Case Study: Accelerating Greyhound Performance Analytics

A detailed case study on how precise data annotation transformed greyhound racing analytics and performance tracking.

Greyhounds are fast. Like, 45 miles per hour fast. Trying to track them with the naked eye is hard enough; trying to analyze their biomechanics in real-time is nearly impossible for a human. But for the racing industry, understanding that speed—and the health of the dogs—is paramount. This isn't just about who wins; it's about welfare and safety.

We partnered with a leading analytics firm in the racing space to tackle this problem. They needed to track specific keypoints on the dogs—shoulders, hips, spine—to detect gait anomalies that could indicate an injury before it became serious. The problem? The footage was blurry, the dogs were bunched together, and the background was a chaotic blur of sand and rails.

Our team dove in. We didn't just draw boxes; we mapped skeletons. We annotated thousands of frames of racing footage, teaching the computer vision model to distinguish between Dog A’s front left leg and Dog B’s back right leg, even when they were a tangled mess of fur and speed in a turn. It was like trying to solve a jigsaw puzzle that was being thrown out of a moving car.

The result? A system that can flag a micro-change in a dog's stride length that is invisible to the human eye. Trainers can now pull a dog from a race before a minor strain becomes a major tear. It’s a perfect example of how data annotation isn't just about numbers; it’s about care. Plus, we now know more about greyhound anatomy than we ever thought possible. Did you know they spend 75% of their race time in the air? Neither did we, until the data told us.