Category: Data Science

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FASERIP-Slugfest – rats on the run

Things are tough in ratland when you add in a cat’s killing ability. Maybe we should go James Herbert next and have a rat pack versus a person as a test. Not that those are giant mutant killer rats. Although possible good superhuman testing ground?

Battles: 1101; Sum of rounds: 6336; rodent: 0.0 ± 0.0; feline: 1.01 ± NA; rodent: 0.0 ± 0.0; feline: 1.099 ± NA;
Team rodent = winning battles: 2; perfect battles: 0; close-call battles: 0;
Team feline = winning battles: 1099; perfect battles: 0; close-call battles: 0;
------------------------------------------------- Combattants ------------------------------------------------
Rat: {team=rodent; avg hp=0.005449591280653951 (from 8); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.6412352406902815; hits/slams/stuns/kills/misses (PBA)= 1.8206176203451407/0.0/0.0/0.0/2.751135331516803; rounds (PBA)=4.572661217075386;}
Cat: {team=feline; avg hp=16.717529518619436 (from 24); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.997275204359673; hits/slams/stuns/kills/misses (PBA)= 1.9986376021798364/0.0/0.45049954586739327/0.05131698455949137/2.9981834695731155; rounds (PBA)=5.4959128065395095;}
Rat: {team=rodent; avg hp=0.005449591280653951 (from 8); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.6412352406902815; hits/slams/stuns/kills/misses (PBA)= 1.8206176203451407/0.0/0.0/0.0/2.751135331516803; rounds (PBA)=4.572661217075386;}
Cat: {team=feline; avg hp=16.717529518619436 (from 24); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.997275204359673; hits/slams/stuns/kills/misses (PBA)= 1.9986376021798364/0.0/0.45049954586739327/0.05131698455949137/2.9981834695731155; rounds (PBA)=5.4959128065395095;}

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FASERIP-Slugfest

I mentioned Matteo Ferla’s DnD-battler yesterday.

https://github.com/bluetyson/FASERIP-Slugfest

I have started adapting this to work with FASERIP. Obviously the mechanics are different, but the basic framework is there, which is why I wanted to use it – all the hard work with creatures, encounters, arenas, running many simulations and tallying them just needs different characteristics and algorithms inserted.

Very much a work in progress, this is like version 0.01.

You can do this, however :-

————————————————————————————————————–
Battles: 100; Sum of rounds: 506; feline: 0.97 ± 0.02; rodent: 0.03 ± 0.02;
> Team feline = winning battles: 97; perfect battles: 0; close-call battles: 0;
> Team rodent = winning battles: 3; perfect battles: 0; close-call battles: 0;
————————————————- Combattants ————————————————
Rat: {team=rodent; avg hp=-0.56 (from 8); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.31; hits/misses (PBA)= 1.655/2.305; rounds (PBA)=3.975;}
Cat: {team=feline; avg hp=9.38 (from 16); avg healing spells left=0.0 (from 0); damage done (per battle average)= 4.28; hits/misses (PBA)= 1.62/2.595; rounds (PBA)=4.7;}
Rat: {team=rodent; avg hp=-0.56 (from 8); avg healing spells left=0.0 (from 0); damage done (per battle average)= 3.31; hits/misses (PBA)= 1.655/2.305; rounds (PBA)=3.975;}
Cat: {team=feline; avg hp=9.38 (from 16); avg healing spells left=0.0 (from 0); damage done (per battle average)= 4.28; hits/misses (PBA)= 1.62/2.595; rounds (PBA)=4.7;}

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DnDBattler

Matteo Ferla has written a python 5E D&D encounter simulator that I have been meaning to get to for some time. Too many real life models to deal with until now.

https://github.com/matteoferla/DnD-battler

He also has an online version here:- https://dnd.matteoferla.com/

http://squidonius.blogspot.com/

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AI: The Lost Irulan

He is not a philosopher nor a scholar, nor a poet. He is not a warrior nor a lover, nor a man of science. He is a poet of the earth, of the stars, of the cosmos, of the universe, the lord of all the world. He is justness without and justice within.

—’Muad’Dib: The God King of Arrakis’ by the Princess Irulan

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Wilderness Simulator Stats – Python

I took the java version from http://cosmicheroes.space/blog/index.php/2021/12/27/wilderness-simulator-stats/

and converted to python – there’s a jupyter notebook here:-

WildernessEncounterSim – Jupyter Notebook

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Wilderness Simulator Stats

https://deltasdnd.blogspot.com/2021/12/wilderness-simulator-stats.html

https://github.com/danielrcollins1/WildernessEncounterSim

https://blogger.googleusercontent.com/img/a/AVvXsEiZNO5GyTtLj1jEZzeQGwR4zXi5a99JUClzZsiJl8h85vSN3nGdwVblRTkf1aQ8ImJa7KuNHWKlxgkbZHCZNTaSMQGMP9cX_EKjI3VnO9VdZ6xvG0Aea_pL3xgmpwhzplVokkKMXfikOeuogXu8F-9VSghSC0ctQBt0NRY7jruSeRWpb_M5Ul1ZRwGKQA=w200-h111

Wilderness Encounters: Clear (log chart)
Wilderness simulator distribution

“One more reflection on the Original D&D wilderness encounter charts. Last week we were using some tabulated charts to decide between two possible rules interpretations, and one was clearly much nicer. But that was based on just looking at the average EHD (Equivalent Hit Dice) for each encounter type, which is maybe a little sketchy. Since I’m obsessive about these things, I wrote a simulator program that actually rolls up the individual encounters (varying the number appearing by psuedo-random dice), and I had it spit out a thousand random encounters for each terrain type.”

This all looks like pretty reasonable results to me!

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