Longitudinal Master Track and Field Performance Decline Rates Are Lower and Performance Is Better Compared to Athletes Competing Only Once

Abstract

In master athletics research, cross-sectional data are easier to obtain than longitudinal data. While cross-sectional data give the age-related performance decline for a population, longitudinal data show individual trajectories. It is not known whether athletes who repeatedly compete have (a) a better performance and (b) a slower age-related decline in performance than that obtained from cross-sectional data from athletes competing only once. To investigate this, we analyzed 33 254 results of 14 118 male athletes from 8 disciplines in the database of "Swedish Veteran Athletics." For each discipline and for the pooled data of all disciplines, quadratic models of the evolution of performance over time were analyzed by ANCOVA/ANOCOVA using MATLAB. The performance was higher in athletes with 2 or more data points compared to those with only n = 1 (p < .001), with further increases in performance with an increasing number of data points per athlete. The estimated performance decline was lower in people with 2 or more results (sprint, 10 km, jumps; p < .001). In conclusion, we showed that longitudinal data are associated with higher performance and lower performance decline rates.

Keywords: Athletics; Big data; Computational model; Life span; Longevity.