How we measure load, risk and readiness
Measured load, four SALUS scores, a 14-day forecast and a risk profile built on the literature.
Four numbers that say how the athlete is, today
Each score runs 0 to 100 and is computed from real load, wellness and clinical history. A two-stage model with Monte Carlo simulation then projects them 14 days ahead under different training scenarios.
Risk
How exposed the athlete is right now: load spikes, accumulated fatigue and open risk factors.
Readiness
How ready they are to train today, from recovery, sleep and the wellness questionnaire.
Tolerance
How much load they can absorb before the balance between acute and chronic breaks.
Robustness
The structural base built over months of consistent training and no interruptions.
Every session is weighted by intensity and duration using methods established in the sport science literature: heart rate where wearables record it, perceived exertion where they don't. When neither exists, the day stays flagged incomplete rather than counted as rest — a zero must never read as recovery.
Ask what happens to the scores if next week's load goes up 20%, stays flat, or drops. The forecast returns median and confidence bands per day, so the staff decides before the microcycle starts.
Nine peer-reviewed papers behind the platform
The team behind the platform publishes in international sports science and sports medicine journals. Injury surveillance, risk factors, load monitoring and fatigue prediction are not marketing claims here: they are the subject of the papers below, written by the same people who build the software.
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A risk profile built on the literature, not on a hunch
The risk model implements the umbrella review by Genovesi et al. (2025), Research in Sports Medicine: intrinsic and extrinsic risk factors, the assessment that measures each one, operational thresholds, and the mapping from factor to body area.
An injury is not a linear sum: it emerges from the interaction between the athlete's state and their exposure. The profile is a web of factors that gets reviewed periodically, and each factor over threshold raises an alert on the athlete's page with the suggested assessment.
Sample data — Genovesi et al. (2025) factor taxonomy
Let's see it on your athletes
Tell us how your club works today and we'll show you the platform on a real case: one team, one season, the data you already have.