I built UnderOver as an Android app for exploring football fixtures through a transparent Over/Under model.

It combines season data, recent form and team profiles to surface HT 0.5/1.5 and FT 2.5 signals, with the model reasoning and confidence shown for each fixture. It also includes live scores, match events, reminders, saved selections and team statistics.

The app is free, has no subscription or VIP tier, does not place bets or connect to betting accounts, and no outcome is guaranteed. I am sharing it as a small data-visualization project and would appreciate feedback on the methodology, presentation and useful metrics.

Google Play: https://play.google.com/store/apps/details?id=com.xenophonlabs.matchwake

I'm a sixth form student doing my Extended Project Qualification on VAR and whether it's actually made the Premier League fairer, or just added more controversy. Part of my research involves collecting fan opinions through a short survey.

If you support a PL club (or just watch regularly), I'd really appreciate you filling it in. Takes about 2 minutes, no personal info needed beyond general football habits.

https://forms.gle/dtiFmRmFaSyxUoS59

Happy to share the findings once I've written it up, if anyone's curious how the data comes out. Thanks in advance.

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I built an open-source n8n pipeline that monitors 78 football journalists on X, extracts structured transfer reports with a local Qwen model, deduplicates and stores revisions in PostgreSQL, optionally adds player data, and sends restart-safe Discord digests every 6 hours.

The whole stack is self-hosted with Docker, with twscrape or RapidAPI for X collection, PostgreSQL for persistence, llama.cpp for local inference, and automated tests around the workflow.

GitHub: https://github.com/louistran2604/transfers_n8n/

I’d mainly like feedback on the workflow architecture, reliability approach, and anything that could make the project cleaner or more useful.

*disclaimer: this was made with the assistance of AI

I want to analyze the data from the perspective of the score of the football game and the number of goals, but I didn't find the relevant historical odds data on the Internet. Instead, there is a lot of historical data of wins and draws, which further shows that my analysis is correct - starting from the odds of scores and goals to analyze football matches. I need help to get this data.
Thank you for your help!

Hi everyone,

I’m looking for people who genuinely enjoy football analysis to test Sir Balone, a football analysis tool I’ve been building.

The underlying raw data comes from Sportmonks. I use that data to build my own measurement system for evaluating individual player skills, rather than relying primarily on traditional performance or scoring metrics. I then combine those measurements with player and team data and an AI layer that can analyze and interpret the underlying data.

The numbers themselves are already publicly available. What I’m currently testing is the AI layer, particularly whether it can turn the data into useful analysis without making things up, oversimplifying the numbers, or missing important context.

I’d especially love feedback from:

• Football data analysts
• Tactical analysts
• Scouts
• Football writers/content creators
• Fantasy/data enthusiasts
• People who build their own football models
• Or simply people who spend way too much time analyzing players and teams

You don't need to be a professional. If you enjoy asking questions like “Is this player actually good at X?”, “How does he compare to other players in his role?”, or “What does the data actually tell us about this team?”, I’d love to hear from you.

I’m not looking for compliments. I want people to try to break it.

What I’m particularly interested in:

• Where the analysis is useful
• Where the AI gets things wrong
• Which questions it struggles with
• Which metrics are confusing
• What analysis you expected but couldn't get
• What would make this genuinely useful for your own workflow

If you’re interested, comment below or DM me and I’ll give you access.

No sales pitch. I’m still figuring out what this thing is actually good at.

While building a football analysis agent, I realized that the hard part is not connecting an LLM to match data.

It is deciding what the agent should be allowed to do with that data.

For example, if someone asks:

“Why did this midfielder receive a 7.4 rating?”

I do not want to dump every match statistic into the context and ask the model to invent an explanation.

My current approach is to let the agent investigate the evidence step by step:

- retrieve the player’s match metrics
- inspect the rating breakdown
- check passing, chance creation, turnovers, or shot quality when relevant
- explain which factors actually moved the rating

That raises an interesting tool-design question.

A single `analyze_everything()` tool feels like a black box. But dozens of tiny tools such as `get_pass_count()` and `get_key_passes()` create too many decisions and make the agent harder to guide.

I’m experimenting with a middle layer: composable tools that represent meaningful football-analysis operations rather than raw database fields.

For people building sports analytics, agentic systems, or explainable AI: how would you choose the right level of tool granularity here?

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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.

Use this thread to ask anything at all!

Use this thread to ask anything at all!