Absol-Mega

dark Damage Calculator
Usage Percent
0%
Monthly Rank
#171

Base Stats for Absol-Mega

HP
65
Atk
150
Def
60
SpA
115
SpD
60
Spe
115

Best Moves for Absol-Mega

Protect
normal
97.551%
Sucker Punch
dark
85.204%
Superpower
fighting
78.509%
Play Rough
fairy
77.928%
Foul Play
dark
13.011%
Feint
normal
11.451%
Will-O-Wisp
fire
9.072%
Swords Dance
normal
6.099%
Knock Off
dark
5.627%
Other
15.547%

Best Items for Absol-Mega

Absolite
100.000%

Best Abilities for Absol-Mega

Magic Bounce
100.000%

Best EV Spreads for Absol-Mega

Jolly
0/
252/
0/
0/
4/
252
77.542%
Jolly
4/
252/
0/
0/
0/
252
8.747%
Adamant
4/
252/
0/
0/
0/
252
4.200%
Jolly
4/
132/
0/
0/
120/
252
2.200%
Careful
244/
12/
0/
0/
252/
0
1.627%
Naive
0/
252/
0/
0/
4/
252
1.318%
Frequently Asked Questions

What are the best moves for Absol-Mega in vgc2016?

The top moves for Absol-Mega in vgc2016 are Protect (97.551%), Sucker Punch (85.204%), Superpower (78.509%), Play Rough (77.928%). These represent the highest usage percentages on the competitive ladder.

What are the best teammates for Absol-Mega?

Absol-Mega synergizes well with Yveltal (79.683%), Sylveon (77.533%), Magmortar (77.530%), Avalugg (77.530%). These Pokemon frequently appear on teams together and cover each other's weaknesses.

What item should I use on Absol-Mega?

The most popular items for Absol-Mega are Absolite (100.000%). The best choice depends on your team composition and strategy.

What ability is best for Absol-Mega?

The most common abilities for Absol-Mega are Magic Bounce (100.000%). The choice often depends on your team's strategy.

What Tera Type should I use on Absol-Mega?

Tera Type data is not available for this format or Pokemon.

What is the best EV spread for Absol-Mega?

The most common EV spread for Absol-Mega is Jolly with 0/252/0/0/4/252 (77.542% usage). This build is optimized for the current metagame.

Is Absol-Mega good in vgc2016?

Absol-Mega is currently ranked in the vgc2016 metagame. Check the usage percentage and teammates section to see how well it performs. The data is aggregated from thousands of high-level ranked battles.

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