Theses on Analytics & AI

The Hidden Assist? Data Analytics in Football as an Organisational Capability

Abbas Haidari & Ryan Jacob Martin (2026) 

Abstract: Data analytics has become increasingly important in professional football, yet access to data does not by itself create a competitive advantage. This qualitative study investigates how clubs in Allsvenskan, Sweden's highest men's football division, use data analytics as an organisational capability to create competitive advantage, and how these capabilities vary under different resource conditions. Grounded in the Resource-Based View, the study draws on semi-structured interviews with informants across clubs, the league trade body, and technology and sport-service providers, analysed using the Gioia methodology and complemented by a contextual analysis of five clubs' annual reports.

The findings show that analytics is most deeply embedded in the sporting side of clubs and is less developed commercially. Since the foundational tools are accessible to all clubs commercially, they do not inherently grant any competitive advantage. What differentiates clubs is the organisational capability built around data use: the interpretation, routines, and continuity through which data informs decisions. This capability is socially complex, causally ambiguous, and path-dependent, and therefore harder for competitors to imitate. These capabilities vary across clubs as a matter of priority rather than presence: larger clubs develop analytics as a broad organisational system, while smaller, resource-constrained clubs apply it more selectively, concentrating on recruitment and talent development.

The study contributes by shifting the question of football analytics from what data can measure to what an organisation can do with it. It suggests that the resource separating clubs is organisational rather than technical, an insight with potential relevance to resource-constrained organisations beyond football.