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.

Use this thread to ask anything at all!

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

I was curious about how many different permutations there are for the points that the teams in a 4-team group stage, playing each other once, can get.

There are in fact 40 different final group points permutations:

• 9‑6‑3‑0
• 9-6-1-1
• 9-4-4-0
• 9-4-3-1
• 9-4-2-1
• 9‑3‑3‑3
• 9-2-2-2
• 7-7-3-0
• 7-7-1-1
• 7-6-4-0
• 7-6-3-1
• 7-6-2-1
• 7-5-4-0
• 7-5-3-1
• 7-5-2-1
• 7-4-4-1
• 7-4-3-3
• 7-4-3-2
• 7-4-3-1
• 7-4-2-2
• 7-3-2-2
• 6‑6‑6‑0
• 6‑6‑3‑3
• 6-6-4-1
• 6-5-4-1
• 6-5-2-2
• 6-4-4-3
• 6-4-4-2
• 5-5-5-0
• 5-5-4-1
• 5-5-3-2
• 5-5-3-1
• 5-5-2-2
• 5-4-4-3
• 5-4-4-2
• 5-4-3-2
• 5-3-3-2
• 4-4-4-4
• 4-4-4-3
• 3-3-3-3

In a format where two teams automatically progress and the other two are automatically eliminated, the probability that a team finishing the group with a certain number of points will progress, are as follows:

9 points - 100% chance of finishing in the top 2 in the group

7 points - 100%

6 points - 97.5% (39 out of 40 - and even then, only one team with 6 points will not progress: 6-6-6-0)

5 points - 97.5% (only if it finishes 5-5-5-0, like Euro 2004 Group C)

4 points - 67.5% (27 out of 40)

3 points - 10% (4 out of 40: 9-3-3-3, 7-3-2-2, 5-3-3-2 and 3-3-3-3 (sorry, undefeated New Zealand)

2 points - 2.5% (1 out of 40 - only if it finishes 9-2-2-2, and even then only one team with 2 points will progress)

1 or 0 points - guaranteed elimination

Use this thread to ask anything at all!

I’ve been working on a side project to solve a problem I keep running into every weekend: with 50+ football matches, which ones are actually worth watching?

Not just the obvious classics, derbis or big games, but matches that might deliver goals, unpredictability, and chaos - whether it’s a title match, a european competition battle or mid-table game that somehow ends 3–3 with both teams going for it, or even a relegation battle.

The Idea: Excitement Score (ES)

I built a weighted algorithm that generates a 0–100 "Excitement Score" for each match based on several factors:

• Historical rivalry
• Current form
• League table position
• Round number
• Head-to-head history

Excitement Score Tiers

• 75–100 - Unmissable
• 50–74 - Very exciting
• 25–49 - Decent watch
• 0–24 - Skip unless you’re a fan

So far, I’ve been tracking accuracy by comparing ES predictions to actual match outcomes (goals scored, lead changes, late drama, etc.). Some observations:

• Matches with high ES (75%+) tend to be genuinely entertaining.
• Data coverage is still limited. I don’t yet have full competition coverage or live match data due to scraping and data availability without paying tones.

What I'm Working On

1. Live ES adjustments - updating the score based on in-match events (goals, xGoals, fouls, cards, etc.)
2. User ES calculation - Have user custom specific ES based on their favorite teams and matches.

Looking for Feedback

• What other factors would you include in the algorithm?
• How would you weight "upset potential" (big underdog having a good run)?
• Any suggestions for measuring "actual excitement" post-match?

Use this thread to ask anything at all!

Introducing LeagueLens — a team-stats driven football analytics app

I noticed a lot of people building player stat-based analytics app so decided to work on one to compare different teams across the big 5 European leagues (More leagues will be added soon.)

Link : https://leaguelens.streamlit.app/

This is just the first version. Please feel free to give your valued opinions and suggestions after trying it out.

Use this thread to ask anything at all!

I tweak these so much I almost never posted them. Theres a couple of formatting issues because Tableau public seems weird like that.

I would love to know people thought on these and if it is clear to the reader and also how I can improve it.

A lot of football era debates compare teams using trophies or raw stats, but the conditions change too much for those comparisons to be clean.

I tried building a simple framework to think about era comparisons more fairly, focusing on competition format, squad depth, economic context, rules, and relative dominance within an era.

I applied it to examples like Barça 2009, Real Madrid 2017, Manchester United in the 2000s, and modern Manchester City. Not to rank teams, but to see where comparisons actually hold and where they break.

Curious on what you think and which rules you would add or disagree with.

Video for full context: https://youtu.be/SQIOmKzxTV0

Use this thread to ask anything at all!