| Player | Team | Position | |
|---|---|---|---|
| Rank | |||
| 1 | Yamil Asad | FC Cincinnati | Left Winger |
| 2 | Conor Bradley | Liverpool | Right-Back |
| 3 | Marcos Portillo | Talleres | Central Midfield |
| 4 | Yankuba Minteh | Feyenoord | Right Winger |
| 5 | Malik Tillman | PSV Eindhoven | Attacking Midfield |
| 6 | Rodri | Manchester City | Defensive Midfield |
| 7 | Lionel Messi | Inter Miami | Right Winger |
| 8 | Ángel Di María | Benfica | Right Winger |
| 9 | Luka Ivanušec | Feyenoord | Left Winger |
| 10 | Luciano Acosta | FC Cincinnati | Attacking Midfield |
Summary
Football recruitment is about finding exceptional talents that fit into your team style.
Often we try and find similar players to a player we like. But is there anyone like fan favourite Kevin De Bruyne?
Here I instead find players that stand out, the outliers.
These outliers may flag up a few exceptional talents for you to look at in your scouting process.
Introducing outlier analysis
Most of the public football recruitment analysis tends to fall into three camps. Given a good player, could you find me a player with a similar style? Visualizations that show a fixed set of skills for a position, e.g. Radars. Scatter plots that show extreme values in one or two traits.
I got interested in the last approach via a thread by @MishraAbhiA. Yet, I am not satisfied with cherry-picking data and identifying a handful of players. Can we do better and scale this type of approach to look at many skills?
Outlier analysis is perfect for this. We use it in the industry to detect faulty machinery if we observe unusual sensor readings. Here we can take many skills and identify players where their mix of skills is unusual instead. We find these unique players via machine learning.
What is an outlier?
An outlier is a player where their skills are far away from the other players. Robert Lewandowski scored 11 more goals than his nearest competitor Messi in 2020-21. He is a clear outlier in the big five leagues, according to FBRef.
“Recruitment is all about outliers. Find me the best players, in every trait, in every league, across the globe.” Ted Knutson, StatsBomb Evolve, 17 March 2021.
Top outliers
In this blog, I identify outfield players who are outliers. I use their skill information, such as the number of interceptions per 90 minutes.
I exclude centre-backs as they are more challenging to scout with data. But @EveryTeam_Mark wrote a great blog on scouting centre-backs with statistics.
As a sanity check, let’s check out the top-10 players identified by their outlier score. The top players look pretty great to me.
The data
I include players who played in the specific leagures in 2023-24. I then exclude players who played fewer than 675 minutes over the last three seasons. The leagues include the top flight in Argentina, Belgium, Brazil, England, France, Germany, Italy, Mexico, Netherlands, Portugal, Spain and USA. The second tiers in England, France, Germany, Italy, and Spain are also included.
The data comes from FBRef via Opta and Transfermarkt.
I combine player data over the last three seasons, so each player has one line. Combining the data ensures that the youngest players have enough data to analyze.
Find your favourite player
The best way to show the potential of this outlier analysis is to show some interactive plots. Higher points in the chart are outliers. Younger players are towards the left of the charts. For each player, I highlight the top 4 statistics that contribute the most to their outlier score.
I have split the charts into three positions. You can toggle points away by clicking on the legend, e.g. to see only the top 10% of players. On mobile, you double click the chart to zoom out.
Can you find any gems?