Researchers Create a Mathematical Model to Optimize Performance and Training for Athletes

According to a study released on Tuesday, researchers have created a mathematical model that promises to maximize training for athletes competing in 400- and 1,500-meter races.

The model is based on performance data collected from professional athletes, such as Norwegian Jakob Ingebrigtsen, the winner of the Olympic 1,500 meters, Femke Bol, the world indoor 400 meters record holder from the Netherlands, and British athlete Matthew Hudson-Smith in the Munich 2022 European Championships.

The co-author of the research that was published in the journal Frontiers in Sports and Active Living, Amandine Aftalion, told AFP, “We wanted to understand what was happening at the physiological level in a 400-meter sprint and a 1,500-meter endurance race.”

With the use of cutting-edge GPS sensors hidden inside competitors’ jerseys, researchers were able to precisely track each athlete’s speed and location 10 times per second.

They combined formulas for computing physiological factors, such as running economy, maximal oxygen consumption (VO2), energy expenditure during exercise, and motor control; in other words, they calculated the part the brain plays in movement, including motivation, which contributes to the delay in taking action.

Scientists from the French National Centre for Scientific Research (CNRS) subsequently analyzed the data and noted how it affected the champions’ speeds.

The CNRS said in a statement that “the model provides instant access to the best strategy so that the runner ‘performs’ in an optimized manner thanks to the quantification of costs and benefits.”

The research highlights the significance of a quick start in the first 50 meters for factors related to oxygen consumption speed or a slower finish time in 400 meters.

The ability of middle-distance runner Ingebrigtsen to rapidly attain and sustain his maximal oxygen consumption (VO2) throughout the race was a key factor in the simulations’ explanation of his success.

Despite his worse start, the Olympic champion is able to maintain a faster pace than his rivals throughout the race, according to Aftalion, because of a unique quality.

The study came to the conclusion that coaches might “refine the racing strategy in relation to the physiological profile of the runner” by using the model, which could lead to performance assistance software.