Abstract
Training for the marathon, especially a first marathon, is always a challenge. Many runners struggle to find the right balance between their workouts and their recovery, often leading to sub-optimal performance on race-day or even injury during training. We describe and evaluate a novel case-based reasoning system to help marathon runners as they train in two ways. First, it uses a case-base of training/workouts and race histories to predict future marathon times for a target runner, throughout their training program, helping runners to calibrate their progress and, ultimately, plan their race-day pacing. Second, the system recommends tailored training plans to runners, adapted for their current goal-time target, and based on the training plans of similar runners who have achieved this time. We evaluate the system using a dataset of more than 21,000 unique runners and 1.5 million training/workout sessions.
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Prediction estimates are more accurate for women than for men, echoing similar findings by [17] when using previous marathon times to predict future PBs.
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Acknowledgments
Supported by Science Foundation Ireland through the Insight Centre for Data Analytics (12/RC/2289_P2) and the SFI Centre for Research Training in Machine Learning (18/CRT/6183).
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Feely, C., Caulfield, B., Lawlor, A., Smyth, B. (2020). Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_5
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