Quantifying the limits of human athletic performance: A Bayesian analysis of elite decathletes

TL;DR

This paper develops a Bayesian model to estimate the upper limits of human athletic performance in decathlon, capturing performance trends and dependencies across events.

Contribution

It introduces a Bayesian composition model that forecasts decathletes' performances and assesses the maximum achievable scores considering event dependencies.

Findings

The model can simulate the distribution of maximal possible decathlon scores.

It identifies decathlete profiles likely to approach performance limits.

The approach captures non-linear performance trends over time.

Abstract

Because the decathlon tests many facets of athleticism, including sprinting, throwing, jumping, and endurance, many consider it to be the ultimate test of athletic ability. On this view, estimating the maximal decathlon score and understanding what it would take to achieve that score provides insight into the upper limits of human athletic potential. To this end, we develop a Bayesian composition model for forecasting how individual decathletes perform in each of the 10 decathlon events of time. Besides capturing potential non-linear temporal trends in performance, our model carefully captures the dependence between performance in an event and all preceding events. Using our model, we can simulate and evaluate the distribution of the maximal possible scores and identify profiles of decathletes who could realistically attain scores approaching this limit.