From athlete monitoring to responsible decision support in sports analytics

Sports analytics has become increasingly central to applied sport science, yet its conceptual development remains unevenly theorized. This study mapped the evolution of sports analytics research from 2015 to 2025 using a Scopus-based bibliometric analysis of 1,211 documents. Descriptive bibliometrics, co-authorship analysis, country collaboration mapping, keyword co-occurrence analysis, trend-topic analysis, bibliographic coupling, and co-citation analysis were used to examine the field’s development, conceptual structure, and intellectual foundations. Findings show that sports analytics has expanded rapidly and is increasingly organized around athlete monitoring, performance analysis, machine learning, training load, artificial intelligence, and decision support. The literature reflects a shift from descriptive measurement toward predictive and computationally mediated forms of applied sport science. However, this growth also exposes unresolved tensions involving model interpretability, data quality, ethical governance, and the translation of analytics into practice. To address this, the study proposes a responsible and integrative framework that positions sports analytics as an interconnected system of data capture, computational processing, human interpretation, applied performance action, and ethical governance. The review argues that sports analytics becomes meaningful only when technological sophistication is aligned with valid, interpretable, and athlete-centred decision-making.