Every true count and penetration combination has associated with them a mean composition, and a mean expectation for that composition. ML simply demands that one has to use that mean and that mean alone for discussions about the mean for a given true count.
There are no such requirements. The use of such mean compositions is simply a useful practice for reducing computations needed to find the mean predicted by a given true count.
It is not a requirement to use this mean and never has been. The actual bow effect is simply the result of the totally predictable difference between the mean expected value for the mean composition associated with a given true count and penetration, and the actual compositions and their expected values.
There in fact is no reversion to such means either. As I detailed in my Blame Woggy.... proof, each composition becomes the starting point for all further distributions.
What results is ML adding to this absurd definition requirement an equally absurd demand that this is the only definition for real mathematicians to use and that one is not allowed to post any response unless one uses this same definition.
Again: there are no such requirements! The ruler used is not the thing being measured. They have different means associated with them.
But once you go down this absurd trail with ML, you have to replace a simple difference in measures with the further absurdity that the basic strategy edge, rather than the edge associated with the actual compositions, floats instead. And that is a simple replay of an ultimate absurdity: that somehow measurement of a population changes the mean expected value of any remainder of that population. That is exactly what is involved when you claim that penetration alone changes expected values, which is what you are saying when you say that basic strategy edge changes with penetration!
