| --- |
| 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. |
|
|
