How to Use a Friction-Resistance Sled for Force-Velocity Profiling
Summary
A friction-resistance sled removes the guesswork from sprint loading. Here's exactly how to use one to generate valid force-velocity profiles in a field setting.
Resisted sprint work has been used for decades. What's changed is the precision with which we can now quantify the load and interpret the output. A friction-resistance sled — one that provides a load proportional to normal force rather than requiring the user to add and remove weight plates — enables consistent, repeatable loading across conditions, which is exactly what force-velocity profiling requires.
Why Load Consistency Matters for Profiling
The Morin-Samozino sprint profiling method requires that each loaded condition produces a known, consistent resistance. Plate-loaded sleds can achieve this, but friction-resistance models offer an advantage: the drag force remains proportional to the surface and load without relying on the athlete or coach to correctly assemble and verify the added mass.
Cross et al. (2018) demonstrated that load-velocity relationships generated from friction-based sleds produced highly reliable F-V profiles when testing conditions were standardised. The key is knowing the friction coefficient of your surface — and using the same surface for every testing session.
Setting Up a Profiling Session
Session requirements:
- A flat, consistent surface (same section every session)
- A timing system that captures mean velocity over the first 15–20 m (timing gates, radar gun, or a validated inertial device)
- Accurate body mass measurement
- Sled with known properties for your surface
Conditions to test:
- One unloaded sprint (sled towed but empty, or no sled, with consistent procedure)
- Three to four loaded conditions at increasing resistance
- Minimum 4 minutes full recovery between each
Load selection depends on the athlete's body mass and the surface friction coefficient. As a starting point, target loads that produce approximately 10%, 25%, 40%, and 55% velocity decrements relative to the unloaded condition. If you don't know these in advance, a simple pre-session to establish rough velocity at a moderate load will help calibrate the session.
Data Collection and Profile Calculation
For each condition, record:
- Mean velocity over the timed section
- The load applied (converted to a horizontal force using the friction coefficient × normal force)
Plot force on the y-axis and velocity on the x-axis. Fit a linear regression. The intercepts give you F₀ (y-intercept) and V₀ (x-intercept), and Pmax sits at F₀/2 × V₀/2.
Several freely available spreadsheets and apps automate this calculation. The Morin & Samozino (2016) supplementary materials include a workbook that handles the regression and outputs FVimb alongside the primary profile metrics.
Interpreting the Output
The force-velocity imbalance (FVimb) index tells you how the athlete's actual profile compares to the theoretically optimal profile for their Pmax:
- FVimb > 1.0: Force-deficient profile. The athlete is under-producing force relative to their velocity capability. Training priority: heavier loaded sprints, strength work targeting horizontal force application.
- FVimb < 1.0: Velocity-deficient profile. The athlete is under-producing velocity relative to their force capability. Training priority: lighter loads, higher velocities, unresisted acceleration work.
- FVimb ≈ 1.0: Well-balanced profile. Both ends of the spectrum are developed relatively equally.
Re-test every 8–12 weeks to track profile shifts in response to training.
A Note on Surface Consistency
If you test on a rubber track in week 1 and synthetic turf in week 8, you cannot directly compare the profiles — the friction coefficients differ. Consistent surface is non-negotiable. Establish a testing surface and stick to it.
References
Mark Fisher
Founder, Swift Performance
Mark Fisher is the founder of Swift Performance and has spent 30 years designing and building athlete testing equipment used by elite sport programmes and universities worldwide. He has worked alongside researchers and PhD candidates across biomechanics, sprint mechanics, and strength science — developing the hardware and software they use to collect and analyse performance data. His writing comes from three decades at the intersection of applied sport science and precision measurement technology.
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