My first pre-match analysis
Here's my first pre-match analysis on Netherlands vs. Morocco for today. Any thoughts ? Or ideas ?
u/Reda_HDr — 6 days ago
r/FootballDataAnalysis
Trying to build a football equivalent of baseball's WAR and struggling to find data sources.
Hey everyone. Background on me: I'm a sports economics professor currently doing a master's in statistics. Football is my main sport, but I've always had a soft spot for baseball and I've always foun the idea of bringing some of baseball's analytical frameworks into football very interesting.
The thing that fascinates me most about baseball analytics is how they've managed to quantify individual player value in a rigorous, reproducible way. Specifically, I'm talking about WAR (Wins Above Replacement).
For those unfamiliar: WAR is a single number that tries to capture how many wins a player contributes to their team compared to a freely available "replacement-level" player. What makes it robust is that it's built from several independent components: batting runs, baserunning, fielding, positional adjustment, and a replacement level baseline, all converted into a common currency of wins. The key insight is that you can decompose a player's total value into distinct, interpretable dimensions.
I want to build something analogous for football. My current thinking is to structure a player's value around three components: an offensive contribution (goal involvement, shot quality, finishing efficiency), a defensive contribution (ball recovery, duels, pressing effectiveness), and a creative/construction one (progressive actions, chance creation, build-up involvement). The weight of each component would vary by position. A striker's value would be dominated by the offensive side, a centre-back's by the defensive one, and midfielders would get a more balanced weighting across all three.
Now here's where I'm stuck: data. I've been going down a rabbit hole trying to find a source that gives me granular per-90 player stats. Things like progressive carries, defensive duels won %, pressures, xG, xA, touches in the box along with minutes played, all in one place and ideally exportable.
FBref used to be the obvious answer, but as most of you probably know, they lost their Opta licence in January 2026 and everything beyond basic stats is gone. I've looked at DataMB, ScoutingStats, Understat, WhoScored, and a few Kaggle datasets, and each one covers part of what I need but not all of it. The consistent problem is missing advanced metrics, or no CSV export on the free tier.
I'm not opposed to paying for something reasonable — something in the €10–30/month range that gives clean, exportable player-level data for the top European leagues would be ideal. Just not in the market for a £3,000/year Wyscout licence.
Two things I'd love your input on. First, data sources, paid or free, that you've actually used and trust for this kind of project. Specifically something with minutes played, advanced per-90 metrics, and CSV export for the Premier League or top 5 leagues. Second, your honest opinion on the project itself. Does a positional WAR framework make sense in football given how interdependent everything is? What would you do differently?
Thanks in advance. Happy to share more of the methodology if there's interest.
Also — not a bot, I promise. Sorry if this reads a bit stiff, English isn't my first language.
u/Round_Acanthaceae223 — 7 days ago
June 28 World Cup Matchup Predictions from ProperlyRanked.com
u/Properly_Ranked — 8 days ago
Where World Cup 2026 squads were born vs the nation they represent - built as an interactive map. [OC]
21.6% of players at this World Cup were born in a different country to the one they're representing. There were many articles publicising these interesting stats but did not see any data to display visually, therefore built an interactive map to display the diaspora.
The data is using player birthplace and squad data pulled from API-sports & Football Data Org. You can filter by team and toggle to view between birthplace and national team representation.
France has the most players born there and who are now playing for other nations (95 players, 7.6% of the whole tournament).
Updated: The posted screenshot shows where World Cup 2026 squad players were born, player count and % of all 1,248 squad players in the tournament. Pinned labels highlight selected countries only, explore others and who each player represents (including diaspora) in the interactive map www.matchofthedata.com
u/Open-Hand-5816 — 11 days ago
Which Data Points Best Predict Future Player Development?
When evaluating younger players, what metrics have you found to be the most predictive of future progression?
For example:
- Progressive carries
- Press resistance
- Pass completion under pressure
- Defensive actions
- Physical outputs
Are there any data points you've found particularly useful for identifying players who are likely to outperform expectations over the next few years?
Interested to hear both professional and hobbyist perspectives.
u/AdditionalChapter254 — 12 days ago
I built a Football analytics tool — here's what the pass networks and final-third data tell us about Germany and Spain's recent games
Been working on Flickstat for a while now — a football analytics platform that covers the Premier League, and we've just expanded to the World Cup. Wanted to share some of what the data is showing so far because a couple of things genuinely surprised me.
Germany vs Ivory Coast (2-1)
Look at Germany's pass network. 665 passes, and almost the entire structure is compressed into one half of the pitch. Pavlovic sits at the centre of everything — every outfield player routes through him. The backline barely features in the network at all, which tells you how quickly they're transitioning out of defence.
The final-third entries make it even clearer. 56 central entries, 15 shots, 1.81 xG — nearly all the danger comes through the middle. Left and right channels combined produced 1 shot and 0.02 xG from 128 entries. Germany aren't trying to stretch you. They're trying to suffocate you centrally and they're very good at it.
Ivory Coast for comparison had 8 central entries all game but generated 1.13 xG from them — their one goal came from exactly that zone. They couldn't match Germany's volume but they were ruthlessly efficient in the rare moments they got central access.
Spain vs Saudi Arabia (4-0)
770 passes vs 387. Spain's pass network is dense and well-connected across the entire pitch — Rodri, Cubarsí and Porro are the standout nodes on the player scatter, all well above the match average on passes and key passes. The zone dominance grid tells you why Saudi Arabia had no answer: Spain had 35% attacking third share to Saudi's 17%, and Saudi's brightest zone was their own midfield at 34.3% — they spent the game defending, not attacking.
Two very different styles — Germany compact and central, Spain wide and suffocating — but the outcome is the same. Both controlled matches through structure, not just individual quality.
All the visuals are from Flickstat. Happy to pull up any other match from the tournament if anyone wants a specific breakdown — we have pass networks, zone dominance, final-third entries, player radars and shot maps for every World Cup game.
u/AltruisticActuator58 — 13 days ago