Athlete readiness intelligence layer

Outcome metric

12 hours saved per reporting cycle

implementation artifact

measured system

IMPLEMENTATION STORY

What changed, what shipped, and why it mattered.

The challenge

Performance, medical, and coaching signals lived in separate tools, forcing analysts to spend hours compiling reports before staff could act on readiness risk.

What we built

We implemented data ingestion, quality checks, athlete-level feature generation, and a staff-facing interface that translated signals into readiness flags and coaching context.

The result

Reporting cycles became repeatable and faster, while coaches gained clearer daily decisions around load, recovery, and intervention priorities.

WHAT WAS ACTUALLY BUILT

Architecture, workflows, and interface surfaces.

Technical diagram: data, models, permissions, workflow ownership.

Interface screenshot: the surface teams use to act on the system.

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