---
license: mit
tags:
  - tabular-regression
  - sklearn
  - card-game
  - flesh-and-blood
  - game-balance
  - educational
library_name: sklearn
pipeline_tag: tabular-regression
datasets:
  - custom
metrics:
  - r2
  - mse
  - mae
---

# 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](https://fabtcg.com/) 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

```python
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