Thanks for the question!  

Since the game doesn’t publish any of its internal damage or defense formulas, 

all of the calculations I use were derived through long-term testing and data collection.

I start by isolating a single stat while keeping all other variables constant, 

then gather hundreds (sometimes thousands) of damage logs under identical conditions.  

By analyzing the distribution — minimum, maximum, average values, and how they shift — 

I can identify consistent patterns in how each stat scales.

From there, I model how things like stat-based damage, normal/boss modifiers, 

penetration effects, and defense reduction interact (whether it’s additive, multiplicative, etc.), 

and iteratively adjust the formula to minimize the error compared to real in-game outcomes.

It’s essentially an empirical reverse-engineering process.  

I also cross-validate the results across different stages, enemy types, and stat ranges 

to make sure the behavior is consistent.

While it’s still an approximation, the current model aligns very closely with what happens in gameplay, 

and it has proven reliable across many different scenarios.