Set Piece Analysis
Set pieces remain the most structurally predictable moments in football — and consequently the most systematically underexploited. Using spatiotemporal event data and frame-by-frame positional tracking, I model both your attacking delivery sequences and your defensive shape against opposition routines.
This covers corners, free kicks from all zones, throw-ins in high-value areas, and goal kicks — analysed not just for outcome probability but for the movement patterns and blocking structures that generate or concede space.
Goalkeeper Analysis
Goalkeeper analysis is among the most technically demanding areas of football data science. Standard save percentage metrics erase context — shot quality, rebound trajectory, outfield structure. My approach builds on post-save rebound modelling (including Hidden Markov frameworks for sequence likelihood), distribution mapping, and sweeper-keeper positioning relative to press shape.
Whether you are evaluating a current number one, prospecting a replacement, or designing training stimuli, the output is detailed, honest, and positioned against appropriate peer cohorts.
Match Analysis
Match analysis here is not a summary of what happened — it is a structural examination of why it happened and what it reveals about repeatable tendencies. This includes in-possession shape mapping, out-of-possession transition triggers, pressing trigger identification, and the relationship between a team’s defensive block and its exposure to progressive carries or third-man combinations.
Deliverables are prepared for both coaching staff consumption and data-literate audiences seeking the underlying model outputs.
Individual Player Development
Player development analytics translates raw performance data into a structured developmental picture — identifying where a player’s output diverges from expected developmental curves, which micro-skills are limiting their ceiling, and which contextual factors (system fit, press intensity, positional role) are masking or amplifying their underlying quality.
I work with clubs and agents to build longitudinal profiles for players aged 15–28, tracking technical, physical, and decision-making indicators through consistent modelling frameworks rather than snapshot assessments.
Recruitment
My recruitment work is built on proprietary player profiling frameworks that go well beyond typical data portal outputs. Using multi-billion row event datasets across competitions in Europe, South America, and beyond, I construct position-specific target lists grounded in role-functional criteria — what a player needs to do in your system — rather than generic performance totals.
This includes transfer efficiency modelling, contract cycle analysis, and cross-cultural adaptation risk assessment. Every shortlist is documented with full analytical rationale, ready to support internal or board-level decision making.
Data Engineering
Most football clubs and analytics teams spend significant time wrestling with data infrastructure problems that have nothing to do with football: vendor API ingestion, format normalisation, storage design, query performance. I design and build data pipelines that resolve these friction points — clean, documented, and maintainable without specialist overhead.
Whether you are handling StatsBomb, Opta, Wyscout, or internal tracking data, I can architect the layer between raw vendor feeds and the analytical environment your staff actually works in. This includes model deployment pipelines for clubs wanting to operationalise custom outputs within their existing tooling.