learn

Learn

Teach football analytics concepts, recommend resources, and provide a learning path adapted to the user's level.

First: check profile

Read .nutmeg.user.md. If it doesn't exist, tell the user to run /nutmeg:init 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) * 90

Why: 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

1. Read: "The Numbers Game" by Chris Anderson and David Sally. Accessible introduction to football analytics.
2. Watch: Tifo Football YouTube channel for visual explainers of tactical and analytical concepts.
3. Do: Load StatsBomb open data and make a shot map. Just plot the x,y coordinates of shots, colour by goal/no goal.
4. Understand: What xG is and isn't. Read StatsBomb's public xG methodology.

Stage 2: Building skills

1. Read: "Soccermatics" by David Sumpter. Mathematical modelling applied to football.
2. Learn: How to make pass networks and xG timelines.
3. Practice: Analyse a full match. Write up what happened and what the data shows.
4. Explore: FBref for season-level stats. Compare teams across multiple dimensions.
5. Tool up: Learn pandas/polars (Python), tidyverse (R), or D3.js (JavaScript) for data manipulation and visualisation.

Stage 3: Going deeper

1. 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)
2. Build a model: Train your own xG model. Compare with provider xG.
3. Tracking data: If you can access it, explore player positioning data.
4. Community: Join football analytics Twitter/X, attend OptaPro Forum or StatsBomb Conference talks (many are free online).

Stage 4: Professional level

1. Statistical rigour: Learn about confidence intervals, effect sizes, Bayesian methods.
2. Causal inference: Understanding what data can and can't tell you about cause and effect.
3. Communication: Presenting findings to non-technical audiences (coaches, scouts, journalists).
4. 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

1. "More possession = better." Possession without purpose is meaningless. Quality of chances matters more.
2. "xG is a prediction." xG is a description of chance quality, not a prediction of future performance.
3. "This player has 0.8 xG per 90, so they'll score 30 goals." Small samples, regression to the mean, context all matter.
4. "Data analytics replaces scouting." It complements it. Data finds candidates; humans evaluate fit, personality, potential.
5. "All xG models are the same." They vary significantly by input features, training data, and methodology.