Most data analysis projects on horse racing start with the same question: how do I get from raw historical results to a working model or dashboard? The answer depends on your tooling, your team size and what you are trying to measure — but the pipeline structure is largely consistent across projects.
Phase 1: Schema Mapping and Initial Load
The first step is loading the historical archive and validating that the field structure matches your analytical needs. HRDB exports include a data dictionary covering every field name, type, encoding convention and nullable status. For teams using PostgreSQL or MySQL, our SQL dump format preserves the relational structure including foreign keys — the schema is ready to query as-is without transformation.
Phase 2: Feature Construction
Once the base tables are loaded, feature construction is a SQL or Pandas exercise. Common derived features include rolling form averages per horse, going-adjusted performance scores, jockey win rates at specific courses, and pace-derived metrics from sectional time data. Because the HRDB schema is normalised and consistent across seasons, these queries generalise across the full archive without per-year adjustments.
Phase 3: Daily Update Integration
For operational pipelines — where you need yesterday’s results in the database before today’s racecard goes live — HRDB provides two integration paths: API polling (GET /results?date=today) for near-real-time ingestion, or SFTP-delivered daily update files for teams with existing file-based ETL infrastructure. Both paths deliver the same canonical data in the same schema as the historical archive.
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