Despite the rise of data generation in football, the expertise of data analytics within the sport is relatively underdeveloped. To further understand the landscape, a cross-sectional, observational study design was used to survey practitioners in senior, professional, or semi-professional football. Areas of interest included the personnel involved (the ‘who’), the data collected (the ‘what’), and the analytical techniques employed (the ‘how’). A total of 206 practitioners completed an online survey, with representation from all six FIFA confederations. Of the 206 respondents, 86% were male, 13% female, and 1% preferred not to disclose their gender. Respondents were categorised as working in either the performance (73%), data (18%), or medical (9%) department. Heterogeneity was observed in responses across all departments regarding training load metrics, outcome metrics, methodological attributes, and measurement properties. Evidence sources used prior to implementing a new metric varied between departments, with performance (63%) and medical (67%) staff relying on professional industry and/or community, while data staff (57%) utilised more in-house projects. The analytical approach used most frequently was exploratory data analysis (90%), with modelling, forecasting, and predicting the least frequent (54%). Respondents reported using a mix of solutions for data storage, aggregating and analysing, and reporting and visualising data. Spreadsheets were cited as a popular solution for data wrangling and reporting tasks. The findings provide an overview of current data ecosystems and information systems in modern football organisations. These results can be used to improve data analytics service provision in football by helping identify areas for development and progression.
Iacono et al. (Thu,) studied this question.