A data-driven approach to the "Everesting" cycling challenge.

Area of Science:

• Sports Science
• Cycling Performance Analysis
• Endurance Sports

Background:

• The Everesting challenge, a cycling feat of climbing Everest's elevation in a single ride, saw increased participation during the COVID-19 pandemic.
• Completion time for Everesting is influenced by cyclist fitness, hill characteristics (gradient, distance), and strategy.

Purpose of the Study:

• To identify and rank key parameters influencing Everesting completion time.
• To segment cyclists based on performance and characteristics using machine learning.
• To provide data-driven recommendations for optimizing Everesting attempts.

Main Methods:

• Web-scraping to compile a comprehensive database of Everesting attempts.
• Quantitative analysis to determine the impact of various parameters on completion time.
• Unsupervised machine learning for cyclist segmentation.

Main Results:

• Cyclist power-to-weight ratio and the hill's gradient-distance tradeoff are critical factors.
• Elite cyclists are advised to select hills with gradients exceeding 12%.
• Amateur and recreational cyclists are recommended to choose hills with gradients below 10% for optimal performance.

Conclusions:

• Understanding the interplay between cyclist capabilities and course selection is vital for Everesting success.
• Strategic hill selection based on cyclist level can significantly reduce Everesting completion times.
• The study provides actionable insights for cyclists aiming to complete the Everesting challenge efficiently.