Running with Cases: A CBR Approach to Running Your Best Marathon
Every year millions of people around the world train for, and compete in, marathons. When race-day approaches, and training schedules begin to wind down, many participants will turn their attention to their race strategy, as they strive to achieve their best time. To help with this, in this paper we describe a novel application of case-based reasoning to address the dual task of: (1) predicting a challenging, but achievable, personal best race-time for a marathon runner; and (2) recommending a race-plan to achieve this time. We describe how suitable cases can be generated from the past races of runners, and how we can predict a personal best race-time and produce a tailored race-plan by reusing the race histories of similar runners. This work is evaluated using data from the last six years of the London Marathon.
KeywordsCase-based reasoning Recommender systems Sports analytics
Supported by Science Foundation Ireland through the Insight Centre for Data Analytics under grant number SFI/12/RC/2289 and by Accenture Labs, Dublin.
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