Data-driven scouting in football: A multiple case study

Abstract

Data analysis is reshaping football scouting, but its impact on decision-making remains a subject of debate. This study investigates how Data-Driven Decision Support Systems (DDDSS) influence scouting practices, using a qualitative multiple-case design based on semi-structured interviews with five professionals in various competitive contexts. The findings show that DDDSS constitute an essential infrastructure, enabling the systematic evaluation of large groups of players beyond manual capacity. However, their effectiveness depends on human expertise for contextual interpretation, tactical assessment, and behavioral evaluation, indicating limited autonomy. The study identifies key implementation challenges, including cultural resistance, limited analytical capacity, and uneven data availability across leagues. Although advanced metrics improve technical analysis and support the internal justification of recruitment decisions, they have a limited direct impact on market evaluation, which remains largely driven by traditional performance indicators. Fundamentally, competitive advantage does not stem from access to data, but from the ability to interpret and apply it effectively. These conclusions position modern scouting as a hybrid decision-making process, in which future progress lies in integrating predictive analysis with human judgment, rather than in the pursuit of total automation.