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The Clustering Project (eBook | Eng)

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EUR €14.99
The Clustering Project (eBook | Eng)
The Clustering Project (eBook | Eng)

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The Clustering Project (eBook | Eng)

EUR €14.99
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Categories: Thematic Research, eBook
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The Clustering Project

A new framework for smarter scouting

  • Written by Soccerment Research and Antonio Gagliardi (Assistant Coach & Head of Match Analysis at Italy National Team FIGC)
  • Foreword by Fabio Paratici (Managing Direct, Football - Tottenham Hotspurs)
  • 160 pages

Abstract

Modern football is a different sport than it was even just a decade ago, with innovative tactical ideas requiring more and more eclectic and tactically fluid players to execute them. The traditional roles we automatically default to when categorising players (e.g. full-back, centre-back, winger, etc.) are therefore not suited to this new landscape, which is shifting the focus more and more towards what a player actually does on the pitch rather than their position within the starting lineup. This work is an example of how we can change the way we categorise players, letting their actions on the pitch tell us what is their actual function in the team’s game plan.

Using detailed stats from season 2017/18 to 2020/21, we applied a clustering analysis to categorise players into thirteen groups, based on their function on the pitch:

  • Ball stopper
  • Build-up initiator
  • First-line breaker
  • Wide controller
  • Wide creator
  • Ball stealer
  • Build-up director
  • Box-to-box raider
  • Chance creator
  • One-to-one explorer
  • Mobile finisher
  • All-round finisher
  • Target man

The selected stats are meant to recover a stylistic characterisation, rather than a power ranking. Our dataset includes all outfield players in the top five European leagues with a sufficient number of minutes played, while goalkeepers will be treated in a separate study, due to the specific nature of the role and the small sample size. We structured our analysis to reconstruct not only a class label for each player, but a full hybridation profile, with the classification probability for all thirteen clusters, allowing us to recover a more unique (albeit not conclusive) characterisation.

These results have an immediate application in scouting, providing a search filter with a data-driven characterisation, as opposed to the a priori one given by traditional roles. Additionally, it can help solve some tactical misunderstanding surrounding the specific role of some players which may be difficult to characterise. Finally, by looking at the squad profile of teams through the lens of our clusters, we can investigate how different top-level teams are built, and recover some hints at which kind of players are required to achieve top results in modern football.

This work is not exhaustive, but it provides an entry point into a new way to look at player categorisation, through the use of data. In the future, this representation will surely be improved upon using a wider dataset, and eventually including additional contextual information such as the one provided by full positional tracking data.

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