Abstract
As running has become an increasingly popular method of personal exercise, more and more recreational runners have been testing themselves by participating in endurance events such as marathons. Even though elite endurance runners have been the subject of considerable research, the training habits and performance potential of recreational runners are not as well-understood. Consequently, recreational runners often have to rely on one-size-fits-all training programmes and race prediction models. As a result, recreational runners frequently suffer from a lack of expert feedback during training and if their race-time prediction is inaccurate this can significantly disrupt their race planning and lead to a sub-optimal race-time after months of hard work. The main contribution of this work is to describe an extended case-based reasoning system for predicting the race-times of recreational runners which, for the first time, uses a combination of training history and past race-times in order to improve prediction accuracy. The work is evaluated using real-world data from more than 150,000 marathon training programmes.
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Acknowledgements
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. (2022). An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners. In: Keane, M.T., Wiratunga, N. (eds) Case-Based Reasoning Research and Development. ICCBR 2022. Lecture Notes in Computer Science(), vol 13405. Springer, Cham. https://doi.org/10.1007/978-3-031-14923-8_22
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