Solutions
See Injury Risk Early. Act Sooner.
Be-Healthy.AI analyses player and football-context data every day to identify elevated injury-risk patterns and help performance and medical teams decide where attention is most needed.
The value is not certainty. It is earlier attention.
What You Receive
Six outputs, all of them about where to direct attention next. None of them a diagnosis.
Identify elevated injury risk
Surface the players whose risk profile has moved, so attention goes where the signal is rather than where the last conversation was.
Monitor risk evolution
Risk is a trajectory, not a snapshot. Follow how a profile develops across a season instead of reading a single number on a single day.
Rank players by relative risk
Compare within your own squad, so the question becomes who needs attention first, not whether a score is high in the abstract.
Identify fragility patterns
Recurrence and return-to-play patterns that are hard to notice match by match and clear over a season.
Support preventive decision-making
The output is a prompt to look, timed early enough to still have options. The decision stays with your medical and performance staff.
Benchmark squad risk
See how your squad sits against the wider league rather than only against itself.
Risk Moves. That Movement Is the Signal.
A single high score is a snapshot and easy to argue with. A profile climbing week after week is a reason to look now rather than later, and a profile falling is just as informative, because it helps teams reprioritise their attention.
Tested Forward, Not Fitted Backward
Model training was cut off at 30 June 2025. Everything since has been live monitoring across the 2025/26 Premier League season, so the results below were never available to the model that produced them. It ran on publicly available data only: no club systems, no wearables, no integrations.
- Critical risk band
- 5.6×
- observed injury frequency, against the league average
- Low risk band
- None
- recorded no injuries across the sample
- Monitoring
- Live
- out-of-sample, 2025/26 season
Not Every Injury Can Be Seen Coming
Soft-tissue injuries often develop through accumulated damage, creating precursor signals we can model. Collision injuries result from sudden external events with no reliable precursor for the system to detect, so we do not attempt to forecast them. Being clear about that boundary is part of what makes our predictions credible.
Anticipated
Patterns the system is built to surface early.
- Muscle injuries
- Tendon injuries
- Non-contact ligament injuries
Not anticipated
Events with no meaningful precursor.
- Contact ligament injuries
- Traumatic fractures
- Trauma from collisions
Noise
Excluded rather than modelled.
- Non-injuries
- Unknown injuries
- Other
We Analyse the Football Context You Don't See
Player profile
- Age
- Height
- Nationality
- Position
- Dominant foot
Career & club
- Matches
- Career trajectory
- Transfers
- Tenure
- Exposure
Injury history
- Injury type
- Severity
- Recurrence
- Contact / non-contact
- Recovery patterns
Player Workload
- Match load
- Selection & Utilisation
- Fatigue proxies
- Rest days
- Dynamics
We Bring the External Context.
You Add What Only You Can Measure.
Be-Healthy.AI works with publicly available football data, giving organisations an independent external layer of intelligence. Clubs can enrich that picture with their own training, wellness, medical and performance information when available.
Be-Healthy
Public football data
Your organisation
Internal data
Result
Better decisions
Which Decision Would You Like to Improve?
Tell us, and we'll show you what the data already says about your squad.