About EVOLVE
Arnaud Depraetere
Sports Doctor
Sports medicine leader shaping methodology, player care, and return‑to‑play strategy.
Tim Pieters
Data & Integrations Lead
Data architecture, third‑party integrations, and applied machine learning for predictive insights.
Arne De Doncker
Engineering & Security
Platform engineering, reliability, and security best practices across the stack.
Birth of EVOLVE
EVOLVE grew out of real work inside professional football.
After spending several years working in football as a sports doctor, our man in the field kept seeing chaotic files, scattered data, unstructured communication, and systems that simply weren’t good enough.
We assumed that elite clubs would operate on a unified and holistic platform. But the reality was the opposite. That’s when we realized the industry needed something new, a platform built from the inside by people who truly understand football’s reality.
When our Sports Doctor met two co-founders, one leading data & integrations and the other engineering & security, we suddenly had the exact combination needed to solve the missing puzzle in professional football.
This was the birth of EVOLVE.
A platform designed to finally bring clarity, structure, and integration to the world of football performance and medical care.
Our Mission
At EVOLVE, we believe football doesn’t just need more data.
It needs connected data: clear, unified, and actionable.
Less noise. More insight. Better decisions.
Why EVOLVE
We give football organizations a single source of truth: performance, medical, wellness, and readiness connected in one secure platform.
In a market filled with rigid performance platforms, EVOLVE stands apart.
We are built with a simple philosophy: football staff deserve tools that adapt to them, not the other way around.
Roadmap
November 2024 — Project Kickoff
Initial research with clubs and practitioners; integration groundwork for Catapult/WHOOP.
November 2025 — MVP
Unified dashboards, questionnaires, and core workflows for early partner clubs.
Predictive Modelling
Applying machine learning to anticipate risk and support decisions.
Continuous Improvement
Ongoing refinements with practitioner feedback and measurable outcomes.