Performance analysis using regression models shows tasks affected leaderboard outcomes in CrossFit, suggesting patterns for coaches.
The aim of this study was to analyze the impact of tasks on leaderboard changes – both before athlete cuts and in the final standings – at the CrossFit® Games from 2021 to 2024. Developed by Greg Glassman in 2000, CrossFit® is a training methodology that enhances physical well-being through exercises from endurance, gymnastics, and weightlifting & throwing. At the CrossFit® Games, male and female athletes compete for the title of “Fittest on Earth” in their respective divisions. From 2021 to 2024, the competitive season consistently followed four stages: The Open, Quarterfinals, Semifinals, and the CrossFit® Games Final Stage. This study used multiple linear regression models to examine the influence of individual tasks on the leaderboard. Results showed that different tasks had the greatest impact on the standings before the cuts in 2021, 2022, and 2023, and on the final standings in 2021 and 2022. In contrast, in 2023 and 2024, the same tasks had the greatest impact in both divisions. A consistent pattern also emerged in the characteristics of the most impactful tasks, offering valuable insights for coaches. In conclusion, this study highlights the variability inherent to CrossFit® and provides a foundation for future performance analysis in the sport.
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Luzes et al. (2025) studied this question.
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