Learn
Teach football analytics concepts, recommend resources, provide a learning path, and answer questions about data provider documentation — all adapted to the user's level.
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.
Glossary of core concepts
Chance quality metrics
| Metric | What it means | Intuition |
|---|---|---|
| xG (Expected Goals) | Probability a shot results in a goal (0-1) | "How good was the chance?" |
| xGOT (xG on Target) | xG adjusted for shot placement in the goal | "How good was the finish?" |
| xA (Expected Assists) | xG of the shot that resulted from a pass | "How good was the chance created?" |
| xT (Expected Threat) | Value added by moving the ball to a more dangerous area | "How much did this pass/carry increase goal threat?" |
| PSxG (Post-Shot xG) | Same as xGOT. StatsBomb terminology. |
Possession and pressing
| Metric | What it means |
|---|---|
| PPDA | Passes allowed per defensive action. Lower = more pressing |
| High press | Defensive actions in the opponent's defensive third |
| Counterpressure | Immediate defensive reaction after losing the ball |
| Build-up | How a team progresses the ball from defence to attack |
| Possession value | How much each action contributes to scoring probability |
Passing
| Metric | What it means |
|---|---|
| Progressive pass | Pass that moves the ball significantly toward the opponent's goal |
| Key pass | Pass directly leading to a shot |
| Assist | Pass directly leading to a goal |
| Through ball | Pass played into space behind the defence |
| Switch of play | Long pass crossing the centre of the pitch |
| Pass completion % | Successful passes / total passes (misleading in isolation) |
Shooting
| Metric | What it means |
|---|---|
| Shots per 90 | Shot volume normalised by playing time |
| Conversion rate | Goals / shots (noisy, small sample issues) |
| Big chance | High-xG opportunity (typically xG > 0.3) |
| Shot on target % | Shots on target / total shots |
Defensive
| Metric | What it means |
|---|---|
| Tackles won | Successful tackle attempts |
| Interceptions | Reading and intercepting opponent passes |
| Clearances | Defensive clearances (often under pressure) |
| Blocks | Blocking shots or passes |
| Aerial duels won | Headers contested and won |
Per-90 normalisation
Always normalise player stats per 90 minutes, not per match:
per_90 = (raw_stat / minutes_played) * 90Why: a player with 2 goals in 180 minutes (per 90: 1.0) is performing the same as one with 1 goal in 90 minutes. Per-match stats penalise part-time players.
Minimum sample: ~900 minutes (10 full matches) before per-90 stats are meaningful.
Learning path
Stage 1: Getting started
- Read: "The Numbers Game" by Chris Anderson and David Sally. Accessible introduction to football analytics.
- Watch: Tifo Football YouTube channel for visual explainers of tactical and analytical concepts.
- Do: Load StatsBomb open data and make a shot map. Just plot the x,y coordinates of shots, colour by goal/no goal.
- Understand: What xG is and isn't. Read StatsBomb's public xG methodology.
Stage 2: Building skills
- Read: "Soccermatics" by David Sumpter. Mathematical modelling applied to football.
- Learn: How to make pass networks and xG timelines.
- Practice: Analyse a full match. Write up what happened and what the data shows.
- Explore: FBref for season-level stats. Compare teams across multiple dimensions.
- Tool up: Learn pandas/polars (Python), tidyverse (R), or D3.js (JavaScript) for data manipulation and visualisation.
Stage 3: Going deeper
- Read key papers:
- Decroos et al. (2019) "Actions Speak Louder than Goals" (VAEP model) - Fernandez & Bornn (2018) "Wide Open Spaces" (pitch control) - Karun Singh (2018) "Introducing Expected Threat" (xT) - Spearman (2018) "Beyond Expected Goals" (pitch control + off-ball)
- Build a model: Train your own xG model. Compare with provider xG.
- Tracking data: If you can access it, explore player positioning data.
- Community: Join football analytics Twitter/X, attend OptaPro Forum or StatsBomb Conference talks (many are free online).
Stage 4: Professional level
- Statistical rigour: Learn about confidence intervals, effect sizes, Bayesian methods.
- Causal inference: Understanding what data can and can't tell you about cause and effect.
- Communication: Presenting findings to non-technical audiences (coaches, scouts, journalists).
- Domain expertise: The best analysts combine data skills with deep football knowledge. Watch matches, understand tactics.
Community resources
| Resource | What it is |
|---|---|
| StatsBomb open data | Free event data, best starting point |
| Friends of Tracking (YouTube) | University-level video lectures on football analytics |
| McKay Johns (YouTube) | Python football analytics tutorials |
| FBref | Free season stats, powered by StatsBomb data |
| The Athletic | Journalism with analytics focus |
| OptaPro Forum | Annual analytics conference (talks online) |
| StatsBomb Conference | Annual conference with published research |
| r/socceranalytics | Reddit community |
| Football Analytics Slack | Community workspace |
Common misconceptions
- "More possession = better." Possession without purpose is meaningless. Quality of chances matters more.
- "xG is a prediction." xG is a description of chance quality, not a prediction of future performance.
- "This player has 0.8 xG per 90, so they'll score 30 goals." Small samples, regression to the mean, context all matter.
- "Data analytics replaces scouting." It complements it. Data finds candidates; humans evaluate fit, personality, potential.
- "All xG models are the same." They vary significantly by input features, training data, and methodology.
Provider documentation
When the user asks about provider-specific details — event types, qualifier IDs, coordinate systems, API schemas, field mappings — use the football-docs MCP tools.
Answering specific questions
Use search_docs with the user's query. Add a provider filter if they're asking about a specific provider.
Examples:
- "What qualifier ID is a headed goal in Opta?" →
search_docs(query="headed goal qualifier", provider="opta") - "How does StatsBomb represent xG?" →
search_docs(query="xG expected goals", provider="statsbomb") - "What free data sources have shot-level data?" →
search_docs(query="shot data free", provider="free-sources")
Comparing providers
Use compare_providers when the user wants to understand differences.
Examples:
- "How do Opta and StatsBomb represent passes differently?" →
compare_providers(topic="pass event types", providers=["opta", "statsbomb"]) - "Which providers have xG data?" →
compare_providers(topic="xG expected goals")
Discovering what's available
Use list_providers to show what documentation is indexed and its coverage.
Cross-referencing with kloppy
When comparing providers, also search for kloppy's mapping documentation. kloppy defines how each provider's events map to a canonical model, which helps the user understand what maps cleanly between providers, what information is lost in translation, and what becomes a GenericEvent (unmapped).
Response format
- Give the direct answer first (the qualifier ID, the field name, etc.)
- Add context about how it works in practice
- If relevant, mention how other providers handle the same concept
- Adapt technical depth to the user's experience level