---
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.