How Do Professional Football Clubs Use Data?
A Global Survey of Analytics Infrastructure - Investigating Operational Frameworks and Practices Across National Federations and Clubs.
The following summary critically reviews the research conducted by Lorenzo Lolli, Pascal Bauer, Callum Irving, Daniele Bonanno, Oliver Höner, Warren Gregson, and Valter Di Salvo, titled "Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world." All data, figures, and analysis presented here are drawn from their original work; I do not claim any authorship or ownership of the content. This summary has been written to provide a concise and technically informed synthesis of the paper’s findings, methodologies, and implications, while maintaining fidelity to the authors’ intellectual contributions.
Introduction
The authors of this paper explore how modern football organisations have embraced data analytics within their operational and performance support infrastructures. They underline that the role of performance science professionals has evolved significantly, becoming crucial not only for player performance and injury management, but also for decision-making across recruitment, coaching, and administration.
The study frames its discussion around two essential components of a modern analytics infrastructure: the data ecosystem and the information system. The data ecosystem refers to how data is collected, structured, standardised, and stored within an organisation, while the information system denotes the interfaces and processes that facilitate the translation of data into actionable insights. As the authors explain, “data standardization and centralization represent the foundations of a data ecosystem”, with standardisation being “a pragmatic definition of names, description, and forms for each piece of raw data”, and centralisation referring to the aggregation of diverse quantitative and qualitative sources.
The authors distinguish between the roles of data engineers—who construct the data infrastructure—and analysts, who interpret and apply the data to organisational decision-making. Yet, they observe that many clubs lack sufficient personnel with such expertise, a bottleneck that hinders the development of effective systems for insight generation.
The study also highlights that the gap between raw data and practical decision-making necessitates robust information systems. These systems should offer timely and logical access to structured insights for various stakeholders. However, many organisations rely on generic, off-the-shelf solutions, indicating underinvestment in bespoke systems tailored to the unique demands of professional football environments.
Another critical observation is the interdisciplinary demand for integrating sports science with computer science, particularly in the processing of positional tracking data. Yet, a substantial proportion of clubs and federations still lack dedicated personnel such as data engineers and sport-side analysts, creating further barriers to fully operationalising data-driven workflows.
Finally, the paper identifies a significant research gap: although data analytics has become an integral part of elite sport, there has been no thorough, contemporary assessment of how data ecosystems and information systems are actually implemented in practice. With this in mind, the study seeks to address whether current analytics infrastructures effectively meet the operational needs of support staff in elite football contexts.
Materials and Methods
Survey Design and Distribution
The study employed a cross-sectional survey targeting staff working in male professional football. The survey aimed to examine data analytics infrastructures and information systems in place at elite-level clubs and federations. The research team leveraged an international conference to recruit participants, ensuring relevance by focusing on delegates from the 32 national federations qualified for the FIFA World Cup 2022™ and clubs affiliated with the Aspire in the World Fellows community—a global network of elite football organisations.
The survey instrument, designed by a panel of six experienced investigators, was informed by foundational literature in sports analytics and sports science. It consisted of four main sections:
Demographic and professional background
Current data ecosystems
Perceptions of analytics interfaces used to generate actionable insights
The use and value of positional data.
Each section was aligned with previous conceptual definitions of data ecosystems and information systems as proposed by Alamar and Delen & Demirkan.
The final version was available in multiple languages. The survey allowed respondents to consult colleagues where necessary. Only national federations qualified for the World Cup completed section (4), due to their relevant expertise in coaching and performance analysis.
Statistical Analysis
Responses from the FIFA and Aspire samples were treated independently. Within the Aspire sample, results from professional clubs and national federations were also separated. Descriptive statistics were used to analyse results, reporting frequencies and percentages across categorical items and median with interquartile range (IQR) for count data. Qualitative descriptors (e.g., "Most," "Majority," "Minority") were assigned to frequency ranges to standardise interpretation. The response rate was calculated as the proportion of organisations who participated relative to those invited.
Results
Respondents
The final sample included 29 out of 32 national federations from the FIFA group (response rate: 90.6%) and 37 out of 48 organisations from the Aspire in the World Fellows network (32 professional clubs and 5 non-FIFA national federations; response rate: 77.1%). Roles of the respondents spanned department directors, heads of performance, analysts, coaches, and medical staff, reflecting a multidisciplinary representation.
FIFA federation respondents reported a median of 15 years of professional football experience (IQR: 12–20 years) and had worked at their current organisation for a median of 4 years (IQR: 2–7 years). In the Aspire sample, professional club respondents had a similar median football experience of 14 years (IQR: 9–17 years) and 4 years at their current club (IQR: 2–8 years). Respondents from the five national federations in the Aspire group reported comparable experience levels.
This respondent profile supports the authors’ claim that their analysis draws from experienced professionals embedded in decision-making processes.
Data Ecosystem
The survey revealed significant variation in the structuring of data ecosystems across organisations. In the FIFA sample, respondents reported a median of 3 match analysts (IQR: 2–6) and 1 sports scientist (IQR: 1–2), while the presence of statisticians and data engineers was notably limited. As the authors noted, “approximately half of respondents (~52%)” acknowledged that their organisation’s data management “has limited standardization,” often involving siloed processes across departments. Only about one-third (~31%) described having standardized systems with integrated platforms and dedicated data staff.
In contrast, the professional clubs from the Aspire sample demonstrated more advanced integration. Clubs employed a median of 5 match analysts (IQR: 3–7) and 3 sports scientists (IQR: 1–5), with a greater proportion (~53%) reporting well-defined, integrated data management processes. Even so, the number of dedicated statisticians and engineers remained low, echoing the underrepresentation previously reported in the literature. National federations within the Aspire group reflected similar heterogeneity to the FIFA sample, underscoring the persistent variability and limited standardization in data ecosystems globally.
This heterogeneity supports the authors’ view that while football organisations increasingly acknowledge the importance of analytics, practical implementation often lacks the dedicated human resources and structural cohesion necessary for effective knowledge generation and decision-making.
Information System
The study revealed notable discrepancies in the maturity and integration of information systems across both FIFA and Aspire samples. In the FIFA sample, less than half of the respondents agreed that “the data analysis strategy is consistent across all business units” (41%) or that “information needed to guide decision-making are readily available” (48%). Although data-driven decision-making was acknowledged across departments—technical (65%), recruiting (52%), performance (69%), and medical (62%)—the clarity of communication remained a limiting factor. Only 42% agreed that information was “communicated clearly” to coaching staff, and a mere 31% indicated this was true for individual players. Communication methods relied heavily on PDF reports (66%) and video-based insights (66%), while the use of bespoke apps remained limited (55%).
Among professional clubs in the Aspire sample, slightly higher integration levels were observed. A majority of respondents (58%) indicated information for decision-making was readily available, while only 36% agreed that the data strategy was consistent across departments. Notably, 58% of respondents confirmed that business intelligence tools enabled clear communication to coaching staff, and 48% to players. Clubs reported greater reliance on interactive tools such as dashboards (50%), though traditional formats like reports (70%) and analyst presentations (57%) still dominated. Information dissemination to players remained indirect, primarily mediated by coaches (67%).
These findings underline a persistent gap between data production and its practical utility. Despite growing appreciation for data-informed decision-making, the use of outdated or fragmented communication tools hinders efficient translation of insights into actionable strategy.
From Positional Data Processing to Coaching Insights
The survey highlighted significant variation in the perceived clarity and utility of positional data analytics among respondents from national federations. While a substantial majority rated general performance metrics derived from match-analysis data as “very useful” or “extremely useful” (ranging from 55% to 87%), opinions on the clarity and consistency of football metrics from international analytics providers were far more divided. Only about 30% expressed agreement with their clarity, while 27% disagreed or strongly disagreed, revealing a critical gap in standardisation and comprehension.
Tracking data (76%), video footage (72%), and event data (62%) were identified as the most frequently utilised sources, predominantly for own-match analysis (62%), opponent scouting (62%), and trend monitoring across competitions (55%). Analysts showed strong confidence in merging these data sources (~69%), with “event-specific phases” and “customised phases of play at the analyst's discretion” being the most preferred frameworks for contextual interpretation (~60%).
These insights reveal a dual challenge: although analysts recognise the tactical value of these data streams, the lack of shared standards in data taxonomy and inconsistent literacy among technical staff may hinder effective translation of metrics into coaching insights.
Discussion
The authors provide a comprehensive account of the current state of data analytics infrastructures in elite football, revealing both progress and persistent limitations. Their findings show that while data analytics has gained widespread recognition as a critical tool for player and team management, actual implementation across football organisations remains inconsistent and often under-resourced. “Our investigation provided a contemporary overview… [yet] the heterogeneity of our findings highlighted the inconsistency of current data analytics architectures” (Lolli et al.).
One of the clearest structural issues is the underemployment of key personnel. The study confirms earlier concerns that many organisations lack dedicated data engineers and statisticians, with support staff from other domains (e.g., strength and conditioning, sports science) often outnumbering analytics specialists. This imbalance likely limits the capacity to process raw data effectively and to build scalable information systems. As noted, “staff involved in other areas… outnumber staff dedicated to data ecosystem development” (Lolli et al.).
Additionally, although clubs generally show more advanced data processes than federations—possibly due to the frequency of operations and financial investment—the study finds that both types of organisations face barriers in integrating insights across units. Most information dissemination still relies on outdated tools like PDF reports or slide presentations, rather than integrated decision support systems. “PDF-reports (~70%) and presentations from support staff (~60%) represented the main tools used” (Lolli et al.), reinforcing concerns that modern communication platforms remain underutilized.
The study also underlines an important but often overlooked issue: the lack of a unified framework for football performance metrics. Respondents struggled with the heterogeneity of terminology, with “only a third… perceiving football metrics… as sufficiently clear” (Lolli et al.). This confusion stems from the absence of standardised taxonomies and diverse methodological approaches adopted by commercial data providers. The widespread use of “customised phases of play at the analyst’s discretion” further reflects this lack of consensus.
Finally, while efforts like the FIFA Football Language are noted as attempts to address this fragmentation, the authors stress that the field lacks a scientifically validated framework to unify how performance metrics are defined, interpreted, and applied. This gap continues to hinder both research and practice. Overall, the study calls for “finalis[ing] scientific consensus on a framework of established taxonomies,” emphasizing that standardisation and domain-specific literacy are foundational for advancing football analytics.
References
Lolli, L., Bauer, P., Irving, C., Bonanno, D., Höner, O., Gregson, W., & Di Salvo, V. (2024). Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world. Science and Medicine in Football, 1-10. https://doi.org/10.1080/24733938.2024.2341837
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