Patch?

[Bayes]

ComradeP,

the Range Effect formula in the first example uses fire range N and max weapon range M to calculate Range Effect:

1 + 4 * (N - 1) / (M - 1)

For fire ranges N = 1, …, 16, the Range Effect table becomes as follows:



Here are some more ideas that might address penetration and quality amplification, which you bring up in your post.

Right now, Armor Effectiveness is based on RAW Hard Attack Values. Not before after Armor Effectiveness has been resolved are Range Effect and Quality modifiers introduced. So, for Tiger firing at T-34 we get the factor 1.33, which then is modified by the Range Effect and Quality modifiers. Similarly, for T-34 we get the factor 0.5, again to be further modified by the Range Effect and Quality modifiers. This produces the following weapon performance stats currently:



If we introduce the Range Effect formula 1 + 4 * (N - 1) / (M - 1) we get the following weapon performance stats:



To get the additional effect you request, I believe maybe one idea would be to apply Range Effect and Quality modifiers directly on Raw Hard Attack Value first. Then, one applies the Armor Effectiveness rule on the range and quality modified Hard Attack Values. The resulting weapon performance stats would be as follows:



using Range Effect 1 + 4 * (N - 1) / (M - 1). Notice how expected kills for Tiger stays rather stable for a while, then quickly drops due to the Armor Effectiveness rule.

I guess the Tiger becomes too superior in the latter table, but this may be fixed by recalibrating the available game parameters. I have no idea how the above idea would work overall, but maybe something to investigate (if not already done).

Bayes

Bayes and Strela: the new proposals still don't take penetration data into account, or how they could be amplified through the quality dynamic.

JTCS values would also allow a Panzer IV G/H to destroy a T-34 at 1.500 meters, which is not realistic if you define the defensive value as mostly being based on frontal armour and overall protection.

Bayes: in your first example, unless I'm mistaken you started substracting the modifier at range 1 instead of range 2 and the modifier I get for the PaK 40 is 1.22 whilst yours seems to be a different one.

1+(4*(1.5-1)/(10-1))->1+(4*0.5/9)->1+0.222=1.222.