Courtside Analytics

A live NBA decision dashboard for front-office questions: who is outperforming their contract, how rest changes results, and how teams are trending.

Built as an end-to-end analytics project: Python ingestion, Supabase Postgres warehouse, dbt models and tests, GitHub Actions scheduling, and a Next.js dashboard on Vercel.

Unofficial project. Not affiliated with or endorsed by the NBA.

Stack

Layer	Tool
Ingestion	Python (nba_api, NBA live feed)
Warehouse	Supabase Postgres (raw > staging > marts)
Transform and test	dbt Core (dbt-postgres)
Orchestration	GitHub Actions
Dashboard	Next.js 15, shadcn/ui charts, deployed on Vercel

Repo layout

ingestion/   Python loaders into the raw schema
dbt/         dbt project (staging, intermediate, marts, tests)
web/         Next.js dashboard (see web/README.md)
sql/         One-time warehouse setup (schemas, log tables)
docs/        Architecture, page specs, data dictionary
.github/     CI and scheduled workflows

Setup

1. Create a Supabase project, then run sql/001_schemas.sql and sql/003_raw_tables.sql in the SQL editor.
2. cp .env.example .env and fill in the connection details.
3. python -m venv .venv && source .venv/bin/activate
4. pip install -r requirements.txt
5. make check-db to confirm the connection works.
6. make load-reference, then python -m ingestion.load --season 2025 as a first test.
7. make backfill to load every season (run once).

Status

• Phase 0: repo and warehouse setup
• Phase 1: ingestion and history backfill (11 seasons loaded)
• Phase 2: dbt staging and core models (project scaffolded, models in progress)
• Phase 3: intermediate models, macros, incremental loads
• Phase 4: analysis marts
• Phase 5: scheduled refresh
• Phase 6: dashboard