Pacing strategy from high-frequency field data: more evidence for neural regulation?

Area of Science:

• Sports Science
• Biomechanics
• Data Analysis

Background:

• Analyzing athlete pacing strategies in races with significant elevation changes has been challenging.
• Existing methods often rely on pre-defined pacing models that may not fit real-world race dynamics.

Purpose of the Study:

• To develop and demonstrate a novel methodology for uncovering true athlete pacing strategies from high-frequency split data.
• To enable scientific scrutiny of previously opaque undulating professional and amateur races.

Main Methods:

• A statistical method utilizing high-frequency split times and altitude-distance data from Global Positioning System (GPS) devices.
• Includes a preliminary discovery step to identify appropriate pacing functions, moving beyond pre-set models.
• Applicable in standard statistical packages.

Main Results:

• Demonstrated the methodology on Haile Gebrselassie's world-record Berlin Marathon and the Six Foot Track Ultramarathon.
• Revealed highly variable micro-scale pacing strategies in both case studies.
• Identified macro-scale symmetry in pacing for one case.

Conclusions:

• The developed method effectively uncovers detailed pacing strategies in diverse race conditions.
• Findings support a complex systems perspective on neural regulation in athletes.
• Opens new avenues for analyzing race dynamics in undulating terrain.