How fast do we slow down? -- Handicapping running and Swimming competitions.

Thomas M. Menino Convention & Exhibition Center 

The success of masters programs in running and swimming competitions has made available a vast amount of data. Using these data for athletes aged 35 to 85, we model the percentage increase in event time to complete several events (including both sprints and long distance) in both running and swimming. We use a stacked model that includes polynomial and neural network models as well as smoothing splines. We bootstrap the procedure to obtain confidence intervals which can help answer fundamental questions about the nature of the age decline. We then turned our attention to the Dipsea, the oldest continuously held U.S. footrace (since 1905), handicapped by age since 1965 and by age and sex since women were officially admitted in 1971. Seeing that no one between the ages of 8 and 40 had won the race, we assumed that their handicapping system was flawed. What we discovered was surprising.

Stacked models

Bootstrap

Cohort effects


Aging

Neural networks

Sports analytics