Why ChampionBred
--------
Why ChampionBred
  • Deep Learning for Australian Breeding Decisions Our system analyses all interactions in the pedigree—not just surface-level statistics—to give breeders and owners a deeper, clearer picture of a horse’s potential. It’s built specifically for the complexities of Australian bloodlines and racing conditions.
  • A Purpose-Built, Unique Approach. We know that no single system can predict the Melbourne Cup and a Kentucky Derby winner the same way. That’s why our approach isn’t one-size-fits-all. We tailor our analysis to different race types and goals, cutting through the noise to deliver insights that actually matter to breeders and owners looking at all interactions in the pedigree helping you choose the best horse.
  • Proven, Validated Results. We don’t just rely on theory. Our approach uses two layers of validation, and real-world testing has shown even better results. You can make breeding and purchasing decisions with greater confidence.
  • Validated Results. Two layers of validation + already even better results in real tests.
  • Unique Approach.
  • Can any system out there predict if a horse will win the Kentucky Derby and also with the same operations who will win the Melbourne CUP? Probably impossible, that is why we have different approaches for different needs. Otherwise, it is noise.
What do we do?

We help you breed or find the right horse in a proven way.

How?

We’ve built our own specialized database and let our models learn directly from it, identifying the patterns in pedigrees that influence the chances of producing a top-class racehorse.

By analyzing these patterns, we can look at any pedigree and predict whether the likelihood of success is high or low. It’s a powerful tool to support smarter breeding and purchasing decisions.

Why ChampionBred
Models being tested. We test to see if models defeat a random selection when horses rate above average.
How well does it work?

Is that enough validation? No—we wanted to be absolutely sure. So we added a second layer of validation outside of the AI itself.

We used the bootstrap method to test whether the model’s success could just be luck. By simulating many random scenarios, we measured how often chance alone could match the model’s results.

The outcome? The probability of achieving better results than our models purely by luck is less than 1%. That gives breeders and owners confidence that our predictions are genuinely meaningful—not just random noise.

These two layers of validation—plus our past comparisons of our top picks against actual race results—are what set us apart from other tools.

With Championbred, you get insights that can deliver the kind of advantage a top stallion might offer—but at a fraction of the cost. It’s about making smarter, more cost-effective decisions without sacrificing quality.

Why ChampionBred
Schematic view of the bootstrap validation process on model 1. The performance achieved by the model is compared with the performance achieved by N random selections, being N a number large enough to be statistically representative of the underlying real sample distribution of horses. The distribution below shows the probability of accidentally obtaining any number of positives. As shown by the vertical line, the probability of accidentally obtaining the same number of positives as the model is below 1%.
Want to know more?

Go to our BLOG section, where you will find articles and news.