Football Data | Administrator: @abelasano
Joined January 2025
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efStats retweeted
Getting free doesn't get you the ball. At least not at the 2022 World Cup.
I built a man-marking model from tracking data to find out:
napla-dev.github.io/2026/09/…
efStats retweeted
Football metrics are not equally repeatable.
A player can rank highly in one metric this season and be likely to do so again next year. In another metric, the same ranking may contain much more noise.
A new preprint analysed 8,207 player-season pairs across the Big Five to see which metrics persist from one season to the next.
For forwards:
Progressive carries: 0.733
Progressive passes: 0.709
xG: 0.664
Goals: 0.593
Assists: 0.428
Defensive volume was even less stable:
Interceptions: 0.476
Tackles won: 0.414
Blocks: 0.399
But stability is not the same as quality.
Pass completion was the most stable metric for midfielders and defenders (0.824), but that persistence can also reflect role, team style and context.
This matters when reading player profiles.
A radar gives every axis similar visual weight, while the underlying metrics may have very different levels of persistence.
For FBPlot, that raises an interesting question: can a player profile show not only what happened, but also how much confidence we should place in each part of it?
Thanks to Mohammad Arshan Shaikh for sending this over. I previously wrote about another of his papers on cross-league player projection.
Paper: Mohammad Arshan Shaikh, Year-to-Year Metric Stability in Elite Football: A Positional Analysis Across the Big Five European Leagues.
efStats retweeted
🟣INTRODUCING PLAYERPRINT
Every facet of a footballer. One app.
Top 5 Leagues | 23/24, 24/25, 25/26 | 4,396 players | 321 metrics
12 concepts on one player page:
Passing | Carrying | Shooting & Footedness | Aerial Duels | Hold-Up | Decision Making | Final Third | Half-Spaces | Tempo Control | Defending | Post-Recovery | Link-Up & Synergy
THE SCOUT. Build the player you're looking for.
Pick up to 10 metrics from any concept. Weight each 0-5 by how much you care. Get the top 50 ranked by Fit, with the percentile behind every column, so you see why he fits and not just that he does.
Nothing is pre-baked. Every percentile, rating and role is computed live against your pool: season, league, position, minimum 90s. Move the pool and the numbers move.
Maps you won't find anywhere else:
Angle Bias Index | Decision Bloom | half-space entries | injectors vs resets | post-carry take-ons | aerial duel quality | link-up synergy
Passes are comets. Carries are dotted. Every visual downloads at 300 DPI.
Link below!
efStats retweeted
Made some updates to the Teams app:
🔹 Added xG-Pi Rating (an improved Elo)
🔹 Upgraded models for xPts & match projections
🔹 “Hot” simulations (ratings evolve within each path)
🔹 UI improvements & minor tweaks
🔗 teamslab.streamlit.app
efStats retweeted
✅ This is the result of my data analysis on the specific play-styles teams employ in the PL this season! Plenty of interesting talking points up for discussion off this 🤔
If you are interested in my methodology for this study, check out the link in the follow-up tweet 👇
efStats retweeted
16-YEAR-OLD CAVAN SULLIVAN IS SPECIAL ✨
What a performance in Philadelphia Union’s 7-0 win in the CONCACAF Champions Cup 👏
90’ played
61 touches
2 goals
2 assists
3 chances created
4 shots
2 tackles
1 interception
9 recoveries
3 duels won
You love to see it 🇺🇸
efStats retweeted
Missed the green horizontal bars? I’ve added 'scouting reports' for each player on markstats.club. Click the small icon next to a player’s name to view them. Everything is still free
efStats retweeted
📊🇯🇵 Keisuke Goto, data report
+ physical specimen, the kind that is trending to be prepared for the rigors of the Premier League; 6'3" (191 cm), and attempts/wins aerials at a fantastic rate for his age
+ high defensive work rate
+ used primarily (an is quite proficient) as a "f it, Keisuke down there somewhere" long ball outlet
+ finds insanely high quality shots despite 'limited service' in the form of shot/attacking penalty area touch volume
- despite electric shot quality, his shot on target rate this season has been relatively abysmal, also worth noting he's managed just 6.71 xGOT on 10.34 xG from 40 shots this season... just far too early to make any assertions on his finishing ability long term, but something to monitor
- definitely more of a poacher at this point (or at least used as one) considering shots per danger area touch, low xA and general creative output
➡️ Filter by aerial duels and npxG considering age and he'll be at the top of most lists. To be fully transparent, I haven't watched a second of him this season, so this is purely a data read... if I'm wrong about things or further context is needed, please let me know. The recent reports make sense that many mid-level PL clubs are tracking him... everyone wants power forwards for strikers, and w/ the recent data point of Igor Thiago probably over-indexed in decision maker's minds, he'll be an appealing prospect to keep on shortlists for the coming year or so.
efStats retweeted
Adam Wharton attempts a progressive pass on 23% of his total attempts, by far the highest rate of any midfielder in the Premier League.
The synthesis of the player's aggression and the manager's pursuit of verticality.
No wonder Palace has easily the highest direct speed.
efStats retweeted
🚨Introducing Tactical Power Ratings (TPR)
Measures any manager containing Opta-era data using two sophisticated metrics;
• Shot Domination % = (Shots For / Shots For + Shots Against) * 100. Weighed based on game-states to eliminate desperate attempts or the leading team 'backing off' when maintaining a comfortable lead (100% if the score is within 2-goal deficit, 45% for =3, 15% for >=4). Benefits teams that can kill the game off early and removes stat-padding. The number is then normalized against the league average of the specific season as well as the strength of the opponent, ensuring it is NOT due to the strength of the players, but by how much the manager tactically outperforms his opponent relative to their strength. For instance, the league average against Burnley this season for Raw Shot Share is ≈ 67.8%, and in GW12 Chelsea came up against them under Maresca only managing to bolster a 65.2% raw shot share, meaning his team underperformed by -2.6% to the rest of the league (normalizing to league), and given Maresca's average career shot dominance measures at +5.3%, it means he underperformed by -7.9% in that particular match compared to what was expected. Ensuring managers get benefitted irrespective of their teams 'natural' quality.
• NET Shot Quality % = (W. Avg xG/Shot - W. Avg xG/Shot Conc) * 100. This is the efficiency metric, and it is similarly weighed using the same game-state percentiles for the same reasons mentioned earlier to prevent blowouts etc, and benefits managers that can obviously generate higher quality chances, and only concede lower quality ones. So, using our example of Chelsea's 2-0 victory away at Burnley earlier in the season, Maresca's team generated an average 0.12xG per shot, and only conceded one's worth 0.05xG, meaning a +0.07 margin or 7%.
Finally, the actual TPR, which you can see by the color graph. This isn't calculated using a standard 1:1 ratio of the two X/Y variables, instead by a 1:1.55 (Shot Domination: Shot Quality), as research proves efficiency is 0.23 times more predictive of PPG than volume. Net Shot Quality measuring the efficiency holds a 0.65 correlation with PPG, with Shot Domination only showing a 0.42 correlation with PPG. So, TPR = (Shot Domination + (Net Shot Quality * 1.55)) / 2.55)... Essentially the holy grail of a manager's capability.
I've inserted a graph of permanent PL managers since the 20/21 season, reminding you that it samples their entire careers outside the PL, meaning not completely indicative of their actual success in the PL that has its own (dominant) tactical niche.
efStats retweeted
I'am excited to share my first web app:
Mean-variance analysis of passes received
Right now you can find matches available for the last weekend in Serie A and the Premier League.
Any feedback, suggestions or requests → drop them in the comments!
👉🏼passes-received-meanvariance…
efStats retweeted
Been working on a new framework to measure how two-footed a player really is called the Ambipedal Contribution Index. My first long-format article explains the ideas and logic behind it. Let me know your thoughts and guesses for who ranks at the top.
medium.com/@sahilgdwn/quanti…
efStats retweeted
Lutsharel Geertruida has been linked to Liverpool several times in the past.
In this THREAD, I will have a look at his data and what all he has to offer.
1. Ball Retention: He is among the top 10 for PR-BR in the league and as the map shows, he plays very few risky passes PR.
efStats retweeted
📊🇧🇦 Tarik Muharemovic, data report
+ very direct passer, almost to a fault... extremely high rate of progressive and long passes per attempt, which comes at the expense of his accuracy numbers; his CB partner is the opposite, which makes me think it is player disposition rather than a tactical byproduct
+ above average ball carrying marks for a CB
+ high sliding tackle rate and interception marks indicate a front-footed defender
+ aerial duel success rate is excellent
- if I call his direct passing style a positive overall (which I think intention is more important than execution for a 22 y/o), I still need to acknowledge the negative of the high turnover rate it causes
- dueling numbers leave a bit to be desired, especially for his archetype of CB; could be variance in the numbers for just this season, but generally you'd want to see a high number of duels (aligns w/ other defending data), or a higher success rate
➡️ A really interesting prospect. I might be galaxy-braining this, but I think his tendency to attempt high-risk passes and ability to defend on the front foot are a bit counter-productive for the top levels of the modern game. Passing profile fits a counter-attacking side, but most of those teams settle into low blocks more often to maximize the effectiveness of the counters, which would require him to curb his aggressiveness. Nonetheless, an entertaining style of play for a CB.
🚨 Manchester United scouted 22-year-old Sassuolo centre-back Tarik Muharemović in action against Cremonese last weekend.
[@scoutsattending]
efStats retweeted
🇵🇹 Mateus Fernandes (21)
- Club: Southampton
- Position: AM, CM, LM
- MV: 15M €
Energetic and versatile MF. Among U21 CM/AM in T5 leagues, ranks high in both Ground Duel wins and win rate—similar to Jude Bellingham.
Possession metrics are above average, and Shooting and Passing are respectable given his age and club context. A highly promising talent for the future.
🚨 Southampton reject West Ham United bid for Mateus Fernandes. #WHUFC offer worth up to £30 including add-ons rejected immediately without counter. #SaintsFC value 21yo attacking midfielder at £50/60m - no more talks unless clubs get closer @TheAthleticFC nytimes.com/athletic/6548731…
efStats retweeted
📊🇵🇹 Mateus Fernandes, data report
+ equally capable of hitting a rocket or picking out the right pass to set up a teammate; last season he was shoot-first, this season create-first... versatility is a good thing
+ passing success rates exceed volume across the board, not uncommon for lower touch midfielders, but is uncommon for ones that see the ball in advanced positions like Fernandes
+ strong runner on the ball, dribble & prg carry rate per touch is higher than raw totals
+ high oop activity levels
- not great in the air, even for age standards
- while oop activity is high, success rates are low
- lower touch/pass volume is certainly partially due to West Ham's lack of control, but for a b2b midfielder you still would like to see better marks
➡️ Comfortably a mid-table level midfielder now, and top 6 potentially not too far on the horizon. He does a lot of things well, not many things poorly, but doesn't (yet) have a "world class" level trait. Balanced profile, high floor considering both in and out of possession contributions, no shot he's playing in the Championship next season.
