Data Lab — Football Analytics Explained

Explainers

Core concepts

What is xG? Expected Goals Explained

Expected Goals (xG) measures the quality of a chance by calculating the probability that a shot will result in a goal. Based on historical data from tens of thousands of shots, accounting for distance, angle, body part, assist type, and defensive pressure.

The average Premier League shot has an xG of approximately 0.11 (11% chance of scoring).

How is Possession Measured?

Possession is calculated as the percentage of total passes completed by each team. It does not measure time on the ball. A team with 70% possession has completed 70% of the match's total passes.

The average PL match sees around 900 total passes.

Understanding Shot Maps and Heat Maps

Shot maps plot every shot attempt on a pitch diagram, colour-coded by outcome (goal, saved, blocked, off-target). Heat maps show where a player or team spends the most time. Both are spatial data tools that reveal patterns invisible in raw statistics.

Central shots within the penalty area convert at 3x the rate of shots from outside the box.

What is xA? Expected Assists Explained

Expected Assists (xA) credits the passer for the quality of the chance they created, regardless of whether the shot was converted. A through-ball to a 1-on-1 generates high xA even if the striker misses.

Top creative midfielders typically record 8-12 xA per season.

PPDA: Pressing Intensity Measured

Passes Per Defensive Action (PPDA) measures how many passes a team allows before making a defensive intervention. Lower PPDA = more intense pressing. It captures the defensive side of possession football.

Liverpool's 2019-20 PPDA of 7.4 was among the lowest in PL history.

The Form Guide: Why Five Matches is the Standard

Form is typically measured over the last five matches. Why five? It balances recency with sample size. Fewer matches are too volatile; more dilute recent trends. The WWDLW dots on Varqyst visualise this five-match window.

15 points from 5 matches is a perfect form record — it happens roughly 3 times per season across all 20 clubs.

Quick reference

Metrics at a glance

Metric	Measures	Good value (top team)	Used for
xG	Shot quality	65-75 per season	Attacking output
xGA	Chances conceded	25-30 per season	Defensive solidity
xA	Chance creation quality	8-12 per player	Creative output
Possession %	Pass share	58-65%	Ball dominance
PPDA	Pressing intensity	7-9 (lower = more intense)	Defensive style
PSxG	Shot-stopping quality	+5 to +8 vs xG	Goalkeeper evaluation
Form (5-match)	Recent results	12-15 pts from 5	Momentum

Why football data matters

Football analytics has evolved from a niche statistical pursuit into an essential part of how the modern game is understood. From the early days of Moneyball-inspired approaches to the sophisticated expected-goals models used by every top-flight club today, data has transformed how we watch, analyse, and discuss football.

Understanding metrics like xG, possession quality, and pressing intensity does not require a statistics degree. The concepts are intuitive once explained — and that is what the Data Lab is for.

A brief history

2012 — Opta begins publishing xG data commercially. The metric enters mainstream football discourse.

2015 — Leicester City's title win sparks a data debate: their xG said regression, reality said otherwise.

2018 — Expected Assists (xA) and Passes Per Defensive Action (PPDA) gain adoption at club level.

2022 — Post-shot xG (PSxG) revolutionises goalkeeper evaluation and transfer decisions.

Today — Every PL club employs data analysts. The question is no longer whether data matters, but how to make it accessible to fans.

"Possession is measured in passes, not minutes."

Understand the numbers