The dataset, served raw
Every 2026 World Cup figure on this site is computed from one bundled match file — and that file is public, at this link. Open it, audit it, reuse it.
Open the JSON →Football, told in numbers
Read the game by its numbers.
Expected goals, pressing intensity, ball progression and projection models — explained in plain language and computed from public data, with every figure traceable to a script you can run yourself.
Nothing here is taken on faith
Audit it yourselfWorld Cup 2026 simulator
Monte-Carlo the real 48-team format in your browser: you set the team strengths, it reports advance and title odds. No scraped feeds, no picks.
Run a tournament →1,494 shots, explorable
Every shot of the 2022 World Cup on an interactive pitch — filter by team, build-up and result, each dot sized by its xG. StatsBomb open data.
Explore the map →A model tested in public
Before our prediction model said a word about 2026, we backtested it on 2022 and published the result — including what it could not do.
Read the backtest →Latest analysis
All deep dives →Matchweek Two: One Heist Refunded, One Doubled Down, and the Ball Keeps Losing
Matchweek two split matchweek one's heists: Ipswich paid theirs back 2-5, Hull stole again, and 10 of the season's 15 winners have had under half the ball.
Data Deep-Dives
Matchweek One Under the Hood: Ten Box Scores, Two Heists, and a Bad Weekend for Possession
Matchweek one's ten box scores, audited: six of nine winners had under 50% possession, shots on target called seven of nine, and United won everything but the match.
Data Deep-Dives
The Table Lies Until October: Last Season's Premier League, Replayed From Matchweek One
All 380 matches of 2025-26, replayed: the table agreed with May at just 0.43 after one game, crossed 0.75 only around game ten, and opening day still paid 14 points.
World Cup 2026
The 2026 Dark-Horse Audit: Grading Our June Checklist
Six teams beat their group seed at the 2026 World Cup. Scored against our pre-tournament dark-horse checklist: the keeper box failed, the kind draw barely helped.
Data Deep-Dives
The 2026-27 Premier League, By the Numbers
The 2026-27 Premier League tracked from completed matches only: goals per game, draws, home advantage and the shots-on-target rule. No predictions — just data.
Data Deep-Dives
The Shots-on-Target Rule
In decided 2026 World Cup games, the team matching or beating its opponent on shots on target won 70 of 80 — 24 of 28 knockouts. The ledger, and the next test.
Data Deep-Dives
Headers, Measured: The Geography of the World Cup's Aerial Shot
Headers were 18% of 2022 World Cup goals, taken from half a foot shot's distance — and open-play headers converted three times better than set-piece ones.
Explore by category
Six beats, one obsession
01
World Cup 2026
The first 48-team World Cup, read through data.
72 articles → 02Stat Explainers
What the numbers actually measure, in plain language.
22 articles → 03Data Deep-Dives
Long-form analysis, built from the raw event data.
31 articles → 04Tutorials
Pull the data and draw the charts yourself.
8 articles → 05Player & Team Analysis
Profiles and scouting, by the numbers.
6 articles → 06Projections & Methodology
How forecasts are built — and why they disagree.
4 articles → 07Football History
The game's past, re-examined through data.
4 articles →New here? Start with these
How the site works →
Stat Explainers
Expected Goals (xG) Explained
The one metric that reshaped how football is watched — what it measures, where it comes from, and where it breaks.
World Cup 2026
We Built a Prediction Model — Then Backtested It on 2022
A transparent Poisson model, backtested on a tournament we already know before it was allowed near the one we don't.
Data Deep Dives
Where Shots Are Taken vs. Where Goals Are Scored From
Every 2022 World Cup shot mapped against where the goals actually came from: attempts spray everywhere, goals collapse into a tight central band.
Tutorials
Every Free Public Soccer Data Source, Ranked
The honest map of where free football data actually lives — what each source covers, and what it doesn't.
Football History
Leicester 2015-16 Revisited
The 5,000-to-1 season, re-read through the underlying numbers — how much was skill, and how much was a miracle.
Player & Team Analysis
Football Aging Curves
When players actually peak, what fades first, and why sprinting dies before passing does.
Numbers, with the working shown
SoccerAnalytics.net is an independent publication about the measurable side of football. The aim is simple: take the metrics that now shape how clubs recruit, coaches plan and broadcasters talk — expected goals, expected threat, PPDA, progressive actions, post-shot xG — and explain what they actually measure, where they come from, and where they break.
Nothing here is hand-waved. Every chart and table is built from public data — primarily StatsBomb open data, supplemented by Understat and FBref — and the Python script that produced it ships alongside the article so you can re-run the numbers or learn from the code. When a figure can't be sourced from a real data pull, it doesn't get published.
If you want the metrics explained honestly, the tutorials to compute them yourself, and the occasional argument about what the data does and doesn't prove, you're in the right place. Start with the stat explainers, or learn to pull the data yourself.