From Data to Decision: Turning Sprint and Jump Numbers Into Training Prescriptions
Summary
Collecting testing data is the easy part. Knowing what to do with it is harder. Here's a practical framework for moving from force-velocity profiles and jump test scores to specific training decisions.
The athlete testing industry has never been more sophisticated or more accessible. Force plates, timing gates, friction sleds, and validated protocols have moved from lab to field. Data collection is no longer the constraint. Interpretation is.
This is the challenge at the centre of applied sports science: turning numbers on a screen into training decisions that improve performance. Here is a framework for doing it.
Step 1: Establish the Baseline and the Question
Before any test data is meaningful, you need to know what question you are asking. "How are my athletes doing?" is not a question that test data can answer — it is too broad. Useful questions are specific:
- Is this athlete force-limited, velocity-limited, or well-balanced for sprint performance?
- Is this athlete's reactive strength sufficient for their sport demands?
- Is this athlete recovered enough to train power today?
- Is the training block producing the intended physical adaptation?
Define the question before selecting the test. The test follows the question; the question does not follow the test.
Step 2: Match the Test to the Question
Question | Test
Force-velocity balance for sprinting | Sprint F-V profiling (Morin-Samozino)
Reactive strength capacity | Drop jump RSI
Slow SSC function | CMJ, CMJ–SJ comparison
Maximal strength | IMTP or 1RM derivative
Neuromuscular readiness | Weekly CMJ height trend
Limb asymmetry | Single-leg hop battery, single-leg drop jump
Do not test everything all the time. Choose a focused panel for your current phase and question, and be disciplined about it.
Step 3: Apply Decision Thresholds
Data without thresholds is observation, not decision-making. For each variable you monitor, define in advance what constitutes:
- Normal variation (do nothing different): e.g., CMJ height within ±3% of rolling average
- Flag for monitoring (increase attention, reduce load if other signals align): e.g., CMJ height down 5–8%, RSI down >10%
- Act immediately (reduce training load, exclude from high-intensity work): e.g., CMJ height down >10% from recent peak
These thresholds should be individualised. Cormie et al. (2011) noted that the smallest worthwhile change in jump height is approximately 1.5–2% for trained athletes — meaning sub-2% changes are within noise. But individual athletes can have idiosyncratic response patterns that differ from group norms.
Step 4: Translate Profile Data to Training Emphasis
From a force-velocity profile, the prescription logic is direct:
Force-deficient (FVimb > 1.0):
- Increase heavy resisted sprint work (sled: 40–60% velocity decrement)
- Emphasise maximal strength in the gym — particularly hip extension patterns
- Reduce light sled and unresisted acceleration work in the short term
Velocity-deficient (FVimb < 1.0):
- Increase unresisted sprint volume and sprint-speed plyometrics
- Reduce sled loading — light loads only if using a sled at all
- Prioritise power development at moderate loads rather than maximal strength
Balanced profile (FVimb ≈ 1.0):
- Maintain balance — no skewed emphasis
- Focus training on improving absolute Pmax through either end of the curve
- Use general power development work (moderate sled, broad plyometrics)
Step 5: Close the Loop — Re-Test and Evaluate
A training decision that is never re-evaluated is not a training decision — it is a guess. Re-profile at the end of the training block (typically 8–12 weeks) and compare to the baseline. Did the FVimb shift in the intended direction? Did Pmax improve? Did jump height trend upward over the block?
Morin & Samozino (2016) used case studies to demonstrate that individualized prescription based on FVimb produced greater specific improvements than generic training — but only when re-testing was used to confirm the profile shift and adjust the next block accordingly.
The loop is: test → decide → prescribe → train → re-test → evaluate → adjust. Collecting data is step one. Closing the loop is what makes it useful.
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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