Preparing for the marathon involves many weeks of dedicated training. Achieving the right balance between building strength and endurance and the need for rest and recovery is a must, if a runner is to arrive at the start-line injury-free and ready to achieve their desired finish-time. However, because most recreational runners rely on generic training plans, they can struggle to find this balance, which can impact their motivation, health, and performance. In this paper, we describe a novel case-based reasoning approach to fine-tuning a runner’s training by recommending training adjustments based on the patterns of similar runners at corresponding points in their marathon training. The approach is designed to target training adjustments that are based on similar runners but with varying race goals, to allow runners to adjust their training for slower or faster finish-times, as their training progresses and motivations change. We evaluate the recommendations produced using a large-scale real-world dataset according to several factors including, (i) the plausibility of the recommended training adjustment, (ii) the effectiveness of the adjustment when it comes to achieving a particular performance goal, and (iii) the safety of the adjustment in terms of the degree of risk that it will lead to an injury or otherwise disrupt training. Our findings suggest that plausible, effective, and safe recommendations can be generated for runners when evaluated against a range of race goals.