AFL Analytics & BI Dashboard
SQL and BI workflow analyzing sports data, with data modeling, KPIs, and interactive dashboards.
- Data Analytics
- BI / Dashboarding
TL;DR
An analytics-engineering project that models raw sports data, computes KPIs with SQL window functions, and presents insights through Power BI / Tableau dashboards built for storytelling. (Placeholder narrative.)
Problem
Raw match data was messy and hard to query for insight. Stakeholders needed clear KPIs and dashboards that tell a story, not just tables.
Note: Placeholder case-study content — replace with your real write-up.
Why it matters
Decision-makers don’t want raw tables — they want answers. This project turns unstructured match data into a clean model and a dashboard that surfaces the right KPIs quickly.
Dataset / inputs
Match-level sports data. (Placeholder — describe source, schema, and time range. Confirm the data is publicly usable.)
Technical decisions
I modeled the data dimensionally first so that every metric had one clear definition, then used SQL window functions for rolling form and ranked metrics. The dashboard was designed for narrative flow: overview → drill-down → detail.
Challenges
The hardest part was defining KPIs unambiguously so the dashboard couldn’t be misread. Documenting each metric’s formula was as important as building it.
Methods
- Dimensional data modeling (fact/dimension design)
- SQL window functions for rolling and ranked metrics
- Power BI / Tableau dashboards with drill-down
- Alteryx for repeatable data prep (placeholder)
Results
- Reusable data model and documented KPI definitions (placeholder)
- Interactive dashboard with filters and drill-down (placeholder)
Lessons learned
- Clear KPI definitions prevent dashboards from being misread
- Modeling upstream makes every downstream query simpler
Limitations
- Single data source; no automated refresh pipeline yet
Next steps
- Automate ingestion + scheduled refresh
- Add statistical context (confidence bands) to trend charts