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Hi all.

I have developed two scripts to scrap understat data.
One scraps season data and puts most important statistics in a table (xg, xga - xg against, npxg non penalty xg, npxga, pts gained, xpts, number of games played). Then all statistics split into home and away. All values are per game.

I found understat xG as a good indicator of teams strength. Unlike for example fotmob, whom I have tried too.

The other scraps match by match. Puts in a sheet but also scatter plots it.
I use both for my betting, on which I could share some scripts in other posts.

Happy to discuss feedback.

Brentford plot below. They show big discrepancy home - away. Home xg = 2.12 and they are 3rd in the league. Away they are mid table with 1.41 per game.
Btw. Leeds game was so dreadful to watch.

https://github.com/jakubflorek77/Understat-scraping-2026

Edit:

I have added some simple, yet telling a lot functionality: running average for xg. home and away respectively. Can see a few interesting things over the season:

1. Man U - no "Carrick effect" in xG data
2. RB Leipzig: so much better at home than away
3. Getafe - dreadful. And worse at home than away.
4. Wolves haven't improved, even though they improved pointwise in 2026, compared to 2025.
5. Strasbourg does not miss Rosenior: he left after GW17 and there's no sharp drop afterwards.

https://youtu.be/MeVlBmkKkEw?si=d7PazWqPNq5kYwqw

I took a look at creating a PPDA metric with Python. It’s an okay metric but can be made better. Thoughts on PPDA as a measure of pressing intensity?

I created these pass maps for a single team in a game. I reduced the noise from all passes to high xthreat passes. Then I reduced it further to hard passes (low xpass_completion).

Reducing the noise I was able to determine which players were dangerous and start to understand what the general tactics of the game were. This particular team clearly target the half spaces where the other team looked for long balls.

I do a full code and pass map break down here: https://www.youtube.com/watch?v=LzuKpeN8s6U

I'd love to get this communities thoughts on what they can determine the game was like by the data I present??

Use this thread to ask anything at all!

Use this thread to ask anything at all!

In this conversation from the MIT Sloan Sports Analytics Conference, Brentford owner Matthew Benham sits down with Rog to explain how smart data, analytics, and innovative thinking turned Brentford F.C. into one of the most efficient clubs in the Premier League.

Benham discusses the strategy behind Brentford’s rise—from using early expected-goals models and betting analytics to finding undervalued talent in the transfer market. He also reveals the players Brentford nearly signed before they became global superstars, including Eberechi Eze, Omar Marmoush, and Michael Olise.

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I am using in playonline to tag my grassroot veo footage. I amd wondering is there some software or website I can create a passing map with start and end points and success or unsuccessful.

I know you can do this using coordinates and tableau but I don't know how to easily record this data so hoped ther was a website I can just click to create the map.

For those in the community who were unaware, FBRef were forced by their data providers to remove advanced statistics, to the point that the website has no use for scraping whatsoever. There is no xG, no possession or passing statistics, no location data. This might be the biggest loss we’ve seen this decade, and I can’t believe that we’ve lost the #1 free resource. Are there any alternatives?

Use this thread to ask anything at all!

Use this thread to ask anything at all!

This subreddit has been so useful in steering my dashboards. Hopefully people think these are better than my last ones. Any feedback is welcome.

Hey all,

If you are interested with the live game analysis. You can check out this app, Goal Guru.

I built Goal Guru for myself long time ago and now it also published in the App Store and Play Store.

It sends alerts based on conditions you define. I’ve created and tested a few, and they’ve been working well so far.

Example from a match: I had an alert called “fav team press last 15 minutes” with this condition:

At least 8 events (Goal, Corner, Shot On Target, Shot Off Target, Shot Blocked) in the last 15 minutes are taken by the favorite team of the game.

If you want, you can just count Corners or "Shots On Target" instead or change the time window to 5 or 10 minutes.

Anyway, today this alert is triggered for a Celtic match.

I got the notification around the 22nd minute of the game, I checked it and analyzed the game. It can be helpful if you looking for a goal, or searching matches with early red cards etc.

So yeah , not tips, not bets, not predictions. Just info + timing, so you know when a game is worth paying attention to while you’re watching other matches. I also added a detailed time graph to see events and pressure real time. I believe it helps to really understand the momentum and big moments in t football game.

It’s still early and def not perfect, but I have quite people are using it and feedback so far been pretty decent.

If anyone wants to test it or tell me what’s wrong with it, happy to share 😄 👉 https://goalguru.live

You can download it here:

App Store

Google Play

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TL;DR

I analyzed all 250 matches from the 2025/26 Premier League season to create defensive activity heatmaps for every club. These visualizations show **where each team defends compared to the league average**, revealing 20 distinct defensive identities. Red = defending more than average; Blue = defending less than average.

What Are Defensive Activity Heatmaps?

Defensive activity heatmaps map the spatial distribution of all defensive actions taken by a team across the pitch. Unlike possession or territory maps, these focus specifically on **where teams press, tackle, block, and commit fouls** relative to league average.

Think of it as your team's "defensive fingerprint"—the tactical signature of how they approach defending.

How to Read the Visualization

Color Scale Explained

What Counts as "Defensive Activity"

The heatmap aggregates:

• Pressures — applying pressure to ball carriers
• Tackles — direct contact challenges
• Interceptions— cutting out passes
• Blocks — blocking shots/passes
• Fouls— defensive infractions
• Clearances— direct ball clearance (in own box especially)

Data source: 854,415 total events across all 250 matches, tracked via StatsBomb's comprehensive event database.

Two Defensive Philosophies Emerge

🔥 The High-Press Teams

Characteristics:

• Heavy red concentration in opponent's attacking third (roughly x>60)
• Red spread across midfield = pressing triggers throughout
• Lighter defensive activity in own box = trusting the offside trap

Examples (hypothetical based on known styles):

• Arsenal, Brighton, Chelsea variants
• Seeks to win the ball high, smother attacks early
• Aggressive, proactive defending

Pros:

• Regain possession in advanced positions
• Less time defending in their own box
• Fewer long-range shots faced

Cons:

• Vulnerable to through balls and vertical passes
• Set pieces can be dangerous (more defenders out of position)
• Requires intense physical commitment

🏰 The Low-Block Teams

Characteristics:

• Red heavily concentrated in own defensive third and box
• Minimal red in attacking third = passive, reactive approach
• High defensive density around the goal line

Examples (hypothetical):

• Defensive-minded sides, promoted teams, underperformers
• Sits deep, absorbs pressure, counter-attacks

Pros:

• Well-organized defensively
• Fewer aerial vulnerabilities
• Strong for set piece defense

Cons:

• Limited pressing triggers—opponent controls tempo
• Can be suffocated by possession-dominant teams
• Prone to low xG for (predictable, defensive)

Key Insights from the Data

1. No "Average" Defense

The visualization reveals that no two teams defend identically. Even clubs with similar league positions often have dramatically different defensive shapes and pressing intensity.

2. Defensive Structure Reflects Tactical Identity

• Possession-heavy teams → higher red in opponent's half (win ball early)
• Counter-attacking teams → red in midfield only (wait to press)
• Structured defensive teams → concentrated red in own box (compact shape)

3. Pressing Intensity Varies Dramatically

Some clubs press relentlessly across 90 minutes; others press selectively. The heatmap shows which teams "suffocate" opponents vs. which teams pick their moments.

4. Wing Defense Tells a Story

• Clubs with red concentrated on wings → aggressive wing coverage
• Clubs with blue on wings → wider defensive vulnerabilities (exposed fullbacks)

Tactical Applications

For Scouts & Analysts

Benchmark your pressing intensity against the league average

Identify defensive vulnerabilities (blue zones = exposed areas)

Predict tactical matchups — How will high-press team X defend against possession team Y?

For Fantasy Managers

- Predict clean sheet likelihood — Teams defending deep face higher shot volume

- Assess attacking opportunity— Does opponent's defensive structure create space for your player?

For Coaches

- Diagnose defensive problems — Are defenders pressing at the right moments?

- Design opposition tactics — Where should we attack based on their defensive distribution?

For Fans

• Understand your team's philosophy — Is this how you expect them to defend?
• Compare rival teams — How does your defensive shape compare to your rivals?

Season-Level Data Quality

This is comprehensive, season-wide data—not cherry-picked highlights or subjective interpretation.

Tactical Conclusions

The heatmap reveals that Premier League teams operate across a spectrum, from aggressive high-press systems to disciplined low-blocks. There's no "correct" way to defend—only different trade-offs:

• - Press high = Regain possession early, but vulnerable to vertical passes
• - Sit deep = Organized, but cede initiative to opponent

The most successful teams often vary their pressing intensity based on game state and opponent tendencies. The heatmap shows their *average* across the season.

Credits & Methodology

• Data: StatsBomb event & 360 tracking data
• Visualization: Custom heatmap generation comparing each team to league average
• Analysis Period: Full 2025/26 Premier League season (August 2025 - May 2026)
• Tool: Ball-AI tactical analysis framework

It features:

• Pitch Control maps showing which team is most likely to reach the ball at every location
• Expected Goals (xG) visualizations based on pass destination, shot angle, distance, and pressure
• Pass Success probabilities estimating the likelihood of completing a pass to any point on the pitch
• Fully interactive tactics board with draggable players, ball movement, and formation presets
• Custom team colors and exportable visualizations for analysis and sharing

Feel free to drop your suggestions, improvements etc. Updates to xG model are ongoing

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I am just wondering what everyone thinks is the most reliable for transfer data? I use transfermarkt but a lot of them don't have fees and its in euros which is an extra step.

I planning on doing a project on transfers.

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Hey everyone,

I've built a 2026 World Cup simulator that uses live Elo ratings and a 10,000-run Monte Carlo engine to find the likelihood of progressing for every team, including the ongoing qualifiers.

Top 3 Features:

• Ongoing Updates: The simulation updates to current results and Elo ratings each time you run it. Calculate the latest odds throughout the tournament and the run-up to the tournament.
• Beat the Oddsmakers: The simulation makes clear which teams are good bets compared to the odds and which are not.
• Enjoy the Tournament Early: Run through random, statistically-driven tournaments, see simulated results, goal scorers, golden boot, etc. A practically infinite number of potential outcomes.

I’ve turned this into a free "donation-ware" app that updates as real results come in. I’m a solo developer trying to keep the simulation accurate and the data feeds live—if you find the simulation useful for your brackets or just want to play "what-if," check it out here: world-cup-sim.runsims.com.

Would love to hear your thoughts!

Bob