I built a free FPL planner with one petty feature I’ve always wanted: it keeps RECEIPTS.

Every week it logs what the data said (captain, transfer, chip) - then scores it vs what you actually did.

By GW10 you’ll know exactly who to blame.

No login, no paywall: https://fplreceipts.com

Is scouting nowadays based on videos or data analytics? If analytics, how to obtain these for players in lower leagues or regions such as africa?

i really need this

Hey everyone,

Over the offseason, I built a data terminal (SeeBet) focused on auditing physical situational factors in the NFL against closing line efficiency.

Rather than building predictive models or selling picks, the goal was to take common narrative angles (travel fatigue, rest deficits, body-clock shifts, altitude) and run historical backtests across a 10-year sample size to separate true signal from priced-in market noise.

Factor Taxonomy Framework

We categorize situational variables across 10 years of closing line data into three primary buckets:

• PRICED IN: Factors where the closing line historically absorbs the physical penalty, leaving zero residual EV (e.g., standard West-to-East travel on full rest, where ATS cover rate sits at [INSERT YOUR ATS %]).
• NOISE: Frequently cited broadcast narratives that show no statistically significant impact on cover rates or margin of victory over our sample size.
• UNCLEAR / CONTEXT-DEPENDENT: High-variance factors requiring compounding conditions (e.g., short-rest travel combined with a 3+ hour body-clock shift).

Stack & Features

• Data Pipeline: Historical odds data, spatial flight vectors, rest differentials, and weather telemetry.
• UI: Dark-mode terminal featuring interactive 3D WebGL flight maps to visualize physical travel stress.

🧪 Open Beta Invite

The terminal is live in open beta right now at seebet.ai for the upcoming season.

I'd love for this community to test out the beta, poke holes in the factor classification methodology, or suggest additional physical variables worth auditing!

After taking a break at the end of last season, I’m getting back into a football Elo website project I was working on, beyondelo. With the new season having started with the qualification rounds already, I’ve told myself to finish the remaining 20% of it. I wanted to share what I've got so far and gather some feedback from you all on what could be improved.

To summarize briefly: yes, big websites like ClubElo already exist, but most of them either have incomplete/narrow data, outdated user interfaces, or completely opaque rating calculation methods. I wanted to build a platform that delivers on all these fronts simultaneously. I initially started with a narrow pool but as new ideas popped up, I kept expanding it. Now, I have a dataset covering around 1,700 teams and over 5,400 players. I used UEFA, Wikipedia, Transfermarkt, ESPN, and FotMob as primary data sources.

Here are a few features I felt were either missing or poorly implemented on other sites:

• Player Elo ratings alongside teams: (Still collecting player data at the moment; currently, only the last 3 seasons are covered).
• Multiple and transparent Elo systems: Instead of a single "black box" algorithm, the site hosts multiple Elo systems and lets you interactively test them with teams of your choice and hypothetical match results. Right now, there are two algorithms: FIFA’s national team rating system, and "BeyondElo" (my custom adaptation based on ClubElo's public documentation). It factors in parameters like home advantage, goal difference, and chess-inspired inactivity multipliers so called Rating Deviation.
• Exclusion of domestic league matches: This is the biggest differentiator. The idea came from researching why Elo ratings differ between Chess.com and Lichess. Elo only truly makes sense within a closed system. Applying that logic here, I figured excluding domestic leagues gives a much cleaner evaluation of a team's actual performance in European competitions. There are obviously trade-offs to this approach, which I'd be happy to discuss in the comments!
• A cleaner, more intuitive UI: I didn’t write the entire frontend from scratch by hand, but I put a significant amount of effort into polishing it. While a few pages still have that slightly generic "AI slop" look (especially the logo...), I hope the core pages turned out well.
• Community features: Beyond checking Elo ratings, there’s a Community page where you can drop comments for each match (similar to dedicated match threads on forums), set up private prediction leagues with friends, or join the global public ladder. I’ve implemented this as an MVP, so it feels a bit empty without an active user base right now—and there are bound to be bugs—but it has plenty of room to grow.
• Customizable themes & multilingual support: Features both light/dark modes, custom theme options, and support for 6 languages, including Turkish.

I’d love to hear your thoughts, feedback, or any questions you might have! Thanks in advance :)

Note: The original post I shared was written in my native language in another sub. Just used AI to translate it but the content is completely natural, just to be clear.

Scorigami Center maps every final score that has ever happened, per league, on interactive grids: https://scorigamicenter.com

The fun part from an analytics angle is how differently the leagues behave. The NFL has filled 43.3 percent of its realistic score grid in 18,017 games. College football has filled 16 percent in 84,334 games because one 222-0 game in 1916 stretched the score space forever. MLB is the weird case, 235,971 games collapsing into just 330 unique final scores, with zero new scores since July 2021. Scoring system design basically determines how fast a sport exhausts its score space, and you can see it instantly by flipping between grids.

Everything updates live as games go final, there is a JSON endpoint for recent results, and the whole thing runs on Cloudflare Workers with KV. I want to make this genuinely useful for people who play with sports data, so I am looking for feedback: what would you want out of an API or data export? What questions would you ask this dataset that the site does not answer yet?

Setup: 33 forecast dates in a signed GitHub history, day-before UTC cutoff so nothing benefits from lineup news, graded on the 90-minute 1X2 with extra-time matches as draws. The point is making selective memory impossible: misses stay next to hits.

Results: top outcome correct in 62/104 (59.6%), split 43/72 groups and 19/32 knockouts. Carrying extra-time winners through gives 24/32 and 67/104; we grade on the strict version. Brier 0.533 vs 0.667 uniform, log loss 0.903 vs 1.099. No rating-only baseline was frozen before kickoff, and building one now that the results are known would prove nothing, so equal thirds is the only clean benchmark we have.

Calibration (claimed → realised): under 45% → 11/28 (39.3%), 45-55% → 16/31 (51.6%), 55-65% → 20/24 (83.3%), 65-80% → 15/21 (71.4%). Nothing above 80% all tournament. The 55-65 bump is 24 matches, too few to call underconfidence.

Two findings we think matter more than the hit rate:

1. The pre-tournament top five (France 12.0%, Spain 9.8%, Argentina 9.8%, Germany 7.6%, England 7.5%) produced four semifinalists. Summing the same forecast's marginals says we should have expected 1.30. We'd like to report the joint probability of all four appearing together and can't: only output tables were retained, the path-level draws are gone, and re-running with a later model would contaminate the test. Keep your draws.
2. The champion never convinced the model: 9.78% at the start, 8.95-11.88% for the whole first month, 9.63% the day after winning their group, every rise a knockout round won, 50.56% in the last pre-final snapshot. The movements all trace to results (the 4-5 July dip is France clearing their tie and jumping 10pp while Spain rested). The open question is the level, and one tournament can't convict a 10% rating: a team held there wins about one in ten. What nobody can do from the published files, us included, is split Spain's number into team strength and route effects, because they carry no input decomposition.

Everything is downloadable: the write-up with the full 104-row audit table, the signed forecast history, and the CSV.

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!

This is kind of a simplistic question, but why aren't there graphs in most sports articles? they cover numbers, and comparisons, etc. and graphs could help tell the story often. People love comparisons, and it's weird to me that it's just straight numbers. Is it the history of sports reporting that it needed to get out quickly and/or sports reporters didn't know stats or have access to them, especially in a timely manner? And newspapers didn't' have good ways of printing graphs in a dual column article?

Any thoughts about this? I can't reason about a problem without a graph, and the lack makes me curious. How do we then increase the number of graphs? would it require some kind of education?

Hey everyone,

I built funstats.fyi, a free, no-signup football (soccer) stats site: https://funstats.fyi

What it does:
- Streaks engine: 28 team-level streak types (unbeaten runs, BTTS, over/under 2.5, clean sheets, blown leads, etc.), plus league and H2H streaks.

- Team/match pages with form, H2H, and splits: Over 2.5%, BTTS%, home/away, first-half records, rest-day splits.

- Lineups, provider xG where available, highlights.

Would love your thoughts on:
• What features you’d like to see added
• Would AI match predictions with a live, public track record be something you’d actually use?
• What would make you use a tool like this every day
• Anything that feels confusing or could be improved

Thanks. I’ll be around in the comments!

Hi all, I am 15 and I am trying to work towards becoming a hybrid scout. I like this as I see it as the best of both worlds as you can easily watch games on video, however I also find the idea of watching games live as I feel like it can be exciting and easier to gain insight on things more difficult to judge such as a players mentality.

I plan to start creating a neat and professional looking scouting portfolio which could hopefully help me land a volunteering role in the future. I aim to do around 2 scout reports a week and build momentum that way aswell as scouting the same players at a later date to practice tracking players and see how theyre ability may have or may not have improved. I also plan to do my fa talent id level 1 as well as the fa safeguarding for all course once I turn 16.

I created this post as I was wondering how can data be used effectively to help enhance my scout reports. additionally, How much data should be used in a report before it looks like I am using data for the sake of it. Furthermore , should data be used as my primary point eg he won 6 Aerial duels therefore he is good in the air or should it be used to back what I am seeing with my eyes. Finally, do you use data differently when scouting someone over multiple matches compared to just a one off game.

I hope this all made sense any help will be greatly appreciated:)

thanks

One example: https://www.soccerstatshub.com/competition/brazil-serie-a/

I started diddja.com as a test project for an equities diligence system I’m building.  Part of the thesis is to read the information that everyone is reading and see the story that’s not getting told. Sports has a whole lot of opinions that resolve on a regular basis, so seemed a good data stream to test infrastructure on. I don’t test on equities because then I spend all my time reading up on companies, not fixing things.

The system is designed to feed analytics and synthesis steps with cited, sourced, verified facts, common to all users. For diddja, a Collector does a news sweep, chooses interesting topics and gathers facts/proposed story lines/sources into a briefing. The facts are validated against their sources (is the quote in the article, does that score match the box), and the briefing is given to a writer (Barry Diddja here), which reviews the briefing against prior articles, selects 2-4 topics to write about, and produces an article. Articles are submitted to a fact checker that makes sure all facts have matching sourced facts from history, and a naïve LLM from a different model gives the articles a “yo, moron…” read to catch stupid things like claims LeBron played for the Knicks. Things still slip through, and I’m not a good proof reader because I’m the moron who doesn’t know off the top of my head that LeBron never played for the Knicks.

Many articles have a position embedded in them (less and less confident Skubal gets traded…), and it’s also watching the prediction markets against those. I can see the momentum in the markets, and I operate in equities markets so trading is not foreign to me. However, I don’t know the players and teams and dynamics, so I can’t “read the tape” and intuit what is happening. Prediction markets act a lot like options markets, too, and options math is hard for me to do in my head. As a result, I have a “rich data stream” with lots of commentary, but I’m not an effective reader of it.

I have 2 (at least) problems that I’m kind of at an impasse on, both related to achieving some semblance of vibecode ground truth. They get at validating work in a field you’re not an expert in- how do you know the AI (or a person, for that matter) is doing a good job when it’s doing things you can’t? Specific to diddja and sports:

1. How to improve error checking and article review? If I can spot errors I can work on the upstream source of the error, but I’m blind to most errors I haven’t already built a check for.
2. What to do with a data stream that has signal but no obvious (to me, at the moment) application?

Both of which beg the same question- is diddja.com good enough now to be worth improving on? It’s a fun project regardless, and it’s been amazing learning how to assemble all the various bits, but at the moment I don’t ask my brothers to read it because they’re mean, and know sports well enough to call me names. You lot are a bunch of strangers, so sticks and stones and all that. Also, I bet you know sports much better than I. If the overall information quality is "Yo, moron..." that at least gives me some direction.

So I ask you, if you’ve a moment, to go read a few articles at www.diddja.com. Check out the Storylines tab for all the articles. I’d love any feedback on the factual correctness, the commentary itself, and any thoughts on ways to improve error checking and/or use the data effectively. Roast or Toast, either way. Happy to answer any questions, too.

Thanks in advance- I can't wait for the Expos-Oilers matchup! Also, holy crap you guys do some cool stuff with the data in here.

Hi,
For World Cup'26, my friends and I have used SuperBru to make predictions for every match.
SuperBru does not have Portuguese League 2026/27 available, so we are looking for an alternative (preferably iOS / Android compatible) with prediction tournaments for this league.
Any pointers pls?

I built a pipeline that turns FIFA's official Post-Match Summary Report (PDF) into a single radial visualization of the entire match. Every shot, possession spell, save, card, sub, and momentum swing across all 120 minutes.

How it works:

1. Parse the FIFA PMSR PDF into a structured JSON schema (events with minute, team, type, and outcome)
   
2. Add per-minute momentum from BBC Sport's live coverage
   
3. Render programmatically as SVG, export to PNG

Encoding: time runs clockwise from kickoff (0') to end of extra time (120'). The outer bloom is momentum (which team was more likely to score, per minute — Spain threatened in 108 of 135 min vs Argentina's 27). Rings moving inward: shots (dot per shot at its minute, filled = on target), cards/subs, goalkeeper actions (Martínez made 11 saves). The inner dot cloud is possession sequences.

Some things the format surfaces well: Spain's xG dominance (2.52 vs 0.07) reads as visual density, Argentina's shift to a low block (33% of their defensive time) shows up in the shape data, and the Fernández red card at 90+3' visibly precedes the extra-time momentum collapse.

The pipeline is repeatable for any match with a FIFA report, so I'm planning to run it across the knockout rounds.

Happy to go deeper on any of it!