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: rankerrows: 1292train_rows: 1024validation_rows: 268races: 124train_races: 99validation_races: 25top1_accuracy: 0.24mean_ndcg_at_3: 0.3809487605714332race_normalized_log_loss: 0.3578525368255854roc_auc: 0.6179423868312758
Feature Importance
expert_prior_score: 1110.0574form_class_score: 950.1305rel_sectional_vs_class_avg: 857.8760rel_api_index: 829.7493rel_barrier: 760.1593speed_map_score: 736.7255rel_margin_beaten_last_start: 707.3938sectionals_score: 660.5257rel_track_condition_win_pct: 609.6845rel_last_600m_rank: 548.6328track_env_score: 510.0266rel_distance_mastery_pct: 320.8251rel_early_speed_rating: 81.1268projected_settling_position: 18.3684rel_days_since_last_run: 6.8742rel_market_firm_factor: 0.0000rel_jockey_trainer_strike_rate: 0.0000rel_track_layout_score: 0.0000human_market_score: 0.0000rel_weight_delta: 0.0000
Files
model.joblib: trained LightGBM estimatormodel.txt: native LightGBM booster text dumppreprocessing.json: feature schema and race-relative scaling metadatatraining_config.json: training settingsmetrics.json: validation metricsfeature_importance.csv: gain-based feature importance