Machine learning and AI research using horse racing data spans pace modelling, performance indexing, time-series forecasting and computer vision. Here is an overview of the application landscape from a data infrastructure perspective.
Translating structured racing records into useful ML features requires domain knowledge and careful schema design. This post covers practical feature construction strategies using the HRDB relational schema.
Sectional time data — split times measured at each furlong interval — has expanded significantly across UK racing in recent years. Here is what the expanded coverage means for data-driven analysis.
A practical overview of how data teams connect HRDB racing datasets to their analysis infrastructure — from initial schema mapping to operational daily update pipelines.
North American racing data — Thoroughbred, Quarter Horse, Standardbred — represents one of the largest untapped markets for structured data products. Here is what HRDB is planning for USA and Canada coverage.
Learn why sectional times are vital in horse racing. These times offer insights into performance, running style, and suitability for different races.
When it comes to horse racing data sets in Hong Kong, there is one name that […]
Horse Racing Form & Statistics There are many things that can impact a race. Ground, weight, […]
Race distances are encoded differently in Hong Kong and UK racing. This post explains the conventions used in each jurisdiction and how the HRDB schema normalises them for consistent cross-market querying.
A guide to what HRDB covers in the Hong Kong racing season — including how often data is updated, how far back the archive goes, and what to expect from each type of data record.
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