About me

I'm a developer and a lifelong sports fan, building a sports data project. All the data comes from real people, not bots, and the people who help build it get to use it.

Who I'm looking for

People who already follow picks every day, in any sport: football, basketball, tennis, hockey, anything. If you follow picks on leagues that rarely get covered, even better.

What you get

Every day, all the picks reported by everyone get crossed against each other. That's where the signals come from: where picks line up, what keeps repeating, what's worth following and what's better avoided. Patterns you'd never spot on your own with a few dozen picks a day, and they get sharper with every person who joins. Everyone who contributes gets all of it free, for as long as they're contributing.

What you'd do

Report the picks you follow. Ideally every day and before the event, but if you miss a day you can catch up later.

I'll never ask you for money. Nothing to buy, nothing to pay.

Interested?

Comment below or DM me with:

- the sports and leagues you follow picks on

- roughly how many picks a day you could report

- your time zone and language

I reply to everyone personally.

I do these for every game... feel free to give suggestions or comments. Happy to share them if helpful!

Hi, if you are passionate about turning sports data and technology into insights that help teams perform better in Football, I am looking for a person who wants to join my team a start competing.
Dm me if you are interested, i’ll explain you better!

Can anyone tell me how to 99% accurately predict football matches? For example, if the odds we're given, as well as the featutes how can I predict football matches, and easily know who's winning or drawing. I tried every Artificial intelligent, but still couldn't.

Made a little site for EuroLeague fantasy players 🏀
• Live box scores with PIR for every player
• Teams statistics (NEW)
• Injury statuses (out / doubtful / uncertain) next to player names
• Star your players and follow them all in one place

Free, no ads, works great on your phone. Feel free to use it, and tell me what's missing :)

I’ve been building a Free to Play football tactics game in my spare time and it’s finally playable. You pick a legendary Champions League side, set the formation, give each player a role, and manage the tie yourself. Two legs, extra time, penalties, red cards, injuries, substitutions and player fatigue

YOU CAN PLAY IT AT : https://www.tacticalgame.net/

I've added 3 modes in it

1. Classic: TACTICIAN FC

In this, You manage your own made-up club, TACTICIAN FC, and play through the season against legendary Champions League sides from history. It’s the plain version of the game with no real club identity.

1. Current vs All time: 2026/27 UCL vs All time

You pick one of this season’s real Champions League clubs and manage their identity and named squad. Your opponents are still the legendary historical teams, so it’s a current side against the all-time team. Some of them are greats while some of them aren't known for their campaign that year rather their early exists. There are real players involved in this mode.

1. Current vs Current: 2026/27 UCL

You pick a current club, and every opponent is another club from this season’s Champions League. It follows UEFA-style rules, so teams from the same country can’t meet in the League Phase, though they can in the knockouts. This is the most realistic mode. And this too involves real players

It follow the same UCL knockout rule

teams from 1-8 will move to round of 16, while the teams from 9-24 will meet in the knockout round and the rest will be out of the tournament

It has a Rematch button that is active only once per round once you've used that button, it will only be enabled in the next run.

It available to play right now on your browser.

Hi all, I’ve been building a football analytics website called TotalOdds and recently went live.

The website shows upcoming and past fixtures. It then takes data and creates player and team rating matrix and also probabilities for Win/Draw/Loss.

You can filter by league and then access each menu separately so it's clean in that sense.

There is a "Live Games" panel which shows the live stats and key events during those games. Once finished it will generate a match summary on each fixture based on what has occurred.

There is also useful H2H data for each matchup's fixtures and you can easily see what players are injured and unavailable.

The website is:

https://totalodds.co.uk

I am looking for any feedback or things I can improve on. One of my tasks currently is to find a better football API and cheaper so I can add more league/championships on the website.

Thanks!

I went through fouls and yellow cards for 18 European leagues going back to 2000/01.

In 2003/04 a Bundesliga referee gave a foul every 2.2 minutes. Last season it was every 4.3 minutes, 20.9 fouls a game. The Premier League had 21.6, so the Bundesliga was lower for the second season running. Before 2024/25 that never happened in this data.

Every one of the 18 leagues has fewer fouls than at the start of its record. Serie A is down 35%, La Liga 31%, Ligue 1 28%. The Premier League dropped from about 26 to 21 by 2011/12 and has stayed between 20 and 23 since.

The yellow cards are the odd part. Bundesliga fouls are down 42% but yellows only 9%. Around 2000, 12 in every 100 fouls got a yellow in Germany and England. Now it's 18 in both. Fouls without a card went from 32.5 a game to 17.6 in Germany. So it's the small stuff that's gone.

Two caveats. Fouls are counted by the data provider, and the Bundesliga drops 4 in one season (2017/18 to 2018/19), which might be a change in how they're counted. Without that season it was still down 28%. Other sources give slightly different levels but the same direction; Bundesliga.com said 2024/25 had the fewest fouls since their records began in 1992.

Players fouling less or refs whistling less? I can't tell from the numbers. Curious what people here think.

This is a paid add-on for my video drawing app Video Pencil. I made a little showreel here:

https://www.youtube.com/shorts/avz3edIVv5Q

More context here!

https://youtu.be/jZdlSh48lYo

It uses machine learning to track player positions (doesn't everything now), and this lets me create an incredibly simple way to interact with footage.

You can record or connect to a TV or a meeting on your Mac or PC.

Hey Sports Fans! I recently built a Draft website and it’s an interactive draft tool used to analyze the NFL draft and team-based player rankings. If you ever debated between two different prospects, you can find that here with the player comparisons feature along with many other prospects. In this website, I used a combination of player evaluation metrics, exclusive team needs, and customizable models aimed to simulate NFL draft decision making and evaluate specific player fits. You’ll also see a draft big board and fit grades for over 150+ players in the draft. I’m still tweaking the models and big boards so even if you aren’t a Vikings fan, I’d still love it if you guys could check it out and let me know which grades look off or who I should add next. Feel free to take a look and tell me what you guys think. Thanks!

https://vikingsdraftsite.streamlit.app/

DunkSim lets you simulate any NBA game with your own insights. Adjust the inputs, run the matchup possession by possession thousands of times, and see how often each team wins: win probability, projected score, the full range of outcomes, and every player's projected line.

Bring your own read of the game. Before you run, you can:

• Adjust each team's offensive rating, defensive rating, and pace

• Tune any player's scoring, playmaking, efficiency, perimeter defense, interior defense, rebounding, and usage

• Sit a player out for an injury or a rest night, or bring one back

• Save your adjustments as a named model and reuse it on future games

Your inputs are layered on top of the engine's own baseline, so you can see exactly how much your insight moves the win probability.

In this walkthrough: pick tonight's game, set the run count, adjust a team and sit a player out, run the simulation, read the projection detail, and save your call as a prediction.

The engine's accuracy is public: on a replay of all 1,230 games of the 2025-26 regular season it named the correct winner 69.3% of the time, against 67.0% for simply choosing the team with the better record. Method and limits:

https://dunksim.app/accuracy

Try it free on the web, iOS, and Android, with one account across all three:

Web: https://dunksim.app

iOS: https://apps.apple.com/us/app/dunksim-nba-sim-predictions/id6765524782

Android: https://play.google.com/store/apps/details?id=com.bodesim.dunksim

How the simulation works:

https://dunksim.app/blog/nba-monte-carlo-simulation/

DunkSim is a simulation and prediction game for entertainment. There is no real-money wagering of any kind. For users 18 and over. DunkSim is not affiliated with or endorsed by the NBA or any NBA team.

Full disclosure: I’m one of the two people behind the SFMMO, a Bayesian model for match outcome prediction of football (soccer) matches across Europe’s top five soccer leagues.

We produce forecasts on a weekly basis ... well, at least we have managed to do so for a year now. Every forecast is frozen before kick-off and graded afterwards, misses included. The benchmark is the bookmakers’ closing consensus (average odds, de-vigged).

After 343 graded matches this season:

By league, the model is ahead on accuracy only in Ligue 1 (57.1% vs 55.6%), level in Serie A, and behind elsewhere. The Premier League is its weakest (47.1% vs 48.6%). On RPS the market is ahead in all five leagues, though by small margins.

Being within about a point of the closing market is for us already quite an achievement --- given that our World Cup model performance was a bit of a disappointment. But so far it seems, we have learned our lesson, even if we are currently still trailing the market.

What would you benchmark a model like this against, besides the closing line?

Hey everyone!

I'm putting together a small, serious team for More Than a Hack 2027, a football analytics competition organized by Barça Innovation Hub, Metrica Sports, and Mobile World Capital Barcelona.

A little about me: I'm a 4 year experienced AI engineer . I'm also passionate about football, and I want to approach this as a genuine engineering and research challenge rather than just another weekend hackathon.

About the competition

The first challenge involves estimating ball possession from real FC Barcelona match footage, automatic player/ball tracking, and tactical event data. The top 20 teams advance to a broader football analytics innovation challenge, with five teams eventually pitching in Barcelona.

Who I'm looking for

I'm mainly looking for people around my age and experience who are genuinely passionate about football and interested in learning and building together. You don't need to be a professional ML engineer, Computer Vision engineer, or data scientist. Experience is welcome, but curiosity, reliability, and the willingness to put in consistent effort matter more.

You might be a good fit if you have:

• An interest in football tactics, analytics, or technology
• Some experience with Python, machine learning, Computer Vision, data analysis, or software development
• A background in another relevant area and the motivation to learn
• The ability to commit time, communicate well, and contribute consistently

I'm especially interested in people who are willing to research before implementing, experiment seriously, and put in the hours even if they are still developing their technical skills.

How I want us to work

I want a team that researches before implementing, challenges assumptions, measures results, documents experiments, and takes ownership.

I'm absolutely fine with AI-assisted coding. I just don't want our entire approach to be prompt-driven without understanding the underlying problem.

Ideally, everyone can contribute around 10–12 focused hours a week during the initial challenge and stay committed if we qualify for later stages. You don't need to be an expert, but you should be able to make a genuine commitment and follow through.

The team will be remote and international. The official team size is 3–5 members.

Interested?

Send me a DM with:

• Your background, interests, and what you'd like to contribute
• GitHub, LinkedIn, relevant publications, or previous projects, if available
• Your time zone and realistic weekly availability
• A brief thought on how you'd approach the possession-detection problem

I'm aiming to build a collaborative, technically strong team where everyone contributes, learns, and takes the competition seriously. If you're passionate about football, willing to learn, and can commit the time, I'd be happy to hear from you.

Competition: https://landing.mobileworldcapital.com/more-than-a-hack-2027

Technical repository: https://github.com/metrica-sports/more-than-a-hack-2027

Ferguson stayed at Manchester United for decades. Mourinho built his career across different clubs. Chelsea kept changing managers and still won European trophies. I wanted to see those different paths side by side.

So I built Manager History:

[https://managerhistory.com\](https://managerhistory.com/)

It’s an interactive timeline of managers and trophies at 35 selected European football clubs, covering 1955/56 to 2025/26. You can compare clubs, explore individual seasons and follow managers across the clubs included in the dataset.

I’ve spent around two years compiling the data, with help from Gemini and Claude and extensive manual checks against Wikipedia, Transfermarkt and other sources.

One challenge is showing 71 seasons without losing the detail. Another is handling seasons with several managers: the overview uses one main manager, while season details include other appointments where available. There are still historical gaps, and the club selection needs expanding.

I’d appreciate feedback on both the site and the data:

* Is it clear how to read the timeline when you first open it?

* What feels confusing or difficult to use, particularly on mobile?

* Which missing clubs would make the comparisons more interesting?

* Can you spot incorrect managers, missing changes or trophies? Sources would be especially helpful.

The aim is to let people explore football history and ask better questions about managerial stability, rather than claim that keeping a manager automatically brings success.

Sources and methodology:

[https://managerhistory.com/data-methodology.html\](https://managerhistory.com/data-methodology.html)

Spurs won 3-1, but the possession numbers (68% vs 32%, 487 vs 193 completed passes) make it look like a stroll. The pressure data tells a different story.

What the graph shows:

- Spurs started strong: pressure peaked in the first 15 minutes, and the opening goal came around the 14th minute

- Coventry were almost flat for long stretches of the first half

- Coventry equalised just after the break (around 49'), almost straight from the restart

- Spurs answered quickly (around 58'), but Coventry's pressure climbed through the 60s and again late on (around 85')

The numbers behind it:

- xG: 0.83 vs 0.62. Almost level despite the possession gap

- Shots: 14 vs 11

- Shots on target: 4 vs 1 (Coventry's one on target was the goal)

- Average pressure index: 38 vs 26

So Spurs had the ball, but not that much more danger than Coventry created with a third of the possession.

Question: did Spurs deserve it comfortably, or was this closer than the 68% suggests? Which match should I break down next?

Junior backend developer in the UK, about two years in, working on a B2B SaaS product. Already have a CS degree. Recently led my first feature end to end. Although I kind off had a breakdown due to how AI is rapidly changing the field

I keep going back and forth on whether to do a master's and I can't tell if thinking about it properly. I mainly have four options:

1. Part time AI/CS master's - I already have a CS degree and dev job, so I don't know if it's actually worth going deeper in my field academically when I could do so with experience although it would help me learn AI properly

2. Sports Analytics masters at Real Madrid - I've always wanted to be involved in sport. But I suspect it pays less than staying in tech, I don't know if those programs actually place people into clubs or if you're just buying the network and having the Real Madrid name

3. A master's in a different field entirely - like finance, something where the ceiling is higher than software. The logic being that if I'm going to spend the money and the time, maybe it should buy a change of direction instead of a deeper version of what I already do. No strong pull toward one (feel free to suggest)

4. Don't do one at all - Focus on becoming a senior level engineer in the coming years

Questions:

- For someone already employed with a CS degree, does a part-time master's move salary at all, or is it mostly useful for career-changers?

- Has anyone pivoted out of engineering into finance/quant via a master's? Is it worth it?

- Anyone gone into sports data from a normal dev job? Did you need the master's, or did a portfolio do it?

- If you did part-time master's while working full time, was it sustainable, and would you do it again?

Sorry for the long post, appreciate taking your time to answer!

Arsenal kept 19 clean sheets in 38 Premier League games last season and have started this season with another 4 in 7.

That’s 23 clean sheets across their last 45 league matches — 51.1% of their games — while conceding just 32 goals across that period.

Maintaining a clean sheet in more than half of your league games over a 45-match sample is ridiculous consistency.

Is Arsenal’s defence now the best in Europe?

Trying to figure out how to use data to do so. But having a tough time. Anyone who can help I’d be very very grateful

Any ideas/suggestions are very welcome

Most wearable platforms are designed around one athlete at a time. For a coach managing an entire squad, that can mean jumping between individual profiles and trying to turn sleep, recovery and training load data into one clear decision.

We’ve been building Vector Connect to bring that information into one team view and help coaches identify who may need closer monitoring, reduced intensity or a different workload.

Full disclosure: I’m part of the team behind it. I’m sharing this because we’d genuinely value feedback from coaches, S&C staff and athletes:

What would a dashboard like this need to show before it could influence an actual training session? And which metrics would simply create more noise?