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A Load-Velocity Relationship in Sprint?

Roland van den Tillaar1, Sam Gleadhill2, Pedro Jiménez-Reyes3

  • 1Department of Sports Sciences, Nord University, 7600 Levanger, Norway.

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|September 27, 2023
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Summary
This summary is machine-generated.

This study found that regression analyses often underestimate sprint running maximal velocity. Polynomial regression using both resisted and assisted loads provided the closest estimation to measured velocity.

Keywords:
assisted sprintsforce–velocitylaserresisted sprintsrobotic pulley system

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Area of Science:

  • Sports Science
  • Biomechanics
  • Human Movement

Background:

  • Accurate measurement of maximal velocity (vVmax) is crucial for athletic performance analysis.
  • Load-velocity relationships are commonly used to predict vVmax, but their accuracy depends on the methods used.

Purpose of the Study:

  • To compare predicted vVmax from various load-velocity relationships against measured vVmax during sprint running.
  • To compare vVmax measurements between a robotic pulley system and a laser gun.

Main Methods:

  • Sixteen male sprinters performed sprints under various resisted and assisted load conditions.
  • Maximal velocity was measured using a laser gun and a robotic pulley system.
  • Predicted vVmax was calculated using linear and polynomial regression analyses with different load combinations.

Main Results:

  • The robotic pulley system and laser gun yielded similar vVmax measurements, except at a 10% velocity reduction load.
  • All tested regression analyses underestimated the measured vVmax, with differences ranging from 0.78 to 6.7%.
  • Polynomial regression incorporating both resisted and assisted loads offered the most accurate prediction, underestimating measured vVmax by only 2.3%.

Conclusions:

  • Standard regression methods tend to underestimate maximal sprint velocity.
  • Using polynomial regression with both resisted and assisted loads improves the accuracy of predicted maximal velocity in sprinters.