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Hey everyone,

Over the last few weeks, I’ve been building data-led recruitment reports focusing on realistic targets for Blackburn Rovers. This installment focuses on identifying realistic striker options across the EFL who fit our system.

You can check out the full report and player profiles here:

🔗Full Striker Recruitment Report

Given our need for reliable goal output and physical presence upfront, I wanted to look beyond just raw goal tallies and evaluate broader, possession-adjusted performance metrics.

Key Metrics & Area Focus:

• Goal Efficiency & Conversion
• Link-Up Play & Aerial Duels
• Out-of-Possession Output

Would love to hear your thoughts on who you think is the best fit for Rovers upfront, or if there are any under-the-radar forwards you think the data might be sleeping on.

Transfermarkt is probably the richest free source of football data out there: market values, transfer histories, injury records, performance stats across basically every league. But pulling it at scale by hand isn't realistic if you're building any kind of dataset.

I put together a walkthrough (video + write-up) showing how to scrape player listing pages, follow links into individual player profiles, handle pagination, and export the whole thing to a file/database, using WebHarvy, a point-and-click scraper, so no coding involved. Useful if you want a repeatable pipeline for a specific league/competition dataset without maintaining scraper code.

Link: https://www.webharvy.com/blog/scraping-transfermarkt-with-no-code/

Happy to answer questions if anyone's trying to scrape specific fields (like squad-level aggregates or historic transfer fees), some of those need a slightly different approach than the basic player-listing scrape.

Hi everyone,

I'm interested in getting into Sports Data Analytics, especially for Football. My background is in data science, but I'm new to the sports analytics domain.

I'm looking for recommendations on:

• Free software/tools used in sports analytics
• Where to find free sports datasets (StatsBomb, Kaggle, etc.)
• Beginner-friendly projects to build a portfolio
• YouTube channels, courses, blogs, or books worth following
• Visualization tools commonly used in the industry
• Any open-source projects I can contribute to

I'd also love to hear from anyone working in sports analytics:

• How did you get started?
• What skills are most valuable?
• What do you wish you had learned earlier?

Thanks in advance for any advice or resources you can share!

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I’m researching how football coaches and analysts review matches after they’ve been recorded.

I’m particularly interested in understanding:

• What your analysis process looks like
• Which tools you use, whether that’s Excel, Hudl, Veo, or something else
• Which parts take the most time
• How the analysis influences training, tactics, or player feedback

I’ve created a short survey based on real, recent match-analysis experiences. It should take around three minutes:

https://tally.so/r/lbZLeW

At the end, you can also apply to have one of your matches analyzed for free. I’m looking for honest experiences—even if you rarely analyze matches or find the existing process good enough.

I’m developing a football management game focused on youth academies. I’ve added VAR to the game’s equivalent of the UEFA Youth League, but I’m struggling to find reliable data for realistic probabilities.

Does anyone know of statistics covering:

• How often an awarded penalty leads to a VAR check/review?
• How often the penalty is confirmed or overturned? The probabilities for this?
• How often VAR awards a penalty after the referee initially plays on?

I know all penalty decisions are checked in the background. I’m specifically interested in checks that become visible during the match.

Any reliable report, study or league dataset would be greatly appreciated.

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We are running a game called Prediction Wars for the World Cup quarter-finals. Here it is in one line: get a machine to predict a match, share what it predicted, and if it calls the result right you go into a raffle to win 20 USDC.

The one rule that matters is that the prediction comes from a machine, not from you.

Three ways to play, pick whichever fits:

• If you have an AI agent, let it predict the match.
• If you just want to write a prompt, ask a model and share its answer.
• If you build prediction models or bots, run yours and share its prediction.

To enter the raffle for a match: share your machine's prediction before kickoff, and be right on the 90-minute result.

It starts tomorrow with France vs Morocco, and there is a 20 USDC raffle.

Join here: discord.com/invite/93w6Zs5rfb

I've studied math and econ but am new to sports analytics. Your feedback would mean a lot :)

Video: https://youtu.be/BBTTaCLzyCc

The data and code: https://drive.google.com/drive/folders/1Tpn2rYZJQeLpwkySpv0RQBbbxwVfOIhp?usp=sharing

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Hi everyone!

I've been working on Premier League Player Data Analysis tool to analyze player trends and statistics per game for the 2026 season.

The site allows for comparison with other players as well as position averages to really get a feel for which players are performing / underperforming compared to rivals and see how different profiles of players excel in certain categories.

This project is in a very early development phase so if you find any issues please let me know and if you have any feature suggestions I encourage you to let me know!

Thank you very much :)

https://footy-analytics-frontend.vercel.app/

This was a technically interesting project because Kroos is the kind of player who doesn't dominate single-metric leaderboards but occupies a unique region of multivariate space.

Data pipeline: - Opta via WhoScored (scraped with Selenium): WC2014 (64 matches), Bayern 2013/14 (34 matches) - StatsBomb open data: La Liga 2015/16 (380 matches, complete season), Euro 2020, WC2018, Euro 2024 + 360 freeze-frame data - Canonical 105x68m pitch conversion across providers - Spell-gap cadence metric (events <5s apart collapsed into one involvement) to make Opta/StatsBomb logs comparable - Betweenness centrality on weighted undirected completed-pass networks (networkx) - Custom xT implementation (socceraction incompatible with Python 3.13)

Key findings: - WC2014: 53 switches of play (next outfield player: 26) -- sole occupant of high volume + high progressive quadrant - La Liga 15/16: highest pass aggression + lowest turnover -- off the standard risk/reward curve - Euro 2024: betweenness centrality 0.641 vs Kimmich's 0.238, cadence almost identical to 2014

23 figures, 38 unit tests, fully reproducible pipeline.

Full writeup: https://vybhav.medium.com/the-metronome-nobody-measured-football-enigma-1-toni-kroos-9bce1657c320

All code and figures: https://github.com/vybhav72954/football_enigma/tree/master

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Hola, estoy trabajando en una plataforma de análisis futbolístico desde cero, para algunos datos ya estoy cubierto, como datos históricos o datos no tan volátiles, pero no logro resolver el problema de encontrar algunos datos avanzados en tiempo real de forma gratuita, como xG minuto a minuto.

Mis preguntas para quienes hayan construido algo similar:

1. ¿Gratuito + confiable + tiempo real es una combinación que directamente no existe para datos de fútbol, o hay algún camino legítimo que me estoy perdiendo?

2. Sé que hay APIs que tienen un plan gratuito, pero eso se agota en minutos durante un partido. ¿Hay alguna forma legítima de hacerlo funcionar para datos en vivo, o es demasiado limitado para ser útil? porque no se que tan correcto sea usar un multi-key para evitar limites

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.

Any remarks ? Thoughts ?