first place.
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LOL. You're always so quick to suggest others aren't getting it, when it is you that is missing the point.
The 'point' of a model (as you suggested in the 2nd bolded comment) is to represent something, and the point of this model is to predict where teams will finish in the standings. jfresh has built a model with lots of inputs,
but it does very little in actually predicting what it is attempting to predict.
The NHL standings are a fairly tight distribution, with about 2/3s of the teams finishing within about a 30 pt ban, each year. And the overall migration of individual teams, from year to year, is generally quite small (good teams remain good, bad teams remain bad, etc).
So a model that has an average error of roughly 10 points, is actually of very little value. And to demonstrate that to you, several people threw up EXTREMELY SIMPLE models, with only one or two inputs, and with no effort to add any actual analytical inputs to them, that were almost as accurate as jfresh's. In doing so, they clearly demonstrated that his significantly more complex model is a waste of time because it isn't getting results that are any better than the simple ones. That's the point, which obviously you completely missed to grasp.
But please, go on another rant, making an entirely different (and irrelevant) point, in an attempt to demonstrate that I and others have missed some point which you are trying to make - that always makes for fun reading!
