Stage 1 Battle Mechanic Test Results
For all tests the following stats are applicable:
Attacker Stats
Hero: 197
Attack Rune: 10
Healing Potion: 10
Blood Lust/Frenzy: OFF
Defender Stats (Level 9 Goblin Camp)
Hero: 0 (Assumption)
Attack Rune: 9
Healing Potion: 9
OVERVIEW
The idea of these simulations is to gather enough data to make progress in determining an accurate model
of the battle mechanic. No single formula is ever going to accurately capture the battle mechanisms.
Instead a series of formulas will most likely be formed over time in order to try and best represent the
battle mechanism. A level 9 goblin camp was chosen as it contains only Artillery troop types. This allows
only other Artillery troop types to be sent as there should be no bonus or penalty when Artillery troop
types fight each other. Each simulation is run five times in order to determine a reasonably accurate
average.
ATTACKER RIDUCOULSY OUTNUMBERED
To simulate attacking a player defending with 25.6 million might 100 archers were sent against a level 9
goblin camp which had 1000 archers, 600 ballistae and 500 catapults. Multiplying everything by 1000
results in simulating 100k archers being sent against a player defending with a huge army. In pure troop
numbers the attacker is outnumbered 21 to 1 whilst in terms of might they are out gunned by 64 to 1.
The attacker lost all troops on each attack whilst an average of 81.8 troops were lost on each attack by the
defender. There was a positive deviation of 3.8% and a negative deviation of 3.4%.
ATTACKER OUTNUMBERED
To simulate attacking a player defending with 2.56 million might 1000 archers were sent against a level 9
goblin camp which had 1000 archers, 600 ballistae and 500 catapults. Multiplying everything by 100 results
in simulating 100k archers being sent against a player defending with a large army. In pure troop numbers
the attacker is outnumbered 2.1 to 1 whilst in terms of might they are out gunned by 6.4 to 1.
The attacker lost all troops on each attack whilst an average of 822.4 troops were lost on each attack by
the defender. There was a positive deviation of 2.0% and a negative deviation of 2.4%.
DEFENDER OUTNUMBERED
To simulate attacking a player defending with 256k might 10000 archers were sent against a level 9 goblin
camp which had 1000 archers, 600 ballistae and 500 catapults. Multiplying everything by 10 results in
simulating 100k archers being sent against a player defending with a medium army. In pure troop numbers
the defender is outnumbered 4.8 to 1 whilst in terms of might they are out gunned by 1.6 to 1.
The defender lost all troops on each attack whilst an average of 1319.8 troops were lost on each attack by
the attacker. There was a positive deviation of 3.0% and a negative deviation of 2.4%.
DEFENDER RIDUCOULSY OUTNUMBERED
To simulate attacking a player defending with 25.6k might 100000 archers were sent against a level 9
goblin camp which had 1000 archers, 600 ballistae and 500 catapults. Multiplying everything by 1 results in
simulating 100k archers being sent against a player defending with a small army. In pure troop numbers
the defender is outnumbered 48 to 1 whilst in terms of might they are out gunned by 16 to 1.
The defender lost all troops on each attack whilst an average of 1310.2 troops were lost on each attack by
the attacker. There was a positive deviation of 2.7% and a negative deviation of 1.6%.
ASSUMPTIONS
It is assumed that might is not a true indicator of troop strength. This seems to be supported by the large
discrepencies seen in past battles. In game the various Tiers are given a star value to indicate their attack
and life. Those star values have been converted into numerical values and a modifier applied based upon
several factors. The following formula has been used to calculate the Modifier:
Code:
Modifier = 1 + (Hero / 200) + (Research / 20) + (Item / 5)
Where:
•
Hero: The level of Hero used in the the attack.
•
Research: The level of Research, ie Attack Rune for attack modifier and Healing Potion for life
modifier.
•
Item: Whether or not Item buffs were uses, attack modifier and life modifier. Where 1 indicates
used and 0 indicates not used.
By placing in the correct values the Attack Modifer and Life Modifer for both the attacker and defender can
be determined. Therefore the following modifiers will need to be calculated:
1.
2.
3.
4.
Winner Attack Modifier
Winner Life Modifier
Loser Attack Modifier
Loser Life Modifier
LIMITATIONS
All attacks consisted of only Tier 1 troops. This limits any conclusions to single Tier attacks only. Test data
for multi-tiered attacks will have to be gathered to expand on battle mechanic model.
CONCLUSION
The very first thing to note is there is definitely a random factor of between 1.6% and 3.8% involved. It is
not a huge deviation and for the purposes of determining the battle mechanic model it can be ignored. As
long as calculated values fall within the deviation then they can be considered as accurate.
Another consistant observation is the deviation between calculated results and expected results when one
side is considerably outnumbered. From the above tests it was shown that approximately a 50% bonus was
applied to the winner. That is the winner had their losses reduced by 50%. Additional tests were run to
confirm or deny this and a new pattern emerged. Attacks were performed at a range of percentages above
the calculated strength of the defender. What emerged was an inverse curve relationship between the
bonus applied and the strength of the army sent in comparison to the defender. The greater the difference
the greater the bonus but it seemed to be maxed out at around 50%. The following formula was
extropalated from the data gathered:
Code:
Winners Bonus = 5 * SQRT(Difference)
Where:
•
Winners Bonus: The bonus applied to the winner, to determine actual troop losses, in percentage.
•
Difference: The percentage by which the winner overpowers the loser. ie Difference = ((Winners
Attack / Losers Attack ) * 100) - 100
Note:
1.
Winners Bonus has a ceiling of 50%
The Winners Bonus formula is by no means perfect but is proving to be relatively consistent with the data
gathered. Occassionaly a result will appear that is outside of the expected Random Deviation. That would
suggest one of the following possibilities:
1.
2.
3.
4.
The Winners Bonus formula is wrong
The Random Deviation is greater than expected
The method of calculating Actual Strength is wrong
Some other unknown factor is having an effect
An initial formula can be concluded from the test data. It will need to be updated as more data is gathered
but could be considered as a general rule of thumb for now.
Code:
Winners Losses = Losers Attack / Winners Life Modifier - ((Losers Attack / Winners Life Modifier) *
(Winners Bonus / 100))
A great many more simulations are needed to start drawing an accurate conclusion. This is just the first in
many many steps. Input from others is greatly appreciated but please give detailed results otherwise the
data is useless.