Fatigue and the Force-Velocity Profile: How Tiredness Changes Your Athletes

Mark Fisher

⏱ 3 min read

Summary

Fatigue doesn't uniformly reduce performance. It shifts the force-velocity curve in specific, predictable ways—and understanding how changes what you monitor and when you train what.

Fatigue is not a single thing. It is a collection of processes — metabolic, neural, mechanical — that reduce performance through different mechanisms at different time scales. Understanding how fatigue affects the force-velocity curve specifically gives coaches and scientists a more precise language for describing what is happening to an athlete's output and why.

What Happens to the F-V Curve Under Fatigue

The force-velocity relationship does not shift uniformly when an athlete is fatigued. Morin et al. (2012) showed, in the context of repeated sprint testing, that fatigue preferentially affects the high-velocity end of the F-V spectrum. Maximum velocity declines earlier and more substantially than maximum force.

This makes physiological sense. High-velocity contractions depend on phosphocreatine resynthesis rate, fast-twitch motor unit recruitment efficiency, and neuromuscular drive — all of which decline rapidly with fatigue. Maximum force production, while certainly affected by fatigue, is partially maintained by slower fibre types and can be supported by altered motor unit strategies.

The practical consequence: a fatigued athlete's force-velocity curve rotates clockwise — V₀ drops more than F₀, and the profile becomes more force-dominant. This is not a meaningful change in the athlete's physical qualities; it is a fatigue effect. Interpreting it as genuine force-velocity imbalance would lead to incorrect training decisions.

Monitoring Implications

This is why testing timing and conditions matter enormously for F-V profiling. An athlete tested at the end of a high-volume training week will appear more force-dominant than they actually are. Testing them fresh will produce a different profile. Neither is wrong — both are accurate reflections of the athlete's state at that moment — but only the fresh profile reflects their true underlying quality.

For training monitoring purposes (as opposed to profiling), the shift in the curve can be informative. A progressive leftward shift in V₀ (with relatively stable F₀) over a training block is consistent with accumulating neuromuscular fatigue. If the protocol is consistent, this shift is real and actionable.

Girard et al. (2011), studying fatigue in tennis players, demonstrated that the mechanical determinants of repeated sprint performance declined in a characteristic pattern — velocity metrics declining first, force metrics showing relative resilience — consistent with the Morin et al. findings in team sport contexts.

Jump Testing and the Fatigue Signature

In practical monitoring settings, force plate CMJ data captures some of this fatigue signature:

- RSI and reactive jump metrics decline early: The fast SSC is the first casualty of accumulating fatigue
- CMJ jump height shows modest decline: Maintained better than reactive metrics as fatigue accumulates
- Contact time in drop jumps increases: The athlete defaults to longer contacts to maintain height when reactive capacity falls
- Eccentric rate of force development declines: The ability to absorb and redirect force rapidly is attenuated before peak concentric force is meaningfully affected

A testing battery that includes both reactive (drop jump RSI) and non-reactive (CMJ height) tests will therefore detect fatigue earlier and with more nuance than either test alone.

Training Around the Fatigue Curve

The understanding that fatigue preferentially affects the velocity end of the F-V spectrum suggests that high-velocity training (fast plyometrics, sprint work, light resisted work) should be prioritised when the athlete is freshest — earliest in the week, earliest in the session, away from heavy strength training sessions. Placing velocity-sensitive work after heavy loading is not just suboptimal; it trains the athlete under conditions that specifically suppress the quality you are trying to develop.

Heavy strength work, which is less velocity-sensitive, is more tolerant of fatigue and can be scheduled accordingly.

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.