A GUIDE TO

Sectional Data

What is Sectional Data?

Timing data is the backbone of all sporting analysis, from athletics to Formula 1 racing. Sectional timing is Horse Racing’s answer to this and underpins how we interpret what has occurred in a race. We use our GPS tracking data to assess how horses travel through each section of a race. Each tracker collects the location of each horse at a rate of 18 GPS data points per second. This paints a detailed picture of a horse’s movements throughout a race and delivers key insights into race tactics and performances. In Britain and Ireland races are divided into furlongs (220 yards), in other horse racing jurisdictions around the world it is every 200m.

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The benefits of Sectional Data

Using our data science and engineering capabilities we produce the timing data:

This provides a quick understanding of:

Why is Sectional Data so popular?

As highlighted above, the possibilities for interpreting sectional timing are endless. Layered with our Par Sectionals, it also gives us a revolutionary understanding of how a race has been run. 

Analysts have used this traditional method for years; however, GPS tracking data brings an extra level of accuracy and accessibility. Broadcasters can display graphics during races to keep fans interested and informed, and for those who want to explore the race narrative in more detail, it offers a comprehensive illustration of what has happened. 

This provides participants and stakeholders with the clarity to make informed decisions about a horse’s chances of winning next time, and how to improve those chances by adjusting tactics.

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What the experts say

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“There is no such thing as too much data. I’ve been a professional racing analyst for over 20 years, but even experienced eyes will miss plenty of relevant details and nuance. Well harvested data has a great capacity to inform, surprise and improve understanding. Sophisticated utilisation of data is an area that is only going to grow in racing.”

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Kevin Blake
Presenter and Racing Analyst

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