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Missing data patterns in runners' careers: do they matter?

Stival, Mattia; Bernardi, Mauro; Cattelan, Manuela; Dellaportas, Petros; (2022) Missing data patterns in runners' careers: do they matter? Arxiv 10.48550/arXiv.2206.12716. (In press). Green open access

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Abstract

Predicting the future performance of young runners is an important research issue in experimental sports science and performance analysis. We analyse a data set with annual seasonal best performances of male middle distance runners for a period of 14 years and provide a modelling framework that accounts for both the fact that each runner has typically run in three distance events (800, 1500 and 5000 meters) and the presence of periods of no running activities. We propose a latent class matrix-variate state space model and we empirically demonstrate that accounting for missing data patterns in runners' careers improves the out of sample prediction of their performances over time. In particular, we demonstrate that for this analysis, the missing data patterns provide valuable information for the prediction of runner's performance.

Type: Article
Title: Missing data patterns in runners' careers: do they matter?
Open access status: An open access version is available from UCL Discovery
DOI: 10.48550/arXiv.2206.12716
Publisher version: http://arxiv.org/abs/2206.12716v1
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10163515
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