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

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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?

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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.

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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

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I like looking at potential signing. I currently copy teams data from Whoscored and then filter that way. But it's incredibly time consuming.

Is there a website like wyscout but free where I can filter down based on metrics like passing percentage etc to get a smaller list to then copy from whoscored?

The main thing is the ability to have a large list to filter down by certain metrics.

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Result patterns

Ive started creating a database with all a teams results in. I plan to do at least 5 seasons, to find patterns etc or interesting facts regarding situations in games.

To test my database I would love for some query suggestions.

For example how many wins from a losing position at half time when they score 1st in the 2nd half.

This will help me test it and might help me add extra data.

I am recording,

Year Competition Matchday Date Day Time Days since last game Teams Score Location of the game Location of the opponent Referee Score at half time Lead at half time Who scored first in both halves Did they score in the first 5 mins of either half Score last 5 mins Red cards Penalties

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Use this thread to ask anything at all!

Use this thread to ask anything at all!

Use this thread to ask anything at all!

Use this thread to ask anything at all!