🇸🇮 Benjamin Šeško

📉 Numbers
📈 Hype

What gives?

Look beyond goals and xG and you will find a sleeping juggernaut.

🧵📊 A detailed data thread.
Overview

The idea behind this came from the radar chart in the first post of this thread; his numbers declined DRASTICALLY this season.

I set out to find out: did he hit a bit of a wall in his second Bundesliga season, or were there underlying reasons for his numbers to dip?

Plus, he's a pretty big domino in the CF landscape I haven't analyzed directly, so it felt like a good time take the plunge.

In this thread, I'll be generally comparing him to one of two groups:

- Broad sweeping peers: The 92 U21 forwards in T5 leagues w/ 900+ minutes in either of the past two seasons
- Bundesliga peers: The eight U24 Bundesliga center forwards w/ 7.0+ npxG this season (you'll see why this grouping in a bit)

To start though, I asked my followers what they thought of his performances this season relative to last. The results of the poll are below.

Before researching, I would've said regressed, but after the fact, I think the opposite 🪝

Let's get into it!

📸📊 The GK Landscape

🧵 Mega thread. Thread Overview/Methodology

The GK market is quietly one of the most fascinating to follow this summer, and I don't put out much content on the position, so I feel this is long overdue.

Straight away: TOP RIGHT DOES NOT MEAN BEST.

No quadrant is better than the others... the players are grouped based on data similarity, not quality of numbers.

The GKs in the study were all keepers with a 5m+ Transfermarkt value at time of writing, that had meaningful advanced data.

To group the GKs, I ran a PCA analysis on their cumulative numbers from '21/22 - '24/25 in the following categories:

- Shot Stopping: PSxG-GA/Shot; Save %
- Distribution: Pass Value Efficiency; Launched Pass Completion %
- Cross Stopping: Cross Stop %
- Sweeping: Actions Outside the Penalty Area p90

The below is the result, where all keepers fell into one of the four FIFA chem style themed clusters (with the general theme of the grouping... not necessarily an exact description of each profile):

- Cat: shot and cross stoppers
- Wall: cross stopping and sweeping
- Glove: balanced
- Shield: distribution and sweeping

Let's get into some of my thoughts on the whole of the graph.

G/xG over-performance: repeatable or isolated?

Re: Matheus Cunha and Bryan Mbeumo

🧵 Thread.

https://twitter.com/joaomopborba/status/1929641822718218475

To set the stage, I put out what was honestly a throwaway tweet from a quick idea I had while walking my dog about how Bryan Mbeumo and Matheus Cunha were the two highest G/xG out-performers in the PL this season. My feelings on the matter remain unchanged:

- Good players, good fits for Amorim's system, have value beyond a high goal tally.
- Their price is inflated because they outperformed xG... if they had 16 npG+A seasons like their xG+xAG suggests was fair, rather than the 21 and 22 they achieved, I think both the perceptions of the moves + the fees paid are lower. Textbook sell highs by Wolves and Brentford, but also not sure what else United could do if they wanted to the prime-aged forward market.

One of the replies got me thinking: how do players perform outside of spike G/xG seasons?

🇩🇪 Florian Wirtz

"Paying for Game Breaking"

🧵📊 A detailed data thread. Thread Overview

Florian Wirtz is a super footballer. Is he worth the (alleged) €150m price tag? I'm still not 100% sure, but he does have several traits that make him uniquely dynamic and "game breaking".

In this thread, I'll be analyzing his data compared to the 117 other u23 advanced midfielders/wingers with 900+ minutes in T5 leagues this season.

Let's go!

🇳🇱 Jeremie Frimpong: Arne Slot's New Rook ♟️🐦‍⬛

🧵 A detailed data thread.

https://twitter.com/David_Ornstein/status/1923785635606732833

This thread contains the highlights, but if you're interested, I wrote 1,500 words on the subject in the below article.

medium.com/@fc_mossman/je…

🇬🇷 Konstantinos Karetsas

🧵📊 A data scouting thread. Thread Overview

I wanted to put this out yesterday, but the Spanish gridlock had other plans.

There are tons of talented young attackers (specifically wingers and AMs) in non-T5 leagues that I don't know enough about, so I'm going to be doing a data deep dive on the ones that interest me most, comparing Opta data, where available, to the Opta data from FBRef for T5 leagues over the past two seasons.

I have a few names in mind I would like to analyze over the next couple of weeks, but if there's a player you'd like to see from a non-T5 league, reply with their name (provided they're in a large enough league for data collection).

I will be sticking to attacking midfielders and wingers, with the a selection of 10 data points to analyze ability, tendencies, archetype comps, and level of impact should they make a move to a major league.

All metrics, unless otherwise noted, are adjusted for league strength and possession, so this will offer a truer picture of where the player would translate in comparison to the numbers generated by players in stronger leagues.

In effect, the adjusted values should be looked at for what the player is now, and the unadjusted for what they could be if they reach their potential.

"Le mieux est l'ennemi du bien" - I know this is not the most comprehensive approach, and you should always watch the player to contextualize the data, but this analysis should provide an excellent baseline for the archetype + potential impact level of the player.

Let's get into it!

🇦🇷 Claudio Echeverri

🧵📊 A data scouting thread. Thread Overview

There are tons of talented young attackers (specifically wingers and AMs) in non-T5 leagues that I don't know enough about, so I'm going to be doing a data deep dive on the ones that interest me most, comparing Opta data, where available, to the Opta data from FBRef for T5 leagues over the past two seasons.

I have a few names in mind I would like to analyze over the next couple of weeks, but if there's a player you'd like to see from a non-T5 league, reply with their name (provided they're in a large enough league for data collection).

I will be sticking to attacking midfielders and wingers, with the a selection of 10 data points to analyze ability, tendencies, archetype comps, and level of impact should they make a move to a major league.

All metrics, unless otherwise noted, are adjusted for league strength and possession, so this will offer a truer picture of where the player would translate in comparison to the numbers generated by players in stronger leagues.

In effect, the adjusted values should be looked at for what the player is now, and the unadjusted for what they could be if they reach their potential.

"Le mieux est l'ennemi du bien" - I know this is not the most comprehensive approach, and you should always watch the player to contextualize the data, but this analysis should provide an excellent baseline for the archetype + potential impact level of the player.

Let's get into it!

🇦🇷 Franco Mastantuono

🧵📊 A data scouting thread. Thread Overview

There are tons of talented young attackers (specifically wingers and AMs) in non-T5 leagues that I don't know enough about, so I'm going to be doing a data deep dive on the ones that interest me most, comparing Opta data, where available, to the Opta data from FBRef for T5 leagues over the past two seasons.

I have a few names in mind I would like to analyze over the next couple of weeks, but if there's a player you'd like to see from a non-T5 league, reply with their name (provided they're in a large enough league for data collection).

I will be sticking to attacking midfielders and wingers, with the a selection of 10 data points to analyze ability, tendencies, archetype comps, and level of impact should they make a move to a major league.

All metrics, unless otherwise noted, are adjusted for league strength and possession, so this will offer a truer picture of where the player would translate in comparison to the numbers generated by players in stronger leagues.

In effect, the adjusted values should be looked at for what the player is now, and the unadjusted for what they could be if they reach their potential.

"Le mieux est l'ennemi du bien" - I know this is not the most comprehensive approach, and you should always watch the player to contextualize the data, but this analysis should provide an excellent baseline for the archetype + potential impact level of the player.

Let's get into it!

📸📊 The 2025 Center Forward Landscape

🧵 A detailed data thread Methodology

I set out to make a "striker transfer market primer" of sorts.

The forwards included in the project are all strikers and second strikers that, according to Transfermarkt, have a current value of €15m+, are 27 or younger, and have a league season with at least 800 minutes in the past two seasons with available advanced data.

The aim was to bucket the forwards into categories based on similar profiles, including forwards that could likely be on the move for a large fee (the reason for the value and age constraint).

Yes, that means it includes players that will not be moving this summer like Haaland and Mbappé, but they are there for reference on other's positions. The majority of the analysis will be done on those that could be on the move this summer.

If you're interested in the details of how I bucketed the players, read on. If you just want to see the player analysis, jump to the next post.

Once I refined the list, I built scores for "abilities", which were weighted z-scores for the percentile rank of the forward's values for each ability from the most recent qualifying season. They were the below:

- Box Domination: equal parts npxG p90, team share of npxG, shots p90, team share of shots, attacking penalty area touches p90, team share of attacking penalty area touches
- Conversion: npG/npxG (for all league shots dating back to '21/22)
- Chance Creation: equal parts xA p90 and chances created p90
- Carrying: 3x Take Ons p90, 1x Take On %
- Pressing: 2x Tackles p90, 1x Recoveries p90
- Aerial: 3x Aerial Duels p90, 1x Aerial Duel %

I would have loved to included things like shot creating actions from take ons, or team share of the other advanced metrics, but some of the non-T5 leagues required heavy data cleaning from non-FBRef sources (primarily FotMob, since they and FBRef both use Opta data), and I am time constrained like everyone else.

Next, I took the Opta "Strongest Leagues" data to create a multiplier so the data collected in better leagues was weighted more favorably... it ultimately just lead to reductions for the lower-level leagues, not so much boosting the good ones.

From there, I ran a PCA to bucket the data based off of those six scores, and the below emerged.

Do not read into this fully as "top right is best, bottom left is worst", as this is a profiling exercise. That said, in looking at the graph, to me, it seems "Physical Disruptors" are the in-training version of the "Target Man"; same for "Second Striker" to "Modern CF".

Also, not every striker's grouping will match your perception of them perfectly. Some surprised me, like Darwin Núñez being a "second striker". While there is a heavy element of 'how the striker is used' that determines their grouping, not just their raw abilities, keep an open mind for ones you think don't make sense, and consider why that player's data so perfectly matches others in that grouping.

Finally, if this style of visualization looks familiar, it is because a good chunk of this was inspired by how @FootballEcon_ presents their data. I didn't set out to copy, but I would be remiss to not credit their work as an influence in how the graph was structured after I had the idea of what I wanted to do.

Let's get into the analysis of the groupings.

📸📊 Attacking Penalty Area Dominance: "How" vs. "How Much"

🧵 A detailed data thread. Thread Overview

The graph is like 75% stylistic grouping and 25% ability indication, so don't automatically look to the top right and assume the best players should be there.

- X axis: how many of a player's attacking penalty area touches are initiated by them with a dribble (left = primarily receive in the penalty area, right = primarily carry into it).

- Y axis: % of team's attacking penalty area touches (APAT) they see (prorated to p90).

The X axis is purely stylistic, and the Y axis is kind of an ability indication as dominating your team's APAT is mostly "skill" in my opinion. However, the more forward a player is positioned, the higher their % will naturally be, so view this as a skill for CFs and advanced wingers, but don't penalize attacking midfielders that are lower on the graph.

This was purely born from the idea that I wanted to visualize player behavior around the box... nothing complex.

If you don't see someone you're looking for, they're probably buried in a cluster, don't play far enough forward, or didn't have 900+ league minutes heading into the most recent international break.

Let's go through the groupings.

(2/7)

📊 Identifying the next great DLPs

🧵 A detailed data thread. Thread Overview

"Deep Lying Playmakers" will probably never go extinct, but I believe they're on their way to being endangered at the top levels of the game.

In an ecosystem where athleticism continues to be prioritized over technical ability, the place for a tempo-dictator and holding creator is becoming ever increasingly a 'luxury' item in tactical plans.

Or, at the very least, if these skills are still coveted by teams, they come secondary to other traits.

However, even if the exercise is trivial, I wanted to identify midfielders that fit the traditional definition of a DLP - sit deep, progress, and initiate attacking sequences with their passing.

To do this, I looked back at some of the best DLPs of recent times... Toni Kroos, Sergio Busquets, Joshua Kimmich, Frenkie De Jong, Manuel Locatelli, among others.

All of them dominate progressive passing within their respective teams, and share some combination of a high number of live pass shot creating actions, through balls, or switches.

So, I plotted "team share of progressive passing distance" along the x-axis, and "possession adjusted live pass shot creating actions+ through balls + switches p90" on the y-axis.

I also made cut offs of 900+ T5 league minutes through the most recent international break, midfielders 25 and younger, and those w/ a vertical positioning of <= 5 (see below image).

More info on 'vertical positioning' can be found in the article in my pinned tweet, but effectively it is how far ahead or behind the team's average depth of touch that a player sees theirs. I picked the mostly arbitrary cut off of 5 yards for this to ensure we maintained integrity in the "deep-lying" component of DLP.

Let's analyze each of the six groupings from the original graph.

Replacing Trent Alexander-Arnold

📊🧵 A detailed data thread.

#LFC Thread Overview

I am doing this one a bit differently.

I wrote 5,000 words on the subject in the below Medium article, which I would encourage you to read if you want the full picture of the "why" behind my methodology and player profiles.

Instead of putting all of that here, I will highlight some of the most interesting bits, including the "ideal archetype", top targets, and general relevant thoughts.

(2/17)

medium.com/@fc_mossman/re…

One of the best graphics to hit the timeline in awhile.

🧵 A quick observation for each team

https://twitter.com/Statsbomb/status/1902296761689080058

- Bournemouth: super reliant on the Kerkez/Semenyo combination, also most commonly pushing forward.
- Arsenal: all roads lead to Bukayo Saka.
- Villa: play most commonly into the spaces Tielemans occupies, as well as Asensio when he's playing centrally.
- Brentford: passing back to the GK that often is pretty gross.
- Brighton: long switches from van Hecke to the left stand out - more side to side than I would've guessed.

📸📊 U23 T5 Midfielders: "Build Up" vs. "Ball Winning"

🧵 A detailed data thread. Thread Overview

Sometimes data analysis is archetype matching, sometimes it is deciphering ability.

This thread is meant to be a bit of both.

"Build Up Score" is an efficiency ratio of progressive pass and carry distance to touches and turnovers. It is normalized to depth of touch, as midfielders that play deeper have a higher raw value, which makes sense considering they operate in more space.

"Ball Winning" is tackles won, dribbler challenges won, and interceptions prorated to p90 and adjusted for team possession.

There is value in observing both how close to the apex of the two axes the midfielder scores, as well as the generally grouping of players they fall into.

I've broken them down into six "notable" groups, which I will discuss in more depth.

(2/9)

A simple matrix for classifying the final phase tendencies of attackers using Xavi Simons, Jamal Musiala, and Florian Wirtz to illustrate.

🧵📊 Quick thread. How attacking midfielders and wingers approach the final phase of play can be broken down into two binary approaches:

- Shoot first or look to create for others?
- When creating, off the dribble or with a pass?

It can really be that simple.

What the numbers say about Nico González

🧵📊 A detailed data thread.

#MCFC Thread Overview

Man City had to an extremely tight needle to thread when signing a new CM...

Needs to be able to play as both a 6 and 8.

Needs to be similar enough to Rodri to alleviate his absence, but not too similar to where they can't play together when he returns.

Needs to be young for the future as the team transitions to a v2 under Pep, but needs to be experienced enough to step right in.

Can't just be a profile fit of the above - needs to also be UCL quality now.

All of this time-boxed into a one month period where they had other business to take care of.

And I think they just about did it with Nico González.

To evaluate the move, I'll be looking at all midfielders in T7 leagues w/ 900+ minutes over the span of the beginning of the '23/24 season up to January 1st, 2025.

This thread contains 98% data analysis and 2% player scouting as the former is my lane... observation of the player's actual play is important and also plentiful on this app.

(2/17)

#MCFC

Jorrel Hato: what the data says

🧵📊 A detailed thread. Thread Overview

I'll be taking a detailed look at Hato's numbers over the past two seasons in the Eredivisie to understand better the profile of player he is, and the potential he possesses.

Of course on the surface, comparing a 17/18 year old's numbers to fully grown men will never flatter the teenager, so part of the thread will dovetail into a string I wanted pull about how to balance performance vs. age/experience.

The sample set for the project is all defenders in the T5, NED1, POR1, and ENG2 leagues with 900+ combined minutes since the start of the '23/24 season (n=1103), or U23 defenders in the same leagues (n=256) to get an understanding of how Hato rates compared to his peers.

There is so much additional context needed to process the data (age, league strength, Hato's flexibility to play both LB/CB and how those roles carry different responsibilities that shape the data, + much more)...

Hopefully this thread will serve as an exploration in equal parts 1) into Jorrel Hato's profile/potential; 2) how I apply context to data.

(2/11)

🇮🇹 Andrea Cambiaso would be a BRILLIANT signing for City.

📊🧵 Quick data scouting thread.

#MCFC #ManCity Andrea Cambiaso: Strengths in Possession

Among many other qualities, Cambiaso is already an elite level ball carrier and extremely accurate long passer.

This is highlighted by his status as the leader among Serie A full backs this season in progressive carry distance/90 and medium+long pass accuracy.

Cambiaso has the ability to be a driver on both the wing and when he tucks into midfield, which will help ease the future "carrying" burden that is inevitable with KDB's impending departure.

He also has the capacity to switch the ball and rotate play from deep.

(2/6)

#MCFC #ManCity

Everything the numbers say about Stefanos Tzimas 🇬🇷

📊🧵 A Detailed Thread.

#LFC Thread Overview

I'll be comparing Tzimas' underlying metrics in his first 14 league matches (925 minutes) for Nürnberg to those of all forwards in Europe's T5 Leagues over the past two campaigns with 900+ league minutes in an individual season.

Some caveats to get out of the way:

- 925 minutes is barely a large enough sample size to make assumptions, but we work with what we have.
- There is value in both watching the player and analyzing the numbers, but I would be veering out of my lane to do the former.
- The marks he achieves in the German second division are not guaranteed to be achieved at higher competition levels. I juxtaposed his numbers to the best competitions in Europe so that you and I could gain a frame of reference for the type of player he is stylistically, and what he could turn into.

(2/10)

#LFC

‼️Quick thoughts on the Trent Alexander-Arnold situation

1. Silence from the player doesn’t mean anything - why would he tip his hand in a contract negotiation?

2. #RealMadrid’s “bid” is a stunt - they know #LFC won’t accept, but they need to show Trent they’re serious.

(1/5) 3. Trent is perfectly within his rights to explore his options with his contract dwindling down… frankly the fact we are in this position (same w/ Salah and VVD) is a poor showing from the Liverpool front office.

(2/5)

How Manchester City should replace Rodri.

📊🧵 A Detailed Thread

#MCFC #ManCity Thread Overview

In the words of Billy Beane (if he was City's sporting director):

"There’s no replacing Rodri. We have to re-create him, re-create him in the aggregate."

To do this, I'll be recruiting the help of analytics to identify five targets City could pursue in order to 're-create' the traits they are missing the most in Rodri's absence.

There are two "smash" targets, two "realistic" targets, and one "sleeper" target.

I will often reference my Action Impact Model (AIM) - more information on it can be found in my pinned tweet.

Let's begin!

(2/19)

#MCFC #ManCity