Abstract
The masters movement in swimming and running has exploded, resulting in an abundance of data to study the impact of age on performance. Analyzing data from masters events in running and swimming for athletes aged 35 to 80, we model the percentage increase in event time (or decrease in performance) by age and sex via stacked models that combine polynomial models, neural networks, and natural splines. To answer fundamental questions on the nature of performance decline for competitive athletes, we bootstrap the procedure to obtain confidence intervals. Cross-sectional masters data from the past decade are used to construct models, and the model predictions are compared to the trajectory of current world records by age and to estimates of decline using longitudinal data. Furthermore, the study explores the impact of constituent year, birth cohort, and participation effects, emphasizing the challenges in distinguishing age-related decline from factors like evolving training practices and varied participation rates. Our results give evidence that men generally decline more slowly than women, performance declines more rapidly for endurance events, athletes who participate more frequently decline more slowly than others, and masters level runners decline at rates roughly equivalent to world record holders.