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