Profitability
Having said that, your sim of the scenario you considered does show a strong improvement in win rate, SCORE, N0, etc as you note. The increase in action shows that you are identifying more big bet situations, so the 0.2% increase in advantage refers to a bigger "outlay" of chips.
iCountNTrack, one other thing that I think is not always fully appreciated about the NRS formula and its parameters is that it provides quite a general way to analyze games. You correctly mention that you considered a simple shuffle that would be hard to find for actual play. But your sim results actually apply equally to any game (with the same rule set) in which, by whatever means, you can track a half deck into a single deck in a game using ? decks (sorry, I forgot whether you considered 6, 8 or some other number of decks).
More generally, we could describe a game with the following parameters:
d, p, q, k
where d = number of decks, p = shuffle point, q = size of playzone, k = slug size.
Suppose, to take an example, that we can track 1 deck (possibly from disparate parts of the shoe) randomly shuffled into a 2 deck playzone in a 6 deck shoe with 4 decks dealt. So we have:
(d, p, q, k) = (6, 4, 2, 1)
The values of d, q, and k imply values for the NRS formula's N and r.
If you can analyze the profitability of this game (either analytically or through simulation), your results will apply to any game or any situation that produces these parameters. It doesn't matter what shuffle was used, or which cards you tracked. All that matters is that d = 6, q = 2, k = 1.
This also gives a handy way to select games. If you run sims for idealized shuffles (not necessarily realistic ones) that produce appropriate values for d, q, and k, you can rank different scenarios for profitability, risk/return, etc. For 4/6 deck games, you might consider the following cases:
(d, p, q, k) = (6, 4, 3, 0.75)
(d, p, q, k) = (6, 4, 3, 1.50)
(d, p, q, k) = (6, 4, 3, 2.25)
(d, p, q, k) = (6, 4, 3, 3.00)
Analyzing these cards can give upper limits on the profitability of various "best half" scenarios. Analysis can be undertaken for different rule sets, penetration levels, etc. Approaches other than "best half" can be analyzed similarly.
So your sim gives an upper limit to the profitability of one category of track game: namely, 1 deck shuffled into 2, irrespective of how (as long as randomly).