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.
Learn why sectional times are vital in horse racing. These times offer insights into performance, running style, and suitability for different races.
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.
HRDB UK dataset coverage spans flat and National Hunt racing across Great Britain and Ireland. Here is what is included, the start date for each data type, and how to access it.
What happens if a horse is pulled out of the race or it ends up head […]
We’ve put together a decimal odds to fractional odds conversion table of some of the most […]
Predicting outcomes in horse racing is one of sports analytics most complex modelling challenges. This post covers the data requirements, modelling approaches and key considerations for ML researchers.
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