Abstract
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Abstract
We introduce a novel approach for predicting running performance, designed to
apply across a wide range of race distances (from marathons to ultras),
elevation gains, and runner types (front-pack to back of the pack). To achieve
this, the entire running logs of 15 runners, encompassing a total of 15,686
runs, were analyzed using two approaches: (1) regression and (2) time series
regression (TSR). First, the prediction accuracy of a long short-term memory
(LSTM) network was compared using both approaches. The regression approach
demonstrated superior performance, achieving an accuracy of 89.13% in contrast,
the TSR approach reached an accuracy of 85.21%. Both methods were evaluated
using a test dataset that included the last 15 runs from each running log.
Secondly, the performance of the LSTM model was compared against two benchmark
models: Riegel formula and UltraSignup formula for a total of 60 races. The
Riegel formula achieves an accuracy of 80%, UltraSignup 87.5%, and the LSTM
model exhibits 90.4% accuracy. This work holds potential for integration into
popular running apps and wearables, offering runners data-driven insights during
their race preparations.
Keywords
running performance prediction -
deep learning -
running logs -
ultramarathon -
marathon
Publication History
Received: 20 May 2024
Received: 30 July 2024
Accepted: 31 July 2024
Article published online:
09 October 2024
© 2024. The Author(s). This is an open access article published by
Thieme under the terms of the Creative Commons
Attribution-NonDerivative-NonCommercial-License, permitting copying and
reproduction so long as the original work is given appropriate credit. Contents
may not be used for commercial purposes, or adapted, remixed, transformed or
built upon.
(https://creativecommons.org/licenses/by-nc-nd/4.0/).
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
Bibliographical Record
Sandhyarani Dash. Win Your Race Goal: A Generalized Approach to Prediction of Running
Performance. Sports Med Int Open 2024; 08: a24016234.
DOI: 10.1055/a-2401-6234
Description
This study presents a method for predicting running performance using deep learning techniques.