October · Snooker research
Read the position.
What do ball positions add beyond the score? An offline replay and a frozen evaluation make the answer visible.
Explore the October articleData · Quant · AI leadership
I lead data products from research to reality.
I turn quantitative research, computer vision and data platforms into products people can use. Recent work connects snooker footage to frame forecasts, keeps editors in control of AI output and gives research teams data they can reuse.
10+ yearsApplied data and modelling
0 → 1 → runStrategy through operation
PhD mathematicsRigour without theatre
Jordan Moore, PhD
Lead Quant · Head of Technology & AI · CTO
Evidence in motion · October 2026
See the work behind the result.
From a snooker position to an editor’s workflow: research, product design and the systems that let a team deliver.
Product · Editorial AI
Give editors control.
Attimo’s workflow now separates graphics review, clean editing feeds and explicit publication. The earlier public interface shows the foundation.
See the project update
Platforms · Shared data
Build once. Reuse well.
Versioned datasets, known dependencies and clear ownership let research jobs reuse work without losing their evidence trail.
See the platform update
Leadership · Evaluation
Know what earns the next step.
A stronger model, a new data source or a passing validator still needs a useful comparison and an accountable decision.
See the research updatesSeptember–October 2026
The latest chapter.
October’s position model, September’s annotation follow-up and the product work happening alongside them.
October edition · sports intelligence
What is a snooker position worth?
From broadcast footage to table state, frame forecasts and a test of what the layout adds.
September edition · updated
What the snooker model gets wrong
The follow-up: reviewing apparent pink-ball errors led to seven approved label corrections.
October project update · applied AI
Keep the editor in control.
A continuing editorial workflow with source checks, separate review and editing feeds, and deliberate publication.
Latest visual demo · Snooker
A comeback, frame by frame.
Watch the table state and frame-win estimate change through a Robertson–Bingham replay, then explore how the research model was tested.
How I lead
Give the team a system it can run.
I connect research, engineering and product around a clear problem, useful evidence and named ownership. The work should become easier to review, maintain and improve as the team grows.
Start with the decision
Define who acts, what changes and what evidence earns promotion before choosing the model or platform.
Make quality reviewable
Golden sets, failure modes, shadow runs and clear readouts turn “looks promising” into an accountable release decision.
Design for operation
Ownership, telemetry, runbooks and stakeholder language are part of the product—not clean-up work for later.
Current portfolio
Embedded where data meets the decision.
Lead Quant Researcher
Research governance, market and wallet signals, challenger models, monitoring and the path from backtest to live operation.
Head of Technology & AI
Sports-data annotation, QA infrastructure, computer-vision challenges and model-ready datasets.
CTO
AI sports-content architecture across ingestion, retrieval, generation, evaluation and editorial review.
Build something dependable
Need a data product that can earn trust?
I work with teams at the point where technical possibility needs to become a clear operating decision.