Cardiovascular Fitness and Stride Acceleration in Race-Pace Workouts for the Prediction of Performance in Thoroughbreds.

Simple Summary: Data collected from training sessions has the potential to better understand how well racehorses will perform on race-days. To date, no research has explored whether speed during and heart rate recovery after fast exercise could predict a horse winning a race. This theory was tested by analysing information from fitness trackers worn by 485 racehorses during training sessions in Australia by looking at factors like the horse's speed and heart rate recovery after exercise. The study found that certain factors, like being a colt or a 'stayer', were good indicators of future success on the racetrack. However, heart rate recovery and speed at the start or throughout the final 600 m during training were not as powerful as race predictors. Nevertheless, combining physiological data with other less tangible factors will help professionals identify which horses are more likely to perform at their best in races, aiding decision-making and potentially reducing race-day poor performance. Ultimately, this could lead to more successful outcomes for the horses, trainers and owners. In-training racehorse physiological data can be leveraged to further explore race-day performance prediction. To date, no large retrospective, observational study has analysed whether in-training speed and heart rate recovery can predict racehorse success. Speed (categorised as 'slow' to 'fast' according to the time taken to cover the last 600 m from a virtual finish line) and heart rate recovery (from gallop to 1 min after exercise) of flat racehorses (n = 485) of varying age, sex and type according to distance (e.g., sprinter, miler and stayer) were obtained using a fitness tracker from a single racing yard in Australia. Race-pace training sessions on turf comprised 'fast gallop' (n = 3418 sessions) or 'jumpout' (n = 1419). A posteriori racing information (n = 3810 races) for all 485 racehorses was extracted and combined with training data. Race performance was categorised as win/not-win or podium or not, each analysed by logistic regression. Colts (p < 0.001), stayers (p < 0.001) and being relatively fast over the last 600 m of a benchmark test in training (p < 0.008) were all predictive of race performance. Heart rate recovery after exercise (p = 0.21) and speed recorded at 600 m of a 1 km benchmark test in training (p = 0.94) were not predictive. In-training physiological data analytics used along with subjective experience may help trainers identify promising horses and improve decision-making. [ABSTRACT FROM AUTHOR]