Load, Readiness and False Precision

In long-term endurance development the main danger from AI load tools is apparent precision rather than missing data. Load monitoring has defensible foundations in the distinction between internal and external load, in intensity distribution and in session rating of perceived exertion, and around them has grown a layer of derived metrics whose predictive claims were criticised in the same journals that popularised them. This paper reviews that criticism, including the mathematical coupling that produces spurious correlation in a widely used workload ratio, the finding that screening is unlikely ever to predict injury, and the base-rate arithmetic showing that a young athlete seeing a red warning is more likely to be well than not. It traces a six-step chain from contested metric to a colour that a 15-year-old acts on before speaking to her coach, and notes that an honest model shown through a misleading interface still misleads. Five design principles follow: describe before predicting, label classifications as descriptive and non-clinical, make uncertainty visible, leave the decision with the coach, and make no injury-risk claim without validated evidence for that population.