Football analytics – Queens University Belfast
As part of a project exploring the use of advanced statistics & modelling in high-performance football, Queen’s University approached us to map the sports analytics & data landscape, capturing the real experiences of practitioners in the field to identify & define a series of high-impact problems that could be solved through their novel methodologies. Guided by the Lean Startup approach, we combined a review of the competitive landscape with a series of practitioner interviews and a structured thematic analysis to guide problem definition and map solutions for exploration in future phases of the project.
Analytics in football is a crowded space, with hundreds of companies, analysts, and practitioners delivering insight for clubs & federations. Queen’s University came to us as part of a project exploring the use of novel advanced statistics & modelling developed by their academics in analysis of physical performance.
Phase 1 of this project set out to understand how data & analytics are really used in high-performance football, and how they could work better, so that staff can protect availability, manage fatigue, and make confident training decisions when it matters.
Guided by the Lean Startup approach, we started by defining jobs, pains, and gains for key performance roles, anchoring the work in what matters to practitioners. This was accompanied by a review of the competitive landscape, including providers in performance tracking, video analytics, data intelligence & AMS.
We then conducted a series of practitioner interviews, speaking with people across performance, medical, data, and commercial roles, mapping real ‘jobs to be done’, workflows, pain points, and perceived opportunities.
Finally, we ran a structured thematic analysis of responses to inform a detailed definition of the problems facing practitioners, and map potential solutions that are practical, testable, and immediately useful in the high-performance football environment.
The result is a set of grounded findings, expressed through practitioners’ own language, that reflect a clear picture of the industry and where innovators should focus their efforts. This information continues to guide the Queen’s team through further research and commercial exploration in Phase 2.