TILES: Attack damage inflicted from tiles


#1

Are tiles based on attack status?

Wilbur has 605atk considered (A) heroes grid 7dd TILES
Proteus has 707atk considered (B) heroes grade 7dd Tiles

Why Wilbur has better tiles?
How was this statistic done?

Based on set with Hero Special?

:slightly_smiling_face:


#2

I think it’s becauee when Wilbur fires his special his tile damage increases a lot


#3

The full answer is here: Damage Calculation

Short answer: yes, the damage done by a tile depends on several factors:

  • the hero(es) behind it. If you have only one hero of each color, then the attack stat of that hero is key. If you have two or more of a color, the damage is calaculated for each and added.
  • the troops under he hero. These bump up the attack stat of he hero. The gains can be substantial, and substantially different across troops.
  • attack buffs/debuffs active on the hero
  • defense buffs/debuffs on the target(s)
  • color relationships. Strong/neutral/weak, based on the color wheel shown in game. Wilbur’s tiles falling on a green hero,will do a lot more damage than Proteus on the same green hero. On a yellow hero, that relationship flips.
  • crit chance. Some troops give a chance of a crit, which does double damage (neutral to color).

#4

I’ve read the topic, I do not know if I understood correctly, but the higher the ATK of DEF would have the greater damage of tiles, and if ATK is lower or equal to DEF it would be minor damage, I did not understand if that was correct

If this table no existed, I would never know of so many complex information, I want to thank the genius who created this table of heroes 7dd God bless a lot brother :pray:


#5

There’s no hard edge about the attack vs defense stats. Higher attack is always better. The take-away from the formula should be that going from, say, 400 to 420 is not as big as going from 420 to 440 (all else equal). The second case gives a bigger boost in damage.

Also note that there is a large random component, so you’d really need a lot of hits to be sure of any conclusion. Which is why we all owe @SolemnWolf a big debt for the careful work of assembling a large dataset.