Live evidence
Live EPL Monitoring Shows Meaningful Separation of Injury Exposure
- Proven
- Players in the Critical class recorded 5.6× the observed injury frequency of the league average in this sample.
- Proven
- Player trajectories show risk building ahead of injury events and falling during absence and recovery windows.
- Live
- The system is useful as an attention-ranking layer, helping staff decide which players merit earlier review.
Be‑Healthy.AI ran its early-warning system live across the 2025/26 English Premier League season. The model was trained only on data available up to 30 June 2025, so it ran out-of-sample on a season it had never seen, using publicly available data only — no club systems, no wearables, no integrations.
Two results
Risk classes separate. Players in the Critical class recorded 5.6× the observed injury frequency of the league average across the sample. The Low class recorded none.
Escalation is visible before the event. Selected trajectories show the same operational pattern repeating: risk builds ahead of an injury, falls during the absence, then rebuilds after return. A single spike is noise. A recurring build-up, reset and renewed climb is something staff can act on.
What this does not claim
The study is deliberately narrow. It does not claim deterministic injury prediction for any individual player, it is not a full technical validation pack, and it does not replace medical, performance or coaching judgement.
Its value is practical, and it is about order rather than certainty: helping staff decide where to look first.
Players are anonymised throughout.