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Climbing Performance in U23 and Professional Cyclists during a Multi-stage Race
Peter Leo1, James Spragg2, Dieter Simon3
1Department of Sports Science, Division of Performance Physiology & Prevention, University of Innsbruck, Austria.
International Journal of Sports Medicine
|July 15, 2021
Summary
Professional cyclists exhibit superior climbing speed and power output compared to U23 riders. Prior workload and intensity significantly predict climbing performance in categories 1 and 2, but not category 3.
Area of Science:
- Sports Science
- Exercise Physiology
- Cycling Performance Analysis
Background:
- Climbing performance is crucial in multistage professional road cycling races.
- Understanding factors influencing climbing, such as workload and intensity, is vital for training and race strategy.
- Differences between elite U23 and professional cyclists' climbing capabilities require investigation.
Purpose of the Study:
- To analyze climbing performance in professional road cycling across different climb categories.
- To assess the impact of prior workload and intensity on climbing performance in U23 and professional cyclists.
- To determine if climbing performance can be predicted by workload and intensity measures.
Main Methods:
- Comparison of climbing performance metrics (power output, ascent velocity, speed) between U23 and professional cyclists.
- Analysis of workload and intensity measures (total work, elevation gain, eTRIMP) in both groups.
- Statistical modeling to identify predictors of climbing performance based on climb category, workload, and intensity.
Main Results:
- Professional cyclists demonstrated higher power output, ascent velocity, and speed on Category 1 and 2 climbs compared to U23 cyclists.
- U23 cyclists exhibited higher overall workload and intensity measures than professionals.
- Prior workload and intensity significantly predicted climbing performance for Category 1 and 2 climbs (R²=0.27–0.89), but not Category 3.
Conclusions:
- Climbing performance in professional cycling is influenced by the climb's category and the cyclist's prior workload and intensity.
- Category 1 and 2 climbing performance can be predicted using workload and intensity data.
- These findings offer insights into training load management and performance prediction in elite cycling.
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