Uncovering data-driven Football: Topic modeling from a data analytics perspective

Football (soccer) is increasingly adopting data-driven approaches, and emerging Data Science concepts are seen as potential game changers. The increase in the amount of data reinforces this development, and new research directions are arising. Therefore, this article investigates the developments in Football Analytics by presenting a comprehensive overview and a comparative analysis of current approaches, trends, and challenges. For this purpose, a first Latent Dirichlet Allocation (LDA) analysis with 152 articles was conducted. Based on the LDA, we subdivide the research field through a taxonomy into seven different topics. After this, the categorized literature was analyzed on the basis of generic terms derived from the LDA, focusing on current trends and open research directions in the domain. In addition, the individual assigned works are reviewed with regard to Football Analytics, highlighting overarching topics to further advance the research field in terms of challenges and open issues. Based on these results, eight open research fields were ultimately defined, which should be systematically addressed in the future.