4math/FAB_Prediction_Model

FAB Prediction Model - Teklovossen Card Balance

Educational Project - NOT affiliated with Legend Story Studios or the official Flesh and Blood game.

A scikit-learn model that predicts win rates for custom Teklovossen (Mechanologist) card decks in the Flesh and Blood trading card game.

This project explores how machine learning can be applied to game balance analysis using simulated gameplay data from a custom game engine.

Model

Detail	Value
Type	Pipeline(StandardScaler + RandomForestRegressor)
Task	Tabular regression - predict average win rate
Framework	scikit-learn 1.8.0
Training data	Simulated gameplay (custom engine)

Features (inputs)

#	Feature	Description
0	AI	AI-themed card count
1	Nano	Nano-themed card count
2	Quantum	Quantum-themed card count
3	Base	Base card count
4-8	0_cost to 4_plus_cost	Cost curve distribution
9-11	equipment, item, action	Card type counts
12	avg_turns	Average game length
13	avg_evos	Average Evo equipment used
14	avg_teklo_energy	Avg Teklo Energy generated
15	avg_nanite_counters	Avg Nanite counters
16	avg_quantum_charges	Avg Quantum charges
17	consistency_score	Deck consistency (0-1)
18	avg_expert_balance_score	Expert balance score (1-10)

Output

avg_win_rate - float in [0, 1] representing predicted deck win rate.

Metrics

Metric	Value
Train R2	0.917
CV R2 (mean +/- std)	-1.3332 +/- 2.1072
Train MSE	0.0049
Train MAE	0.042
Samples	9
Features	17

Quick Start

from huggingface_hub import hf_hub_download
import joblib, numpy as np

path = hf_hub_download(repo_id="4math/FAB_Prediction_Model", filename="model.joblib")
model = joblib.load(path)

# Example: a Nano-focused deck
features = np.array([[9,0,0,2, 5,2,1,0, 9,9,12, 0.6, 9.4,13.6,7.3, 0.74, 6.7, 0,0]])
print(model.predict(features))  # predicted win rate

About This Project

This is a fan-made educational project exploring ML for TCG game balance. The model is trained on simulated gameplay data from a custom Flesh and Blood game engine featuring Teklovossen, a Mechanologist hero with custom Evo cards across four tech themes: AI, Nano, Quantum, and Biomancy.

Limitations

• Trained on simulated data, not real tournament results
• Small dataset (9 samples) - treat predictions as rough estimates
• Simplified card mechanics in the simulation engine
• Fan-made cards only - does not cover the official card pool

License

MIT