File size: 2,762 Bytes
b3d2174 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
license: apache-2.0
tags:
- horse-racing
- australia
- gallops
- lightgbm
- ranking
- tabular-classification
pipeline_tag: tabular-classification
---
# Australian Gallops Tipper
LightGBM `ranker` model for ranking Australian thoroughbred race fields.
The model uses FormFav-derived form/field variables and Racing Australia result
labels from a Supabase `ml_feature_matrix` style dataset.
## Intended Use
- Rank runners within a single Australian gallops race.
- Estimate field-normalized win probabilities.
- Support form analysis and model research.
This is not financial advice and must not be treated as a guaranteed betting
system.
## Feature Logic
Numerical inputs are min-max scaled within each `race_id`, so each horse is
evaluated relative to its field. Lower-is-better variables such as barrier,
days since run, last start margin, and last 600m rank are inverted after
scaling.
The model also receives expert bucket scores:
- Speed map and pace: 25%
- Form and class: 25%
- Track and environment: 20%
- Sectionals: 15%
- Humans and market: 15%
This baseline was trained from the local `2026-08-01` FormFav and Racing
Australia comparison archives. Several unavailable live variables are currently
proxy-filled, including true early sectionals, last-start weight, jockey/trainer
historical strike rate, and market firming. Treat this as a first real data
artifact, not a production betting model.
## Validation Metrics
- `mode`: ranker
- `rows`: 1292
- `train_rows`: 1024
- `validation_rows`: 268
- `races`: 124
- `train_races`: 99
- `validation_races`: 25
- `top1_accuracy`: 0.24
- `mean_ndcg_at_3`: 0.3809487605714332
- `race_normalized_log_loss`: 0.3578525368255854
- `roc_auc`: 0.6179423868312758
## Feature Importance
- `expert_prior_score`: 1110.0574
- `form_class_score`: 950.1305
- `rel_sectional_vs_class_avg`: 857.8760
- `rel_api_index`: 829.7493
- `rel_barrier`: 760.1593
- `speed_map_score`: 736.7255
- `rel_margin_beaten_last_start`: 707.3938
- `sectionals_score`: 660.5257
- `rel_track_condition_win_pct`: 609.6845
- `rel_last_600m_rank`: 548.6328
- `track_env_score`: 510.0266
- `rel_distance_mastery_pct`: 320.8251
- `rel_early_speed_rating`: 81.1268
- `projected_settling_position`: 18.3684
- `rel_days_since_last_run`: 6.8742
- `rel_market_firm_factor`: 0.0000
- `rel_jockey_trainer_strike_rate`: 0.0000
- `rel_track_layout_score`: 0.0000
- `human_market_score`: 0.0000
- `rel_weight_delta`: 0.0000
## Files
- `model.joblib`: trained LightGBM estimator
- `model.txt`: native LightGBM booster text dump
- `preprocessing.json`: feature schema and race-relative scaling metadata
- `training_config.json`: training settings
- `metrics.json`: validation metrics
- `feature_importance.csv`: gain-based feature importance
|