Modern football analysis uses statistics that go far beyond goals and possession. This guide explains every key metric — xG, xGA, PPDA, xA, progressive passes — in plain English, with worked examples. By the end, you'll know which stats to trust, which to ignore, and how to apply them to predictions and fantasy decisions.
In traditional football analysis, we judged teams by the scoreline. But goals are highly random. A team can play brilliantly, create 3.0 expected goals worth of chances, and lose 1–0 to a counter-attack. Without statistics, we might wrongly conclude the better team won.
Modern football statistics measure the process, not just the outcome. They answer questions like: How dangerous were the chances created? Is this team pressing effectively? Is that striker getting into good positions even if they haven't scored?
The core insight
Outcomes (goals, results) are heavily influenced by luck over short periods. Quality metrics (xG, xGA, PPDA) stabilise much faster and are far more predictive of future performance. Analyse the process — the results will follow.
Start here. These are the metrics that professional analysts and sharp bettors use every week.
Expected Goals
What it is
A probability score (0–1) assigned to each shot based on historical data: shot location, assist type, angle, and whether it was a header.
Why it matters
Goals are random in the short term. xG cuts through luck and shows whether a team is genuinely creating danger.
Example
A team wins 2–0 with xG of 0.8 vs 2.4 against. Their opponent was the better side — and future results will likely reflect that.
Expected Goals Against
What it is
The xG sum of all chances a team conceded. Measures defensive quality by evaluating the quality of shots the defence allows, not just whether they go in.
Why it matters
A goalkeeper can save a high-xG shot, but that luck won't last. Low xGA indicates a genuinely well-organised defence.
Example
Chelsea xGA 0.6 per game: they're allowing low-quality chances. If their keeper was having a bad run, goals will likely correct downward.
Expected Assists
What it is
The xG value of a shot that directly resulted from a pass. Measures how well a player creates goalscoring opportunities, not just whether the shots are converted.
Why it matters
A player can have 0 assists but a 0.8 xA — meaning they're creating excellent chances that teammates are squandering. Future assists are likely.
Example
Bruno Fernandes xA 0.18 per game vs 0.10 assists per game: his creation is ahead of his assist return and likely to correct upward.
Passes Per Defensive Action
What it is
The number of opponent passes allowed per defensive action (tackle, interception, foul) in the defensive half. Lower = more intense pressing.
Why it matters
Pressing style affects everything: match tempo, transition speed, fatigue, and counter-attack vulnerability. PPDA makes pressing measurable.
Example
Man City PPDA 7.2 vs Burnley PPDA 16.4: City press relentlessly while Burnley sit in a low block.
Progressive Passes
What it is
Passes that move the ball at least 10 yards closer to the opponent's goal, or crosses into the penalty area. Measures how effectively a team moves forward.
Why it matters
A team that completes many progressive passes is consistently building dangerous attacks, not just keeping possession in harmless areas.
Example
Arsenal average 92 progressive passes per game vs 64 for Sheffield United — Arsenal's attacking build-up is far more penetrating.
Shots on Target
What it is
Shots that require a save or result in a goal. Excludes shots that miss the target or are blocked before reaching the goalkeeper.
Why it matters
A crude but accessible measure. More reliable than total shots, but xG is far superior — a shot on target from 40 yards is far less dangerous than one from 6 yards.
Example
A team with 8 shots on target but xG 0.5 is shooting from long range. Don't be misled by shot counts alone.
One of the most important distinctions in football analytics is the difference between counting stats (how many times something happened) and quality stats (how good those events were).
Rule of thumb: If a statistic could be artificially inflated without the team getting better (e.g. shooting from 40 yards to inflate shot count, keeping safe sideways passes to inflate possession), treat it with scepticism. Good stats measure outcomes that are hard to game.
Use the right stats for the right question.
Pro tip: Focus on xG per game (not total) when comparing teams with different numbers of matches played.
Pro tip: xGA is the gold standard defensive metric. A team with low xGA but goals conceded is likely getting unlucky and will improve.
Pro tip: Possession % alone tells you very little. A team with 70% possession in their own half is actually under pressure.
Pro tip: Always use per-90 stats, not totals, when comparing players — a player with 5 appearances will always trail a player with 30.
When you look at a league stats table, follow this sequence to extract the most useful signal:
Check games played
Always confirm sample size first. Stats after 3 games are noisy. After 10+ games, patterns start to stabilise. After 20+, you can start drawing reliable conclusions.
Use per-game or per-90 figures, not totals
Teams with more games will always have higher totals. Use xG per game, xGA per game, and player stats per 90 minutes for fair comparisons.
Compare xG to actual goals
A team scoring significantly more goals than their xG is likely overperforming and due to regress. A team scoring fewer is likely underperforming and due to improve.
Look at both sides: attacking and defensive stats
xG alone tells half the story. Combine it with xGA to understand the full picture. A team with high xG and low xGA is genuinely strong; high xG and high xGA might be an open-game team.
Split home and away
Home advantage is real and measurable. Some teams have dramatically different home/away profiles. Never average them when making match-specific decisions.
Trusting goals over xG
xG shows what should have happened. After 5+ games, xG is more predictive than actual goals scored.
Using total stats instead of per-90
A player with 20 games will have higher totals than one with 8 games. Per-90 levels the playing field.
Ignoring sample size
Football stats stabilise slowly. After 5 games, xG is useful; after 1 game, it's noise.
Treating possession % as dominance
Where possession happens matters. High possession in your own defensive third means you're under pressure.
Ignoring context: home vs away, opponent quality
Always split home/away stats and adjust for opponent strength. A 1.5 xG against Brentford differs from 1.5 xG against Man City.
The most powerful betting signal is when xG and actual goals diverge significantly. A team with xG 2.1 per game and actual goals of 0.9 per game is likely underperforming and due to correct. When the market hasn't adjusted to this divergence, you have potential value.
Combine xGA with PPDA to identify defensive fragility. A team with high xGA and low PPDA (poor press, conceding quality chances) is likely to continue leaking goals. Look for BTTS or Over 2.5 opportunities.
Read: How to use xG to find betting value →xG per 90 and xA per 90 identify players who are creating more than their returns suggest. A midfielder with 0.18 xA per 90 but 0 assists is likely to deliver returns soon — and at low ownership, they're a differential pick.
For defensive returns, use xGA (lower = better clean sheet probability) and check the FDR for upcoming fixtures. Combine both to identify the best defensive assets.
Read: How to build FPL strategy with data →Put these stats into practice with KiqIQ's free tools.
Poisson Calculator
Input team xG figures to calculate win/draw/loss probabilities and compare with bookmaker odds.
BTTS Calculator
Calculate Both Teams to Score probability from xG data and recent form statistics.
Over/Under Calculator
Estimate Over 2.5 and Over 3.5 goals probability using team xG averages.
Football Glossary
130+ football statistics terms explained — from xG to gegenpressing, all in plain English.
xG Full Guide
Everything you need to know about expected goals: calculation, limitations, and applications.
Live Fixtures
Browse upcoming matches and analyse statistical profiles across 8+ major leagues.
What is the most important football statistic?
Expected goals (xG) is the single most useful football statistic for evaluating performance. It measures the quality of scoring chances rather than just the number of goals scored, giving a much more accurate picture of whether a result reflects the underlying play.
What is the difference between xG and xGA?
xG (expected goals) measures the quality of a team's attacking chances — how many goals they should have scored. xGA (expected goals against) measures the quality of chances conceded — how many goals they should have let in. Together they give a full attacking and defensive picture.
What does PPDA mean in football?
PPDA stands for Passes Per Defensive Action. It measures pressing intensity: a low PPDA (e.g. 6–8) means the team is pressing aggressively, while a high PPDA (e.g. 12+) means they're sitting deeper and allowing more passes before intervening.
Where can I find football statistics for free?
KiqIQ provides free football statistics tools including a Poisson calculator, xG-based match analysis, and a glossary of 130+ football statistics terms. Other sources include FBref for historical data and Understat for xG data.
xG vs Actual Goals
How to spot teams over or underperforming their xG — and what to do about it.
PPDA Explained
The definitive guide to pressing statistics in football.
What is xA?
Expected assists — how to identify undervalued creative players.
Poisson Distribution
The mathematical model behind match predictions — with worked examples.
Analyse a Match
A step-by-step framework for pre-match statistical analysis.
Analyse a Team
The 5-metric team analysis framework used by sharp bettors.
Browse all KiqIQ guides and free tools — from Poisson calculators to xG-based match analysis.
