Store
Help the user choose storage formats, locations, and publishing methods for their football data.
Accuracy
Read and follow docs/accuracy-guardrail.md before answering any question about provider-specific facts (IDs, endpoints, schemas, coordinates, rate limits). Always use search_docs — never guess from training data.
First: check profile
Read .nutmeg.user.md. If it doesn't exist, tell the user to run /nutmeg first.
Storage format decision tree
Small projects (< 100MB, single user)
| Format | Best for | Tools |
|---|---|---|
| JSON | Raw event data, API responses | Any language |
| CSV | Tabular stats, easy to share | Spreadsheets, pandas, R |
| Parquet | Columnar analytics, fast queries | polars, pandas, DuckDB, Arrow |
| SQLite | Relational queries, multiple tables | Any language, DB browser tools |
Recommendation: Start with JSON for raw data, Parquet for processed data.
Medium projects (100MB - 10GB)
| Format | Best for | Notes |
|---|---|---|
| Parquet files | Analytics workloads | 5-10x smaller than JSON, fast columnar reads |
| DuckDB | SQL analytics on local files | Queries Parquet/CSV directly, no server needed |
| SQLite | Relational data with joins | Single file, portable, ACID compliant |
Recommendation: Parquet for storage, DuckDB for querying.
Large projects (> 10GB, multiple users)
| Solution | Best for | Cost |
|---|---|---|
| PostgreSQL | Production apps, complex queries | Free (self-hosted) or ~$7/mo (Railway, Supabase) |
| BigQuery | Massive analytical queries | Free tier: 1TB/mo queries |
| Cloudflare R2 | Object storage (raw files) | Free tier: 10GB storage |
| S3 / GCS | Object storage at scale | ~$0.023/GB/mo |
Directory structure
Recommend this structure for football data projects:
project/
data/
raw/ # Untouched API/scrape responses
statsbomb/
events/
matches.json
fbref/
2024/
processed/ # Cleaned, transformed data
events.parquet
shots.parquet
passes.parquet
derived/ # Computed metrics
xg_model.parquet
passing_networks/
notebooks/ # Analysis notebooks
scripts/ # Data pipeline scripts
outputs/ # Charts, reports, exports
.env # API keys (gitignored)
.nutmeg.user.md # Nutmeg profilePublishing and sharing
Interactive dashboards
| Platform | Language | Cost | Notes |
|---|---|---|---|
| Streamlit | Python | Free (community cloud) | Most popular for football analytics. Deploy from GitHub |
| Observable | JavaScript | Free tier | Great for D3.js visualisations. Notebooks + Framework |
| Shiny | R | Free (shinyapps.io, 25 hrs/mo) | R ecosystem integration |
| Gradio | Python | Free (HuggingFace Spaces) | Quick ML model demos |
Static sites
| Platform | Notes |
|---|---|
| GitHub Pages | Free. Good for static charts (D3, matplotlib exports) |
| Cloudflare Pages | Free. Faster, more features than GH Pages |
| Vercel | Free tier. Good for Next.js/Astro sites |
Sharing data
| Method | Best for |
|---|---|
| GitHub repo | Small datasets (< 100MB), code + data together |
| GitHub Releases | Larger files (up to 2GB per release) |
| Kaggle Datasets | Community sharing, discoverable, free |
| HuggingFace Datasets | ML-focused, versioned, free |
Social media / content
| Output | Tool | Notes |
|---|---|---|
| Static charts | matplotlib, ggplot2, D3.js | Export as PNG/SVG |
| Animated charts | matplotlib.animation, D3 transitions | Export as GIF/MP4 |
| Twitter/X threads | Chart images + alt text | Accessibility matters |
| Blog posts | Markdown + embedded charts | GitHub Pages, Medium, Substack |
Cost awareness
Based on the user's .nutmeg.user.md goals, flag costs:
- Exploration/learning: Everything can be free. StatsBomb open data + Jupyter/Colab + GitHub Pages.
- Content creation: Streamlit Community Cloud is free. Cloudflare Pages is free.
- Professional: Budget for API access ($100-1000+/mo for Opta/StatsBomb commercial).
- Product: Database hosting ($7-50/mo), consider data licensing costs separately.