A full understanding of centre-back behaviour ๐ฏ
The seven pictured players share an elite ability to progress the ball through the opposition structure, but the defensive contexts in which they operate vary considerably.
SkillCorner's Game Intelligence data captures that complexity by combining rich on and off-ball insights for precise profiling of player style and performance.
Learn how that can be applied in practice across our two-part series
๐ Analysing Centre-Backs' Defensive Behaviour: https://t.co/dxj3PVBCqV
๐ Centre-Backs Playing Out From the Back: https://t.co/nuo9rMgTF8
๐๐ฒ๐ป๐๐ฟ๐ฒ-๐๐ฎ๐ฐ๐ธ๐: ๐๐ฑ๐ฒ๐ป๐๐ถ๐ณ๐๐ถ๐ป๐ด ๐๐ถ๐ด๐ต-๐ฉ๐ฎ๐น๐๐ฒ ๐๐ฎ๐น๐น ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฒ๐๐๐ผ๐ฟ๐ โฝ
Advancing the ball inside the opposition defensive structure has become a fundamental element of most possession-oriented teams.
That makes successful inside line-breaking passes executed under pressure among the most valuable actions in the game.
In our latest blog, we use our Game Intelligence data to layer on contextual information like phase of play and degree of pressure to pinpoint centre-backs who regularly complete these passes.
Read the full analysis: https://t.co/nuo9rMgTF8
We're starting to get into the really fun stuff with our Open Data series now.
Our Dynamic Events files open up a huge range of analysis possibilities, and hopefully this intro to visualising Off-Ball Rums provides a way into that rich dataset for people to explore further
๐ Open Data: Digging into Dynamic Events
In the previous entries in our Open Data Series, we have used aggregated metrics to chart player attributes, compare players and build archetypes.
This time around we explore our rich event-level Game Intelligence data through the lens of Off-Ball Runs.
๐Learn how to filter down to a subset of runs and visualise them on a pitch
https://t.co/lggjVYj4kE
Identifying difference-makers under pressure ๐ฏ
๐๐ฎ๐ป๐ด๐ฒ๐ฟ๐ผ๐๐ ๐๐ฐ๐๐ถ๐ผ๐ป๐ ๐จ๐ป๐ฑ๐ฒ๐ฟ ๐๐ป๐๐ฒ๐ป๐๐ฒ ๐ฃ๐ฟ๐ฒ๐๐๐๐ฟ๐ฒ uses our advanced pressure model in conjunction with outcome-based measures to identify players capable of not only dealing with pressure but converting it into favourable situations for their team.
It is one of the key contextual metrics that make up our Game Intelligence suite, combining on and off-ball insights for more precise performance and stylistic profiling.
Watch the video below or check out our blog for more detail ๐ https://t.co/U7zOliWDfi
For years, evaluating the behaviour of centre-backs has remained a significant challenge for scouts and analysts alike.
With our On-Ball Engagement metrics, we now have the relevant context to better quantify the intricate art of defending.
In our latest blog, discover how these metrics can be used to identify and evaluate game situations relevant to a defined game model ๐ https://t.co/dxj3PVBCqV
๐ ๐ช๐ฒ ๐ฎ๐ฟ๐ฒ ๐ฝ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ ๐๐ผ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ ๐ผ๐๐ฟ ๐น๐ฎ๐๐ฒ๐๐ ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐: ๐ฆ๐๐ฟ๐ถ๐ธ๐ฒ๐ฟ ๐๐ฟ๐ฐ๐ต๐ฒ๐๐๐ฝ๐ฒ๐
Using Physical and Game Intelligence data, we analyse on and off-ball behaviours to define key positional archetypes.
The report focuses on stylistic profiling rather than simple rankings, showing how to build tailored performance indices that can be directly applied to your scouting process.
Download the full report ๐https://t.co/OJgNPtqJE6
๐ ๐๐ฟ๐ฐ๐ต๐ฒ๐๐๐ฝ๐ฒ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด: ๐ง๐ต๐ฒ ๐ป๐ฒ๐ ๐ ๐๐๐ฒ๐ฝ ๐ถ๐ป ๐ผ๐๐ฟ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฒ๐ฟ๐ถ๐ฒ๐
In Part 3, we expand our analysis by adding Game Intelligence layers to our physical data.
Weโve updated our 2024/25 A-League open dataset to include Off-ball Run (OBR) and Passing aggregates, allowing for a more complete view of player performance.
Building on the z-score methodology from Part 2, we walk you through merging these multi-dimensional datasets to create a unified framework for striker profiling.
Click here to see the full article: https://t.co/MbbGG3a7Tk
๐ ๐ช๐ฎ๐ป๐ ๐๐ผ ๐ด๐ผ ๐ฏ๐ฒ๐๐ผ๐ป๐ฑ ๐๐ถ๐ป๐ด๐น๐ฒ ๐บ๐ฒ๐๐ฟ๐ถ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐ผ๐๐ถ๐๐ฒ ๐ฝ๐น๐ฎ๐๐ฒ๐ฟ ๐ฝ๐ฟ๐ผ๐ณ๐ถ๐น๐ฒ๐?
In our second installment of the SkillCorner Open Data Series, we dive into the world of composite player profiling. After exploring how to visualize physical data in our first article, it's time to answer broader scouting questions like, โ๐๐ฉ๐ฐ ๐ข๐ณ๐ฆ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ด๐ต ๐ฆ๐น๐ฑ๐ญ๐ฐ๐ด๐ช๐ท๐ฆ ๐ฑ๐ญ๐ข๐บ๐ฆ๐ณ๐ด ๐ฐ๐ท๐ฆ๐ณ๐ข๐ญ๐ญ?โ or โ๐๐ฉ๐ช๐ค๐ฉ ๐ฑ๐ญ๐ข๐บ๐ฆ๐ณ๐ด ๐ค๐ฐ๐ฎ๐ฃ๐ช๐ฏ๐ฆ ๐ธ๐ฐ๐ณ๐ฌ ๐ณ๐ข๐ต๐ฆ ๐ข๐ฏ๐ฅ ๐ช๐ฏ๐ต๐ฆ๐ฏ๐ด๐ช๐ต๐บ ๐ฆ๐ง๐ง๐ฆ๐ค๐ต๐ช๐ท๐ฆ๐ญ๐บ?โ
To do this, we introduce z-scores, a statistical tool that helps standardize different metrics and combine them into one comparable score.
๐ Get started here: https://t.co/bPYJVjCavJ
Want to get started in football analytics?
Our SkillCorner Open Data series gives you hands-on experience with the same data driving decisions at professional teams.
In the first entry, we show you how to manipulate & visualise aggregated physical data ๐
https://t.co/S7kM24bAlA
Metrics such as running volume and high intensity output offer a useful starting point for scouting, but without role specific context or when viewed in isolation, they can be difficult to interpret.
Traditional volume based metrics often fail to capture how players move, overlooking explosiveness and the ability to accelerate, decelerate, and change direction in decisive moments.
๐ ๐ง๐ต๐ถ๐ ๐ฟ๐ฎ๐ถ๐๐ฒ๐ ๐ฎ ๐ธ๐ฒ๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป ๐ณ๐ผ๐ฟ ๐ฟ๐ฒ๐ฐ๐ฟ๐๐ถ๐๐บ๐ฒ๐ป๐: ๐ต๐ผ๐ ๐ฐ๐ฎ๐ป ๐ฝ๐ต๐๐๐ถ๐ฐ๐ฎ๐น ๐ฑ๐ฎ๐๐ฎ, ๐ฐ๐ผ๐บ๐ฏ๐ถ๐ป๐ฒ๐ฑ ๐๐ถ๐๐ต ๐ฒ๐ ๐ฝ๐น๐ผ๐๐ถ๐๐ฒ๐ป๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ต๐ฎ๐ป๐ด๐ฒ ๐ผ๐ณ ๐ฑ๐ถ๐ฟ๐ฒ๐ฐ๐๐ถ๐ผ๐ป ๐บ๐ฒ๐๐ฟ๐ถ๐ฐ๐, ๐ฏ๐ฒ ๐๐๐ฒ๐ฑ ๐๐ผ ๐บ๐ผ๐ฟ๐ฒ ๐ฐ๐น๐ฒ๐ฎ๐ฟ๐น๐ ๐๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ผ๐๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ฝ๐ฟ๐ผ๐ณ๐ถ๐น๐ฒ๐?
To explore this question, Trym Sรธrum presents a scouting case study done by one of our data analysts Liam Bailey. For a deeper dive into the analysis, make sure to check out the full blog post here: https://t.co/tlRAfuOFT8
You can now watch the ๐ฎ๐ฌ๐ฎ๐ฒ ๐ฆ๐ธ๐ถ๐น๐น๐๐ผ๐ฟ๐ป๐ฒ๐ฟ ๐ซ @PySportOrg ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐๐ฝ finalist sessions ๐ผ๐ป-๐ฑ๐ฒ๐บ๐ฎ๐ป๐ฑ.
At our Grand Final in Paris on ๐ฑ ๐๐ฒ๐ฏ ๐ฎ๐ฌ๐ฎ๐ฒ, the top six projects were showcased to a panel of football and data leaders from clubs across Europe.
Congratulations to Amar Shah, winner of the first edition, for his standout project on ๐ฆ๐ถ๐บ๐๐น๐ฎ๐๐ฒ๐ฑ ๐๐ป๐ป๐ฒ๐ฎ๐น๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐ฃ๐ผ๐๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐
Replay the six finalist sessions here โถ๏ธ https://t.co/Ua06e0xdoF
Widening Horizons: Championship Transfer Trends Post-GBE
In our new report, we dig into the data to see how GBE rule changes have impacted scouting and recruitment in the English second tier.
Download: https://t.co/PWeLcN3nJM
It was fun to dig into the @Statsbomb numbers on Declan Rice's slight shift in role this season and how it relates to wider changes in how Arsenal use and occupy space.
Interesting side note to follow in next post
https://t.co/nSDnwt29eS
@Statsbomb Looking for midfielders with similar role changes this season -- crudely, receiving the ball further back, in more space, and making more progressive passes -- Elliot Anderson of Nottingham Forest, Rice's midfield partner for England against Serbia, appears prominently.
New: Declan Rice, Space and Ball Progression
@chewingthecoca uses our Statsbomb 360 data to analyse how Declan Rice's strong start to the season is related to a wider shift in how Arsenal use and occupy space
๐ฝ
https://t.co/WizbYRnsb1
Celtic seemingly close to signing Shin Yamada.
He was one of the young statistical standouts of the 2024 J1 season: https://t.co/0QYlErya5M
But he hasn't yet hit those same heights in 2025.
Will be interesting to see how he does at Celtic.