Sports data analytics
From sensor data to race-winning decisions
We turn raw, multi-channel sensor streams from high-performance sailing and foiling into clear, decision-ready insight — and into the tools that put that insight in the hands of athletes and engineers.
Confidential: SailGP engagement data is private. Any charts on this page use generalised or synthetic data and illustrate method only — no client data is reproduced.
The challenge
High-performance sailing and foiling generate large volumes of multi-channel telemetry — boat speed, load, attitude, control inputs, environmental conditions. The raw data is rich but unusable as-is: the value is locked in the relationships between channels and in the handful of moments that actually decide a race.
What we did
- Built data pipelines to ingest, clean, synchronise and process raw sensor data into analysis-ready datasets.
- Performed insight extraction — deriving the performance metrics and patterns that matter to athletes and engineers.
- For SailGP, developed an interactive website with a React-based AI agent that answers concrete questions about the discipline by querying a curated knowledge base (a “second brain”).
Approach
1 — Data pipeline
Placeholder: ingestion, cleaning, time-alignment of channels, and the processing steps that produce a trustworthy dataset.
2 — Insight extraction
Placeholder: the derived metrics and the relationships that turned out to drive performance.
3 — Interactive tools & AI agent
Placeholder: how the interactive tool and the question-answering agent let the team self-serve answers instead of waiting on analysis.
Outcome
Placeholder: the decisions this enabled and the difference it made.