Welcome once more to the final part in this series for now. Warning up front: This article will not contain (much) new information, but will serve, as the title implies, as a conclusion on the previous parts I-IV, summarizing the findings, and perhaps serving as an easy reference piece. So let’s go over what we covered in the previous parts.
- Other parts in this series:
>continued
Over the first four parts, we looked at finding a way of making the right choices concerning gear and Gems, and how we could compare different sets of gears with different sets of Gems. Here is a small recap of each part:
Part I. The basic Battle Formula
In the first part we looked at the formula’s used in battle in the the Hobbit KoM. We started with the simple observation of troop kills being equal to (attack/life). From there, we worked out how buffs and debuffs worked, and figured them into this basic formula. The end result was (eq.11):
For any combat situation, two of these formula’s are used, in which the attacking force fills in the orange factors, and the defending party the blue factors. As combat is fought simultaneously, you would theoretically fill in the orange factors once to see how many enemy troops you would kill, and the blue factors once to see how many troops the opponent kills. That is, IF we were to know each and every buff and debuff.
In part two we took a small step back and examined briefly how all the different buffs could interact, because we have a lot. We made a separation here between knowing the exact battle outcomes (by knowing each and every buff and debuff and exactly how they interact), and knowing relative battle outcomes, by focusing on the important gear buffs only, and being able to compare different scenarios. In going through the math, we changed the attack buff in (eq. 11) to the more specific attack buff_gear term:
Part III. Why your opponent doesn’t matter
In part three we further developed the notion of focusing on relative performances of gear sets versus absolute. Through rather simple math on (eq.19), we showed that your own gear performance is completely unrelated to what kind of gear your opponent is wearing. With this, we could remove all the factors pertaining to the opponent’s factors in the equation:
Note that from here on, we were forced to deal with separate equation for attack and defense gear sets. Shown above is the formula to check a defensive gear set’s performance.
Besides removing the opponent’s factors, we also got rid of the base life and base attack factors, as they aren’t interesting or needed for relative comparisons. We were left with one final formula for defensive sets:
and one for offensive sets:
Part IV. Diminishing Returns and how to deal with it
In the final part we are left with the question on how to best optimize the performance of a gear set. We saw that besides the notion of “higher is better” we had to deal with Diminishing Returns (DR). Through simple math examples, we saw how DR works, and how it is, in principle, best to even out buffs and debuffs:
But we also saw that you shouldn’t even out buff/debuff at any cost, and that sometimes, a higher but more unequal buff/debuff distribution could still outperform a lower but even buff/debuff gear set.
Lastly we went into balancing out your gear’s buffs and debuffs using the Gems at your disposal, and to always do the math yourself on new gear combinations to see if it works better than your old set, using eq.20 and eq.21.
Conclusion
For now, this will be the last part in this series of Battle Mechanics. There are still many other topics on combat in the Hobbit that might deserve their own article, but these will have to wait. With these articles, I think I have given every KoM player enough material to understand how to make decisions on gear and Gems, and some background knowledge on the mechanics and mathematics behind this part of the game.
I’ve enjoyed making this series, and am happy I can finally close the book on it. But if you have any comments or suggestions for future work, let me know in the comments below, and as always: Happy Hobbiting!
ouch, i didn’t know there would be math involved in this game. Going back to PONG!!
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Thanks for an excellent explanation. What still isn’t clear to me, is how to calculate that single value of a buff to use in formula 20 & 21. If I have for example an Orcrist III with 4 attack debuffs of 9.6%, 36.1%, 52.3% & 18.7%. Do I add all the percentages together (116.7%) plus the values for 4 off lv 8 sapphire gems (64% ea = 256%), thus giving a total of 372.6%? Does that make the value I use in formula 20 for Att debuff gear (1+3.726)?
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The following has happend,
my city was open, def hero Thorin, good gear over 1000/1000 buff/debuff.
I was wiped out by an attack of somewhat stronger hero with only woodys
Lost in 1 attack over 700k SG
Of course my opponet had slighly higher city buffs etc but the most impact had his 4x att skill bow II in combination with fierce loyality.
I am no good in math but i think thats missing in your equasions… the skills
Or did i miss something?
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A thorough and engaging exploration of the topic. Thank you for such a helpful article.
On my server, players tend to always attack cities with T3 troops because “it gives the best ratios”. Would love to see your thoughts on best configurations to attack with generally, and what troops to leave in a city when you open.
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thank you! and T3 and T1 are indeed ratio/might wise best options for TK.
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Sehr gut. Endlich habe ich es verstanden. Weiter so!
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Danke sehr!
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Thanks BM, excellent explanation. Been working on the full battle formula… would love to see something on that if you ever have the time, and also a decent CHS formula, the one I have is out of date.
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Next project we can try to figure out the exact formula will all buffs and debunks in it maybe. We already did some test and there are still some mysteries
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yes I saw your comments and am happy to be updated with your progress!
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Great job!
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Excellent series; thank you!
Trying to convey this sort of information via chat is terribly difficult. I can now point people to this series and have them come back to me when they’ve read it a few times and understand it. 😉
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