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Brettapps/trifecta-bro/v1
Version: 1.0.0
Task: Australian Gallops trifecta prediction (pick the top-3 finishers, in order).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 oncedata/results/is populated.
| 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 |
1# Install from the Hub
2# pip install huggingface_hub
3from huggingface_hub import snapshot_download
4path = snapshot_download("Brettapps/trifecta-bro-v1")
5import sys; sys.path.insert(0, path)
6
7from trifecta_bro_v1 import TrifectaPredictor, race_from_payload
8
9payload = {...} # Trifecta-Bro race payload
10race = race_from_payload(payload)
11prediction = TrifectaPredictor().predict(race)
12print(prediction["primary"], prediction["secondary"], prediction["value"])python -m trifecta_bro_v1.main --data predictions-2026-08-10.jsonmodel_artifacts/model_artifact.json documents the model method, feature
weights, and version — making the published model interpretable and reproducible.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.