A groundbreaking mathematical model designed to enhance the training optimization for athletes in the 400-meter and 1,500-meter athletics events has been developed, as detailed in a study published on Tuesday. The model, based on performance data from elite athletes, including Olympic 1,500 meters champion Jakob Ingebrigtsen, world indoor 400m world record holder Femke Bol, and Britain’s Matthew Hudson-Smith, offers insights into the physiological aspects of middle-distance running.
Co-author Amandine Aftalion explained that the researchers aimed to comprehend the physiological dynamics in both sprint (400 meters) and the first endurance race (1,500 meters). Leveraging GPS sensors placed under athletes’ jerseys, the study precisely tracked the speeds and positions of each athlete, capturing data ten times per second. Equations factoring in energy expenditure, maximum oxygen consumption (VO2), running economy, and motor control were integrated to analyze the impact of these variables on the athletes’ speeds.
The French National Centre for Scientific Research (CNRS) scientists examined the data, focusing on how it influenced the champions’ performance. The model, offering instant access to the best strategy for optimized performance, could potentially lead to performance support software for coaches to refine racing strategies based on the physiological profile of the runner.
The study highlighted the significance of a rapid start in the first 50 meters and the role of oxygen consumption speed. The simulations provided insights into Ingebrigtsen’s performance, emphasizing his unique ability to quickly reach and maintain maximum oxygen consumption throughout the race, allowing him to sustain a faster pace than competitors.
Ultimately, this innovative model opens the door to advanced performance support tools, enabling coaches to tailor racing strategies to the physiological profiles of individual runners.
