Athlete monitoring is widely used to support training and recovery decisions in elite sport, yet practitioners often face challenges related to data quality, feasibility, and the interpretation of short-term readiness signals within longer-term training adaptation. This narrative review synthesizes conceptual and applied developments in athlete monitoring through the lens of 'training effects', encompassing positive adaptation, maintenance, or maladaptation arising from training, competition, and contextual stressors. We distinguish assessment as isolated or periodic measurement from monitoring as repeated, systematic data collection used to track change over time. Building on contemporary conceptual models, readiness is positioned as an operational proxy for training effects that can inform day-to-day decision making when interpreted longitudinally and within context. We integrate the Minimal, Adequate, and Accurate framework to support tool selection that is economical in resource use, sufficient to meet clearly defined objectives, and grounded in valid and reliable measurement. Tools and metrics are organized according to the primary construct they inform: training load, athlete state and training response. We summarize practical considerations across neuromuscular, subjective, physiological, biochemical, and sleep-related indicators, emphasizing interpretive scope, measurement variability, and implementation constraints. To operationalize individualized monitoring, we outline pragmatic approaches using athlete-specific baselines and distribution-based thresholds (e.g., standard deviation intervals, minimum detectable change), alongside decision-making considerations related to Type I and Type II errors. Overall, this framework aims to reconcile scientific rigor with real-world feasibility, supporting practitioner decision making while acknowledging that monitoring should function as a decision-support process rather than a stand-alone determinant of performance outcomes.