r/FootballDataAnalysis

Jayden Fevrier Full Report

Fevrier made the move from Stockport County to Blackburn Rovers recently for around £500k

I break down Fevrier's data from the last 2 seasons, including his spell with Charlton in the Championship.

I know it's a bit late, I have had quite a few projects on the go. Sam Morsy is on the way.

What are your thoughts on Fevrier?

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.

https://preview.redd.it/khvhn0zwqkih1.png?width=1287&format=png&auto=webp&s=c448ae552512a2ce90557b592fa90bd30bfd4c0a

*disclaimer: this was made with the assistance of AI

I've been reading r/vibecoding for quite a while, but this is my first time actually posting here.

First, an important disclaimer: I'm not selling anything, I'm not looking for customers, and I have no intention of selling this app or any related service. There are no links, referrals, subscriptions, or promotions. This is simply a personal project and I'd like to exchange ideas with other people who use vibe coding.

I'm a self-taught programmer, and for some time now I've been developing a server-based application for sports analysis and predictions.

The project has grown quite a lot compared to the original idea.

At the moment, the system:

• collects data and predictions from hundreds of different websites;
• automatically compares the different sources;
• uses sports APIs to analyze team history, previous results, recent form, and statistics;
• uses AI to analyze and compare all of this data;
• generates its own final prediction;
• assigns a confidence level to each prediction;
• avoids generating predictions when the data is too uncertain;
• automatically records predictions and their final results;
• analyzes failed predictions afterward to try to understand what caused the mistake;
• measures the reliability of individual sources over time;
• gives different weights to sources based on their historical performance;
• detects conflicting signals between different sources;
• performs backtesting on historical data;
• keeps separate statistics by league, team, and prediction type;
• has its own backend/server, API, database, user interface, and dashboard.

At the moment, on the predictions I'm tracking, the system is getting around a 70 / 75% hit rate.

Of course, I know that 70/75% by itself doesn't mean much. It depends on the type of prediction, odds, sample size, leagues, and many other factors.

So I'm definitely not claiming that I've built some kind of miracle system or something that can "beat sports betting."

My problem now is almost the opposite of when I started: many of the features I originally had in mind are already implemented, and I'm starting to run out of ideas for what to add next.

So I'm curious:

If this were your project, what would be the next feature you would build?

It doesn't necessarily have to improve the prediction hit rate.

It could be related to AI, statistical analysis, server architecture, learning from mistakes, data management, visualization, or something completely different that I haven't thought about yet.

I'm especially interested in experimental ideas or suggestions like:

"If this were mine, I'd try adding..."

This project was mainly created to learn, experiment, and see how far I can push something built through vibe coding.

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?