# Exploratory analysis of critical event phases and the impact of team size and performance levels in 24 hour ultra cycling

## TL;DR

This study examines how team size and performance affect rank predictability in 24-hour cycling races, finding that larger teams and high/low performance teams have more predictable outcomes.

## Contribution

The study introduces CFRD as a novel metric to analyze rank-order stability and identifies critical phases influenced by team size and performance.

## Key findings

- Larger teams achieve clearer final rankings earlier in the race.
- Solo riders show less predictability in final standings throughout the race.
- High- and low-performance teams have more predictable outcomes at early stages.

## Abstract

This study explores the complex dynamics of rank-order stability by analyzing the current-to-final rank difference (CFRD), a metric that provides a dynamic view of rank fluctuations and their impact on the final ranking. This approach enables the identification of critical event phases that significantly influence the final rank. Specifically, we examine how varying team sizes and performance levels shape temporal trends in CFRD during 24-hour cycling races. A comprehensive dataset covering four consecutive years (2019–2022) of a 24-hour cycling race on a 17.9 km repetitive driven road track, encompassing diverse team sizes (solo rider, or teams of 4 and 10), is used. The results indicate significant interactions between time, team size, and performance F(18.04) = 1.74; p = 0.03). As the race progresses, the final standings become progressively more predictable. Solo riders exhibit the least clarity in their final standing throughout the race. In contrast, larger teams achieve a clearer indication of their final ranking earlier in the race. Medium-performance teams, especially solo riders, show lower clarity in their final standing across the race duration, whereas high- and low-performance teams tend to exhibit more predictable outcomes at earlier stages of the race. Overall, this study advances our understanding of endurance team cycling, and offers valuable insights for strategic decision-making and race optimization.