How to Read Football Statistics: A Complete Guide

Why Advanced Stats Matter for Scouting

Traditional football statistics — goals, assists, appearances — were designed for newspaper sports desks, not for understanding what is actually happening on a pitch. A striker with 12 goals might be an elite finisher, or they might be a mediocre forward at a dominant team who received 35 high-quality chances. A midfielder with zero assists might be the most creative player in the division — just one whose teammates keep missing the final pass.

Advanced metrics exist to strip away the noise. They attempt to measure what a player is actually contributing, independent of their teammates' quality and their team's tactics. For anyone serious about player evaluation — whether for fantasy football, sports betting, genuine scouting, or simply understanding the game — they are indispensable.

This guide explains every major advanced metric in plain English. Not the mathematical definitions — those are widely available — but what each metric is actually measuring in football terms, what a good number looks like, and what the stat misses.

Expected Goals (xG) Explained

Expected Goals (xG) is the single most important metric in modern football analytics. It assigns a probability value to every shot taken, based on historical data from thousands of similar shots. A penalty is worth roughly 0.76 xG — meaning penalties are scored approximately 76% of the time. A shot from 35 yards with a defender in the way might be worth 0.02 xG.

A player's xG per 90 tells you how many goals the quality of their shots should have produced per 90 minutes. Their actual goals per 90 tells you how many they actually scored. The difference between those two numbers is their xG overperformance (or underperformance).

Consistent xG overperformance over two or more seasons is evidence of elite finishing ability. One season of overperformance might be luck. Two seasons is a signal. Three seasons is a fact.

For attacking players, both xG and xA (Expected Assists — the xG value of the chances a player creates) matter. Combined, xG + xA per 90 gives a complete picture of an attacker's total threat contribution, independent of whether teammates convert.

Progressive Passes and Carries

A progressive pass is defined as any pass that moves the ball at least 10 yards closer to the opponent's goal (with specific zone adjustments near the penalty area). Aprogressive carry is the same concept applied to dribbling — carrying the ball 10+ yards toward goal.

These metrics matter because they measure a player's ability to actually advance play toward dangerous territory, rather than simply touching the ball. A midfielder who completes 93% of their passes but all of them go sideways is providing far less value than one who completes 78% but constantly drives the team forward.

Progressive passes per 90 is the defining metric for deep-lying playmakers and ball-playing centre-backs. For these roles, you are looking for numbers above 8-10 per 90 in top divisions. Elite playmakers like Kevin De Bruyne or Toni Kroos at their peaks were posting 12+.

Progressive carries per 90 is the defining metric for dribblers and box-to-box midfielders. Wide forwards who are genuinely dangerous in transition post 4-6 progressive carries per 90. Numbers above 7 are exceptional.

PPDA: Pressing Intensity

PPDA stands for Passes Allowed Per Defensive Action. It measures how aggressively a team (or player) presses in the opponent's half. A lower PPDA means a team allows fewer passes before applying a defensive action (tackle, interception, press), which indicates a higher-intensity press.

At team level, PPDA is one of the clearest proxies for tactical style. Jürgen Klopp's Liverpool and Pep Guardiola's Manchester City both operated with very low PPDA — relentless pressing systems. Teams that sit in a mid-block and defend deep tend to have high PPDA numbers.

For individual players, PPDA derivatives — specifically pressures per 90 andsuccessful pressures per 90 — tell you how much work a player does off the ball. High pressing numbers are essential for players in Klopp or Arteta-style systems. Players with low pressing numbers are liabilities in those structures regardless of their technical quality.

Pressure Regains and Defensive Metrics

A pressure regain occurs when a team wins the ball back within 5 seconds of applying a defensive pressure. It is the successful outcome of pressing — and because it happens close to the opponent's goal, it tends to create high-quality chances immediately.

Pressure regain rate (regains as a percentage of pressures applied) separates smart pressers from high-volume but ineffective ones. A player applying 15 pressures per 90 and winning 4 back is more valuable than one applying 20 pressures and winning 3 — the first player is both efficient and effective.

For defenders, additional key metrics include:

• Tackles + Interceptions per 90 — raw defensive action volume
• Aerial duel win percentage — critical for centre-backs in high-ball systems
• Errors leading to shots — negative metric; high numbers are a red flag
• Ball recoveries per 90 — how often a player wins loose balls; a proxy for positioning quality
• Dribbled past per 90 — how often an opponent beats them 1v1; lower is better

Stats Reference Table

What Stats Miss

Advanced metrics are powerful tools — but they are not complete tools. Understanding their limitations is as important as knowing how to read them.

Leadership and mentality are almost entirely invisible in the data. A player who organises the defensive line, motivates teammates, and raises their game in finals does not show up differently in xG or PPDA. This is why experienced scouts still watch matches — you cannot read a room from a spreadsheet.

Context dependence is another major limitation. A player on a dominant team will naturally see better ball positions and more progressive opportunities than one playing for a relegation side. Raw numbers without context can seriously mislead — always compare within positional and team-quality brackets.

Injury resilience and recovery are not captured in standard metrics. A player who posts elite numbers but only plays 60% of games due to muscle injuries is a very different asset from one with identical stats but 95% availability.

Set piece contribution is partially captured (corners, free kicks create xA opportunities) but the orchestration of set piece routines — the movement patterns, the blocking runs, the decoy runners — is largely invisible.

How to Use Stats with AthleteBrief

AthleteBrief aggregates statistical data from multiple data providers and presents it in a standardised format that allows comparison across leagues, seasons, and age groups. Every player profile shows per-90 metrics (not raw totals) which makes comparisons meaningful even across players with very different game time.

The percentile ranking system shows where a player sits within their positional group across the leagues we track. A centre-back in the 94th percentile for progressive passes is not just good — they are exceptional within their peer group. This framing is more useful than raw numbers for most evaluation purposes.

Combine the statistical profiles with our search trend data to identify players whose statistical emergence is being noticed by the public in real time. Early movers in both dimensions simultaneously are the most reliable leading indicators of a genuine breakout.

FAQ

What is a good xG for a striker?

For a first-choice striker in a top-five league, 0.40+ xG per 90 is solid, 0.55+ is very good, and 0.70+ is elite. Context matters — a striker at a possession-dominant team will typically see better xG opportunities than one at a counter-attacking side.

Is xG better than goals as a metric?

For prediction purposes, yes — particularly over short sample sizes. Goals are volatile over a single season; xG is more stable. Over 3+ seasons the two tend to converge, but xG is the better diagnostic for whether a player's numbers are sustainable.

Do free kicks count toward a player's xG?

Yes, in most data systems free kicks are included in xG calculations. Some analysts separate direct free kick xG from open play xG since free kick conversion rates vary significantly by taker.

Why do some sites show different xG numbers for the same player?

Different data providers use different xG models — the variables they include (shot angle, body part, game state, assist type) differ, which produces different values. There is no single universal xG. AthleteBrief uses a standardised model applied consistently across all tracked leagues for comparison purposes.

What is the most underrated football stat?

Ball recoveries per 90 is consistently undervalued. It measures how often a player wins loose balls and second balls — a proxy for positioning quality, anticipation, and competitive intensity. Players who rank highly here tend to make teams significantly better in ways that don't show up in traditional stats.