| ---
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| license: mit
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| tags:
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| - tabular-regression
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| - sklearn
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| - card-game
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| - flesh-and-blood
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| - game-balance
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| - educational
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| library_name: sklearn
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| pipeline_tag: tabular-regression
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| datasets:
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| - custom
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| metrics:
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| - r2
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| - mse
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| - mae
|
| ---
|
|
|
| # FAB Prediction Model - Teklovossen Card Balance
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|
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| > **Educational Project - NOT affiliated with Legend Story Studios or the official Flesh and Blood game.**
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|
|
| A scikit-learn model that predicts **win rates** for custom Teklovossen (Mechanologist)
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| 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
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| using simulated gameplay data from a custom game engine.
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|
|
| ## Model
|
|
|
| | Detail | Value |
|
| |--------|-------|
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| | Type | `Pipeline(StandardScaler + RandomForestRegressor)` |
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| | Task | Tabular regression - predict average win rate |
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| | Framework | scikit-learn 1.8.0 |
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| | Training data | Simulated gameplay (custom engine) |
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|
|
| ## Features (inputs)
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|
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| | # | Feature | Description |
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| |---|---------|-------------|
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| | 0 | AI | AI-themed card count |
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| | 1 | Nano | Nano-themed card count |
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| | 2 | Quantum | Quantum-themed card count |
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| | 3 | Base | Base card count |
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| | 4-8 | 0_cost to 4_plus_cost | Cost curve distribution |
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| | 9-11 | equipment, item, action | Card type counts |
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| | 12 | avg_turns | Average game length |
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| | 13 | avg_evos | Average Evo equipment used |
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| | 14 | avg_teklo_energy | Avg Teklo Energy generated |
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| | 15 | avg_nanite_counters | Avg Nanite counters |
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| | 16 | avg_quantum_charges | Avg Quantum charges |
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| | 17 | consistency_score | Deck consistency (0-1) |
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| | 18 | avg_expert_balance_score | Expert balance score (1-10) |
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|
|
| ## Output
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|
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| `avg_win_rate` - float in [0, 1] representing predicted deck win rate.
|
|
|
| ## Metrics
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|
|
| | Metric | Value |
|
| |--------|-------|
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| | Train R2 | 0.917 |
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| | CV R2 (mean +/- std) | -1.3332 +/- 2.1072 |
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| | Train MSE | 0.0049 |
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| | Train MAE | 0.042 |
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| | Samples | 9 |
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| | Features | 17 |
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|
|
| ## Quick Start
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|
|
| ```python
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| from huggingface_hub import hf_hub_download
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| import joblib, numpy as np
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|
|
| path = hf_hub_download(repo_id="4math/FAB_Prediction_Model", filename="model.joblib")
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| model = joblib.load(path)
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|
|
| # Example: a Nano-focused deck
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| 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]])
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| print(model.predict(features)) # predicted win rate
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| ```
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|
|
| ## About This Project
|
|
|
| This is a fan-made educational project exploring ML for TCG game balance.
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| The model is trained on simulated gameplay data from a custom Flesh and Blood
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| game engine featuring Teklovossen, a Mechanologist hero with custom Evo cards
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| across four tech themes: AI, Nano, Quantum, and Biomancy.
|
|
|
| ## Limitations
|
|
|
| - Trained on **simulated** data, not real tournament results
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| - Small dataset (9 samples) - treat predictions as rough estimates
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| - Simplified card mechanics in the simulation engine
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| - Fan-made cards only - does not cover the official card pool
|
|
|
| ## License
|
|
|
| MIT
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| |
