The analytics-practice gap: why sports data fails to translate into coaching decisions

The rapid expansion of tracking systems, wearable sensors, video-based analysis platforms, and analytical models has created increasingly data-rich environments in sport. However, the extent to which these developments translate into coaching decisions remains uncertain. In applied settings, decisions about training design, tactical preparation, player selection, workload management, and in-game adjustment often continue to rely heavily on experiential knowledge, contextual judgement, and established practice. This paper examines this disconnect, conceptualized as the analytics-practice gap, and argues that the gap is not primarily caused by insufficient data, but by the limited actionability, contextualization, integration, and usability of many analytical outputs. Performance indicators and advanced metrics may describe what occurred, but they do not automatically clarify what coaches should do next. Addressing this gap requires a shift from data-first to decision-first analytics. This involves defining the coaching problem before selecting metrics, integrating contextual information, synthesizing technical, tactical, and physical data, and communicating outputs in ways that support practical intervention. Ultimately, the value of sports analytics depends less on the capacity to generate additional data and more on the ability to connect existing information to actionable changes in training design, tactical planning, player management, and performance preparation.