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title: Trifecta-Bro v1 — Australian Gallops Trifecta Predictor
emoji: 🐎
colorFrom: green
colorTo: blue
sdk: "false"
tags:
- racing
- horse-racing
- australian-gallops
- trifecta
- prediction
- sports-betting
license: mit
library_name: other
---
# Brettapps/trifecta-bro-v1
**Model id:** `Brettapps/trifecta-bro/v1`
**Version:** 1.0.0
**Task:** Australian Gallops **trifecta prediction** (pick the top-3 finishers, in order).
Trifecta-Bro v1 is an open-source, multi-factor trifecta scorer for Australian
gallops. Given a race's field + form, it assigns each runner a 0–100 score and
emits a **primary**, **secondary**, and **value** trifecta combination, plus the
top-3 ranked runners with win/place probabilities.
> **Status:** v1 is a deterministic rule-based scorer. No supervised training
> was performed because the project has no historical race **results** (labels)
> yet. v2 will train a gradient-boosted / logistic model on observed outcomes
> once `data/results/` is populated.
## Method
For each runner, a weighted 0–100 score is computed from:
| Factor | Max weight |
|--------|-----------|
| Recent form (last 5 starts: 1/2/3 finishes) | 25 |
| Career overall win % | 20 |
| Career overall place % | 10 |
| Track strike rate (places/starts) | 10 |
| Distance strike rate | 8 |
| Condition strike rate (per going) | 8 |
| Barrier draw | 5 |
| Career prize money | 5 |
The three highest-scoring runners form the **primary** trifecta. A **secondary**
and **value** combination are derived from the next-best runners (with an
outsider angle when a score > 30 exists further down the field).
## Usage
```python
# Install from the Hub
# pip install huggingface_hub
from huggingface_hub import snapshot_download
path = snapshot_download("Brettapps/trifecta-bro-v1")
import sys; sys.path.insert(0, path)
from trifecta_bro_v1 import TrifectaPredictor, race_from_payload
payload = {...} # Trifecta-Bro race payload
race = race_from_payload(payload)
prediction = TrifectaPredictor().predict(race)
print(prediction["primary"], prediction["secondary"], prediction["value"])
```
Or run the bundled CLI:
```bash
python -m trifecta_bro_v1.main --data predictions-2026-08-10.json
```
## Artifact
`model_artifacts/model_artifact.json` documents the model method, feature
weights, and version — making the published model interpretable and reproducible.
## Backend note
This model is also wired as the `Brettapps/trifecta-bro/v1` identity in the
Trifecta-Bro LM Studio / Obsidian-vault backend. The HF-published code is the
canonical, dependency-light inference implementation.
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