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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I was trying to prove that an average error of 10.4 is not good, only slightly better than picking random numbers. All of those inputs in the JFresh model make it no better than some simple model that has nothing at all to do with the sport or the league. So there's something wrong with it. If the JFresh model is any good, it should be measurably better, regardless of how that result was reached. If you're inputting a whole bunch of information that you think is relevant but your results are no more accurate, then maybe what you think is relevant isn't, or isn't weighted properly. It's not the concept of the JFresh model that's the problem, it's the model's design.