Nutmeg Wrangle

Wrangle

Help the user manipulate football data effectively. This skill is about the mechanics of working with data, adapted to the user's language and tools.

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. Use their profile for language preference and stack.

Core operations

Coordinate transforms

Football data coordinates vary by provider. Always verify and convert before combining data.

Use search_docs(query="coordinate system", provider="[provider]") to look up the specific system. Key conversions:

• Opta (0-100) to StatsBomb (120x80): x * 1.2, y * 0.8
• Wyscout to Opta: x stays, y = 100 - y (invert Y)
• Any to kloppy normalised: use kloppy's .transform() in Python

Filtering events

Common filtering patterns for football event data:

By event type:

• Shots: filter for shot/miss/goal/saved event types
• Passes in final third: filter passes where x > 66.7 (Opta coords)
• Defensive actions: tackles + interceptions + ball recoveries

By match state:

• Open play only: exclude set pieces (corners, free kicks, throw-ins, penalties)
• First half vs second half: use periodId or timestamp
• Score state: track running score to filter "when winning", "when losing"

By zone:

• Penalty area actions: x > 83, 21 < y < 79 (Opta coords)
• High press: actions in opponent's defensive third (x > 66.7)

Joining datasets

Common joins in football data:

Fuzzy team name matching is a constant pain. Build a mapping table:

TEAM_MAP = {
    'Man City': 'Manchester City',
    'Man United': 'Manchester United',
    'Spurs': 'Tottenham Hotspur',
    'Wolves': 'Wolverhampton Wanderers',
    # ...
}

Reshaping

Common reshaping operations:

• Wide to long: Season stats tables (one column per stat) to tidy format (one row per stat per team)
• Events to possession chains: Group consecutive events by the same team into possession sequences
• Match-level to season aggregates: Group by team, sum/average per-match values
• Player-match to player-season: Aggregate across matches, weight by minutes played

Handling large datasets

Full event data for a PL season is ~500MB+ (380 matches x ~1700 events). Strategies:

Python:

• Use polars instead of pandas for 5-10x speed improvement
• Process match-by-match in a loop, don't load all into memory
• Use DuckDB for SQL queries on Parquet files without loading into memory

JavaScript/TypeScript:

• Stream JSON files with readline or JSONStream
• Use SQLite (better-sqlite3) for local queries
• Process files in parallel with worker threads

R:

• Use data.table instead of tidyverse for large datasets
• Arrow/Parquet for out-of-memory processing

Data quality checks

Always validate after wrangling:

Format conversion