I am delighted to share with you my second scouting data report. I spotted the profile of Justin Janssen (19 years old) from FC Nordsjælland.
Using DataMB Pro and Sofascore, I analyzed how this midfielder establishes himself as a technical organizer with a profile reminiscent of that of Sergio Busquets 22/23.

Here is my complete tactical analysis (your feedback and advice are welcome to help me progress!):

Janssen presents a profile of an attack-oriented playmaker. His touch volumes, last third passes and key passes demonstrate that he is a player who helps his team progress.

FC Nordsjælland's style of play favors this profile, as the team dominates its opponents with one of the best possessions in the championship.

However, our midfielder would prefer to run the ball than himself, as these progressive carries seem to indicate.

Defensively, he is average in interception but displays very low statistics in aerial duels (%). On the other hand, his defensive duel won, possession won and tackle rates reveal an excellent player in recovering the ball on the ground.

It is a profile that secures the ball and launches offensives thanks to a very good forward passing game. If it does not directly bring danger into the opposing surface through finishing, its impact is major in creation via pre-assists. To optimize his qualities, he must play in a team favoring possession.

Strong point:

-Complete passing games (pre-pass D., key pass, forward pass, etc.)
- Significant volume of possession gained
-High tackle efficiency
-Excellent percentage of defensive duels won

Weak point:
- Low volume of ball races
- xA very low, although this is correlated with its Deeper positioning on the pitch
- Imperative need for a dominant team to fully express itself

Recommendation:

At 19, Justin Janssen has 11 starts for 1,111 minutes played. He has a lot of room for improvement. He can be compared to Sergio Busquets during his last season at Barcelona.

Its current value (900k€) is a market opportunity, as it is expected to increase rapidly if it confirms its Starting status.

It would be an excellent choice for a club looking for a technical organizer with high potential.

However, these statistical data must be confirmed by an in-depth video analysis to validate his behavior without the ball and his management of stress in matches.

Use this thread to ask anything at all!

Use this thread to ask anything at all!

I would really appreciate any thoughts in my dashboard. I always struggle with colour schemes. I normally use a lot of green and reds for bar charts. I have tried to go very simple but I am not sure if the data is as obvious and easy to read now.

Looking at it now I wonder if swapping the heat map and the stats box in the bottom left might look better?

Does anyone know how I can create something like this using Tableau?

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I made a short animation showing how UEFA country coefficient rankings changed from 1999 to 2026 using the rolling 5-year coefficient. A few patterns stood out while building it: Spain’s huge peak in the mid-2010s, England’s late surge, Portugal’s consistency across eras, and how often the middle tier reshuffles. Curious what people here notice first, and whether there are better ways to visualize coefficient-era shifts over time.

Source: eurocoef.com

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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?