I work on football data projects focused on turning match data into useful analysis and insights -- through visualisation, modelling, and clean pipelines. Everything here is a learning project, built seriously.
| Project | What it does |
|---|---|
| AFCON 2023 Final -- Passing Analysis | Individual and team passing maps for the AFCON 2023 Final. Built on StatsBomb event data with mplsoccer and matplotlib. |
| AFCON 2023 Final -- Match Dashboard | End-to-end match analysis dashboard: passing networks, shot maps, xG flow, and team statistics in a single publication-ready figure. Template for any StatsBomb-covered match. |
Match Analysis Passing networks, shot maps, xG flow, heatmaps, pitch control, possession value models
Recruitment and Scouting Player similarity models, clustering, role detection, player profiling, squad analysis tools
Expected Metrics and ML xG, xA, xT, match prediction models, team style classification
Data Engineering ETL pipelines, event data processing, API integration, PostgreSQL and MongoDB workflows, data cleaning and validation
Languages
Data and ML
Football Data
Visualisation
Databases & Platforms
Good football analytics does not add complexity. It removes confusion.
If you work in football analytics, sports tech, recruitment, or performance analysis -- reach out via LinkedIn or follow the work on Medium and Substack.