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 profile

Publishing 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.