Welfare
RaceiQ has the potential to support horse racing’s welfare research and analysis with its Data Science and AI capabilities in order to better identify and minimise risk and improve safety on and off the track.
- Data-driven approach to improving equine welfare outcomes
- Using complex tracking data to support enhanced analysis, understanding and study into the relationship between in-race performance and horse welfare outcomes
- Leveraging this capability to generate comprehensive horse welfare analysis
- Supporting racing’s focus on equine welfare
Using metrics and insights to inform understanding
Combining RaceiQ ‘s data insights with welfare and veterinary databases can facilitate deeper understanding and the ability to improve injury prevention and track safety. RaceiQ offers the opportunity for welfare analysts to query and correlate risk factors.
- Predicting horses at risk of in-race injury and key associated factors
- Monitor horses with previous injuries / other identified risk factors
- Correlate jumping efficiency and patterns with fallers and injuries
- Assessing track layouts by comparing in race data against track features such as bend alignment, course topography etc
- Assessing jump characteristics by comparing derived jump metrics
