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Optimising hexa matrix bossing for Angelic Buster

All initial BA data was shared by Boosty (ImSoBoosted). AB discord provided behind the scene numbers.


For each skill, put the total damage they did in the BA's column.
It is recommended to have at least lvl 1 of Da Capo and Hexa Supernova before using this optimiser. If they are level 0, assumptions will be made.
Having Hexa Supernova also turns supernova into a 30s summon for mobbing, making it very useful for training regardless of its bossing damage.
BA total damage

(everything, including skills not listed here)
Grand Finale total

(no Cheering Balloons)
Cheering Balloons total

(not Seeker)
Trinity total

(not Fusion)
Seeker total

(not Cheering Balloons)
Supernova total

If hexa Supernova is not unlocked, input the 4th job supernova total.
Spotlight total
Mascot total
Sparkle Burst total
Trinity Fusion total

(not Trinity)
Da Capo total

-blank space-
Sol Janus is not shown here as it is not used for bossing. Each player considers the importance of exp/mob clear differently, so it should be prioritised according to your personal peference.
Enter the current Hexa levels of each skill:
Grand Finale level

Hexa Trinity level

(not Fusion)
Hexa Seeker level

Hexa Supernova level

-blank space-
Hexa Spotlight level
Hexa Mascot level
Hexa Sparkle Burst level
Hexa Trinity Fusion level

(not Trinity)
Da Capo level

For origin ied/boss boost at lvl 10/20/30, and hexa stat
E.g. put 5 to mean that unit will give you 5% FD or multiply your dps by 1.05x
FD% per 30 raw att
%
FD% per 200 flat (Not Affected by %) dex
%
FD% per 8% crit dmg
%
FD% per 40% boss dmg
%
FD% per 40% ied
%
If the FD%s aren't known, I recommend using https://maplescouter.com to fill in the character's stats and adjust the 'Value' column under 'Stat Efficiency'.
The units asked for match maplescouter's Stat Efficiency.

Setting a high number of trials could make the simulation take a long time as the simulation is repeated for each Hexa Stat level.
Number of trials:
Disclaimer: Due to Hexa Stat being random, the data here may not match your specific case. Only the average case is considered.

Results: