A paper in Frontiers attempted modelling the optimization of world-class 400 m and 1,500 m running performances using high-resolution data.
“In the present study, rather than using statistical analyses of 100-m split times or big data, we choose to analyze a select sample of World-class athletes individually using high-resolution data and fit a mathematical model to their pacing profiles. This mathematical model allows us to perform further predictive simulations and analyze the effect of different physiological variables on performance. “
The model used in this analysis is a deterministic system of equations depending on half-a-dozen parameters like (of course!) the VO² max. Most of them “are not measured for any individual athlete (e.g., using experimental methods), but are estimated through a computation to fit the data for a specific race and athlete.”
The authors then turn cranks to see how each parameter increases the speed curve, as in the graph below for Femke Bol winning the 2022 European Championship 400m in München and the impact of increasing final VO², anaerobic energy e⁰, or propulsive force. And the same for Jakob Ingebrigtsen’s 1,500m win [above graph]—in a time shorter than my current 1,000m best! What is unclear to me is how the model repercusses the toll of such increases on the athlete’s other parameters—since Ingebrigtsen already has an amazing VO² max!. Some of the changes leads to a 5sec improvement, which is quite large at record levels—the World record for men moved by 1s between 1998 and 2022. I’d thus be interested in a field experiment where one of these top runners would attempt to implement the suggested changes in their pace to see how much this impacts the other factors… Another experiment with the fitted model is to check the impact of the lane allocated to the runner for a 400m race, the fastest lane being the first one despite higher centrifugal forces on the bends. Which seem to have a lesser if still noticeable effect on the 1,500m race [above graph].
