Patch?

[Bayes]

Thanks for the thorough feedback and taking my input into consideration, Strela!

The problem I see with the existing range formula

1 + (N-1) * (R - 1)

is that when you change R from e.g. 1.5 to 1.2 you affect all weapons alike. E.g., if Tiger I becomes twice as effective at range 6, T-34 becomes twice as effective as well. As a result, changing R won´t change kill ratio because the range effect change is cancelled out when you calculate the ratio. You can only control the amount of kills overall by changing R. What I would like to do is to reduce the effectiveness of T-34 at range 6 and at the same time increase the effectiveness of Tiger I. The formula I propose achieves this effect.

Here is another formula that almost exactly replicates the Range Tables from JTCS, with similar properties (makes long rage weapons more effective):



Range Effect = 1/(1 - (3/4) * ((N-1)/(M-1)))

N - Range
M - Max range of weapon


Maybe using these well-tested values from JTCS could be an interesting option too.

BTW. I guess I should clarify why I bring up the need for long range weapons to be more effective at long range.

Here is a scenario where 20 T-34 attack 24 8.8cm Flak from a range of about 2.5 km. Because of the quickly degrading effect of ranged weapons in the current system, it turns out that most of the combat in this scenario take place at Range 1, after the T-34 have moved a full turn or so. I would have expected that the majority of T-34 would be lost at longer range, as they approach. Maybe also increasing the chance of Opportunity Fire would help here (I have spread out the 8.8cm Flak to maximize opportunity fire occurrences):



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Also, thanks for the interesting link with the analysis of combat between T-34 and Tiger I, Richie61.

For Panzer Battles, using the current system, I guess a corresponding analysis would be:

Range 7+; Tiger I superior (T-34 cannot harm Tiger I).
Range 1-6; constant kill ratio of 4:1 in favor of Tiger, with more random variation at closer range.
Assault by T-34; kill ratio of 4:1 in favor of Tiger.

Bayes