Young athletes, parents, and coaches are increasingly exposed to training metrics from wearable technology, yet such metrics are difficult to interpret without contextual explanation. We present a rule-grounded data-to-text framework for supporting data literacy in youth football through concise, stakeholder-specific summaries of training sessions. A rule layer maps duration-normalised indicators to structured facts about session profile, internal intensity, speed exposure, and movement dynamics, which are then verbalised by a large language model for coaches, parents, or players. We compare direct generation from raw metrics, generation from rule-derived facts, and an augmented rule-grounded configuration, ENRICHED, that supplements validated facts with raw metrics and explicit threshold definitions. In this setting, selected open-weight models are additionally adapted using LoRA. The framework is developed using 122 anonymised player-session records from a U15 environment and evaluated on a held-out subset of ten sessions with stakeholder-oriented reference summaries. The results indicate that rule grounding improves reliability and audience adaptation compared with direct generation from raw metrics, particularly by reducing unsupported or overly strong interpretations. A school-based expert evaluation with physical education teachers further suggests that player-facing explanations in the evaluated ENRICHED setting can remain accurate, comprehensible, and practically useful. We position the framework as an interpretable data-literacy support interface for youth sport analytics.