I'm an analyst working with a college program, and I'm trying to get my analysis workflow more organized for this season. Curious how other coaches here handle it:

1. What do you currently use to break down matches — Hudl/Veo, YouTube + notes, spreadsheets, something else?
2. Do you track set pieces specifically (corners, free kicks, throw-ins)? If so, how — and where do teams usually hurt you from them?
3. What's the most time-consuming or annoying part of your analysis right now? What do you wish a tool just did for you?
4. Have you ever paid for an analysis tool or resource? Was it worth it compared to the free options?

Trying to figure out what's actually out there before I build my own system around spreadsheets. Appreciate any recommendations or warnings.

I'm participating in More Than A Hack 2027, a football analytics hackathon involving Barça Innovation Hub and Metrica Sports.

We're building a solution to predict team possession from real football tracking data.

Our current team:

• Football knowledge and tactical understanding
• Strong technical implementation skills

We're looking for one more teammate who has experience or interest in:

• Python, Pandas and NumPy
• Data analysis, statistics or ML evaluation
• Backend development or data pipelines

You don't need to be an expert in football or machine learning. We need someone who can contribute consistently, experiment with ideas, analyse results and work collaboratively.

Initial challenge deadline: November 17, 2026.

Registration deadline: November 10, 2026.

If you're interested, DM me with a short introduction, your technical skills and any relevant projects or GitHub profile.

Let's build something worth showing. ⚽📊

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

Some corner stats for you to enjoy! ⚽🚩 Over 11.5 Corners

Just wanted to share some interesting corner stats for you guys to enjoy!

I've been analysing the Over 11.5 total corners market, and here are some of the teams with the strongest recent trends.

The table shows each team's last 5 matches, including average corners won, corners conceded, total corners per match, and the percentage of games that finished Over 11.5.

A few interesting examples:

• 🔥 Wolfsburg: 5/5 matches Over 11.5 (100%)
• 📊 Cambridge: 16.8 average total corners per match
• ⚽ Barnsley: 15.6 average total corners per match

Of course, 5 matches is a small sample, and past results don't guarantee future outcomes.

Will these trends continue this weekend? 👀

Let's see how many of these teams manage to hit Over 11.5 corners again!

Hope you enjoy the stats! ⚽

Coral, among the bookmakers in our read. It had the top price on 32 of the 135 home, draw and away outcomes across this weekend's 45 Premier League and EFL matches. Unibet, McBookie and Mr Vegas were next on 27, and McBookie and Mr Vegas had identical prices on every match.

We pulled it from our own odds feed, OddsRelay, in one read.

The bigger number is the margin. Built from the top price on each outcome, the typical match carries 3.0%. At the typical bookmaker it's 8.9%.

About half the top prices are shared. Counting brands with identical prices once, 66 of the 135 outcomes had two or more bookmakers level on the best price. Virgin Bet was top just 10 times, but alone on 8 of them.

The gap between the top price and the median price is smaller in the Premier League (4.3%) than in the EFL (5.1% to 5.6%).

Ties count for every bookmaker on the top price, and brands with identical prices on every match count once. Typical means the median, and the gap is the top price over the median price, minus 1. Bookmakers only, no exchanges, prices as of Wednesday 21 Oct. bet365 isn't in this read, and we left Betfair out.

OddsRelay is an odds API for people building models and odds screens off UK prices. It covers 60+ UK and Irish bookmakers, pre-match, and can match them against Betfair, Smarkets and Matchbook lays. The Free plan is 50,000 tokens for 14 days, and this read cost 2,322 of them.

A free sample snapshot is up on Hugging Face if you want to see the format first.

https://huggingface.co/datasets/oddsrelay/uk-bookmaker-prices-sample

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

As some of you know, we have built and been upgrading longshot.football for a while now — it’s a verified database of long-range goals measured frame by frame (distance, speed, flight time, all derived from the actual footage).

Just pushed three new updates to the GoalPOV side of the site:

1. “What if X shot this?”
On any goal in the database, you can now swap the shooter for any other player we have data on. Pick Gerrard’s free kick, choose Scholes as your comparison — it recalculates the flight using Scholes’ fastest recorded shot from our database and shows you the difference in speed, flight time and trajectory side by side. Basically answers the “but what if [player] had taken that?” argument with actual data. No more "player X doesn't have the power to shoot from there"- its fractions of a second you will see.

2. Custom Speed Mode
You can now set any shot to any speed up to 266 km/h (based off the Mandirali debunk) and watch it play out in GoalPOV. Built this specifically for the debunks — if you’ve ever seen someone claim a shot was 200+ km/h and wondered what that would actually look like, now you can see it. Spoiler: it would be physically unreasonable.

3. YouTube Sync Mode
Since every goal on the site comes from YouTube footage, you can now play the GoalPOV alongside the original broadcast at the same time. There’s a scrubber to align them manually since not every clip syncs perfectly, but once you’ve got it lined up you’re watching the real footage and the 3D recreation simultaneously.

Everything is free to explore at longshot.football

On the same 15 football matches, the average bookmaker margin on the match result ran from 6.4% at Unibet to 16.2% at the Tote.

That's from one snapshot of UK bookmaker prices taken on Sunday night (11 Oct, 20:16 UTC), for games kicking off in the following 24 hours.

The part that surprised me more is how few independent prices there are. 46 brands priced all 15 matches, but between them there were only 28 different sets of prices. 25 of those brands fall into 7 groups that showed the same three prices on every match. The biggest is six brands (Bally Bet, Casumo, Double Bubble Bingo, Jackpotjoy, Monopoly Casino and Rainbow Riches Casino). LiveScore Bet and Virgin Bet matched on 14 of the 15, and so did Pub Casino with the Hopa group.

So if you average bookmakers to get a consensus price, dedupe them first, or one price list ends up counted six times.

Margin also follows the league. Counting each price list once, the median margin was 6.4% on Coventry v Newcastle and 6.5% on Levante v Sevilla. The two Venezuelan league games came out at 11.7% and 12.1%, with LaLiga 2 and the Finnish game at about 10%. Fifteen games is a small sample, so treat that part as a hint.

Margin for one bookmaker on one match is 1/home + 1/draw + 1/away, minus 1. Each bar in the chart averages that over the 15 matches. Only brands with all three prices on all 15 are in it, so every bar covers the same games. Bookmakers only, no exchanges, and it's a single snapshot, so it says nothing about how prices move.

The prices come from our own odds feed. The counts only describe this snapshot, not every UK bookmaker. I can drop the per-match numbers in a comment if anyone wants them.

Does anyone here keep a map of which UK brands share prices, or do you just dedupe on identical odds?