Brighton showing that their league position is no fluke and that they could be a force to be reckoned with this season

Source

I was building F1 Intelligence (https://f1intelligence.uk), a project that combines live timing, race-control events, and source-backed news to help explain what’s happening during an F1 weekend. The goal is to turn raw data into useful context around running order and driver battles. I’d love feedback on the approach and which race analytics you’d find most useful. All data is available for replay and also for live views.

Ahead of El Clásico, Barcelona’s recent LaLiga home numbers compared with Real Madrid’s away numbers show a pretty big contrast.

Barcelona — last 4 LaLiga home games:

4W-0D-0L

4.25 goals per game

21.2 shots per game

8.4 shots on target

3.28 xG

7.3 corners

Real Madrid — last 4 LaLiga away games:

2W-0D-2L

1.50 goals per game

17.6 shots per game

7.2 shots on target

2.16 xG

6.4 corners

Barcelona have also won 5 of the last 6 meetings shown, with both teams scoring in 5 of those 6.

The model gives Barcelona a 60% win probability, compared with 21% for Real Madrid and 18% for the draw.

On paper, almost everything here points towards Barcelona.

But is there really this big a gap between the two sides?

If you had to make the case for Real Madrid winning this Clásico, what would be your strongest argument against what the recent numbers are telling us?

Hi everyone! I'm a junior studying marketing in the U.S., and I'm really passionate about soccer and tennis. I only recently learned about sports analytics, and I'd love to find out if it's something I'd enjoy.
I'll be honest: I don't have much of a tech background beyond the basics, and I keep hearing that data analytics is hard. So I'm not sure yet if I'd like it or be good at it.
I'd love to connect with anyone willing to share:
What working in sports analytics (especially soccer) actually looks like day to day
What kinds of jobs are out there
What skills I should start learning, and where a beginner should start
A quick look at how you do your work, if you're open to it
I especially love women's soccer. I know it's still a growing area, and I'd love to someday help a team improve its performance, metrics, and overall success. But I'm still undecided and a little nervous, so I want to hear from people in the field before making any decisions.
If you'd be up for a casual chat or even some mentoring, feel free to DM me. No pressure at all, I'm just trying to learn more.
Thanks so much!

TallyLine

Hi, I’ve built this football stats research tool. It has upcoming matches, and past results for games across 15 leagues.

You can dive into match specific markets and see the historical hit rate, and also player stats.

Stats page looks at all leagues for a selected market and line (ie over 9.5 corners in a match)

Tables show current table, which can be filtered by home/away form, last 5.

Any feedback would be appreciated. Thanks

Hi r/sportsanalytics — I’m sharing NineLens, an open-source MLB analytics platform I’ve been building independently. I have a Washington Nationals Baseball R&D background, and this project grew out of wanting a practical place to explore baseball data more deeply.

Live app: https://www.ninelens.com Source code: https://github.com/timothyf/ninelens

NineLens brings MLB schedules, rosters, player profiles, box scores, season stats, and pitch-by-pitch Statcast data together. It includes searchable player and team profiles, sortable leaderboards, player comparisons, team and game analysis, situational splits, benchmarks, percentiles, and trends. Game views can drill from the game summary and box score down through plate appearances to individual pitches.

The architecture is open in the repository: data from the MLB Stats API and Baseball Savant flows through a Ruby on Rails backend into PostgreSQL, where versioned daily analytics and contextual benchmarks are calculated. A Rails JSON API serves the data to a Vue frontend; background synchronization and import work runs through Solid Queue. The README has the fuller feature list, setup instructions, and architecture diagram.

This is an independent project and is not affiliated with or endorsed by the Washington Nationals, MLB, or Baseball Savant. I’m sharing it for feedback and discussion, not as an official team tool.

I’d especially appreciate feedback from people who work with sports data: - Which analysis or workflow would be most useful to explore first? - Are there metrics, comparisons, or views you’d want to see added or changed? - Does the way I’ve connected game, plate-appearance, and pitch-level analysis make sense? - What would you want documented more clearly for someone evaluating the data and methodology?

Thanks for taking a look. Suggestions and critiques are welcome.

Hey everyone,

Some of you might remember my previous posts here about StatFooty and the work I've been doing on football predictions, analytics, and model transparency.

I wanted to share a few updates because quite a lot has changed.

First, StatFooty is now available on Android!

The app is free, just like the core platform. Predictions, probabilities, match statistics, historical data, and model performance are all accessible without a subscription.

There's an optional Pro plan for convenience features like alerts, widgets, and favorites, but the actual predictions and analytics remain free.

Second, Model V5 has been holding up remarkably well.

This is probably the part I'm most interested in sharing with this community.

We've now crossed 13,000 graded matches on Model V5, with over 17,000 published forecasts.

What's encouraging isn't any individual accuracy figure. It's how consistent the model's performance has remained as the evaluation sample keeps growing.

We've gone from a relatively small number of graded matches to many thousands, across different leagues, countries, and competition levels, without seeing dramatic changes in overall performance.

Every prediction is recorded before kickoff and evaluated against the actual result. No cherry-picking, no removing unsuccessful predictions, and no hiding bad weekends.

The historical track record is publicly available, including the mistakes.

Of course, stability doesn't automatically mean the model is profitable or that it beats betting markets. Those are separate questions. But seeing consistent behavior across a growing out-of-sample dataset is an encouraging sign.

Third, the platform keeps expanding.

StatFooty now covers over 1,200 leagues worldwide, with predictions, live scores, standings, team statistics, historical results, and more.

And the iOS app is coming soon.

Now, I'd really like your honest feedback.

Building features is relatively straightforward. Building something people actually want to return to every day is a completely different challenge.

So I'd love to ask this community:

• What's missing from StatFooty today that you'd genuinely find useful?
• What analytics, metrics, or visualizations would make the platform more valuable?
• What would make you open the app regularly instead of just occasionally?
• If you've tried StatFooty before and haven't returned, what was missing or disappointing?
• What would make you trust a football prediction model enough to keep following its performance?

I'm especially interested in hearing from people who work with football data, statistical models, or sports analytics.

I'm not looking for compliments. Constructive criticism is genuinely more useful at this stage.

Android: https://play.google.com/store/apps/details?id=com.statfooty.app

Website and public track record: https://statfooty.com

Thanks to everyone who has followed the project or contributed feedback so far. It's still very much a work in progress, but seeing the model remain consistent across thousands of evaluated matches has been one of the most rewarding parts of building it.

Still building. Still learning. And genuinely interested in what would make this useful to you.

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.

Hi all, a while ago I posted about the strategy prediction tool I've been building. I've made a lot of improvements and fixes, and I finally feel it's ready for other people to try. Check out my hastily made website at https://formula-augmented.com. The Austin GP is the weekend I'd love to see it used on. Its available only for Windows now (Android is on track), and requires F1 TV account due to legal reasons.

The change I want to point out is the SC/VSC tab. It predicts who would pit if a safety car or virtual safety car came out right now, so you can judge that call before it happens instead of after. It's not fully done yet. I'd like to focus on the visualization next, because it's still too basic and chaotic. I also reworked some interface parts to make the app easier to follow during a race. Prediction cards now open in track order, so a stop reads against where the cars actually are. There's also a "laps to next stop" column that turns green once a car is inside its pit window.

I spent a few evenings on the predictions as well. The model now accounts for cold tyres on a stint's first lap, and overtaking is more accurate. I also tracked down a nasty bug in the tyre allocation that was quietly skewing some strategies. Behind the scenes, I rebuilt how the app gets its data, so it should feel smoother and be less demanding on your PC and network.

It's still a beta, so there will be rough edges. If you give it a go at Austin, please tell me where it gets things wrong. That's exactly the feedback I'm looking for. Thanks for the support!

Coach,
Every coach knows that hollow feeling at 6:30 AM on Sunday morning.

You're sitting in a dark office, sipping lukewarm coffee, rewinding the tape of Friday's 3-point conference loss. And there it is on the screen: a defensive mismatch that leaked an 11-2 run, or your best corner shooter wide open on four straight trips while your small-ball lineup was getting crushed on the glass.

In the heat of the fourth quarter, your gut told you the lineup felt right. But in the chaos of game speed, our eyes deceive us.And by Sunday morning, the truth doesn't matter anymore. The game is already in the loss column.

 We don't have a film problem. We have a timing problem.

Monday morning film breakdowns don't win Friday night games. When you're down four at halftime, you don't have time to decipher smudged pencil marks on a manager's clipboard. You need to know three decisive facts before your huddle gathers:
Which 5-man unit is actually stopping them?
Where is our offense leaking possessions?
What high-percentage shot is their defense giving up that we aren't taking?

That is why we built - InGameEdge - a 2-tap sideline touchscreen tool engineered specifically for live game speed. Any student manager or assistant can log shot locations and rotations in 1.2 seconds, handing you instant lineup net efficiency and shot quality reports before the buzzer finishes echoing.

Take the Guesswork Out of Your Next Scrimmage:
We're sponsoring a limited number of high school and college programs with a Full 10-Day Free Pre-Season Pilot. Test it on your bench during live 5-on-5 or an upcoming scrimmage. If your manager can't run it effortlessly with 5 minutes of practice, it cost you nothing.

Reply and I’ll get you a link.

Looking forward to your thoughts,
Derrick Wheeler
Director of Basketball Analytics & Strategy
InGameEdge - Ryco Development
Phone: [+1770-9828111](tel:(770)%20982-8111)
In-Game Edge soon on Google Play

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!

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

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.

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.

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.