<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of runners risking over-straining, injury, or collapse, we would like to provide runners with advice to appropriately plan their pacing. Many participants are older or without extensive training experience and may particularly benefit from such pacing assistance. Our aim is to provide this with the help of machine learning. We first analyze a large publicly available dataset of results from the years 2010 - 2019 (n = 423 496) to identify pacing patterns related to age, sex, ability, and temperature of the race day. These features are then used to train machine learning models for predicting runner’s finish time and to identify which runners are at risk of making severe pacing errors and which ones seem set to pace well. We find that prediction of finish time improves over the current baseline, while identification of pacing patterns correctly identifies over 70% of runners at risk of severe slowdowns, albeit with many false positives.</p>
</abstract>

The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of runners risking over-straining, injury, or collapse, we would like to provide runners with advice to appropriately plan their pacing. Many participants are older or without extensive training experience and may particularly benefit from such pacing assistance. Our aim is to provide this with the help of machine learning. We first analyze a large publicly available dataset of results from the years 2010 - 2019 (n = 423 496) to identify pacing patterns related to age, sex, ability, and temperature of the race day. These features are then used to train machine learning models for predicting runner’s finish time and to identify which runners are at risk of making severe pacing errors and which ones seem set to pace well. We find that prediction of finish time improves over the current baseline, while identification of pacing patterns correctly identifies over 70% of runners at risk of severe slowdowns, albeit with many false positives.

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

Dept. of Computer Science and Engineering

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of...

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

Data publikacji: 03 lut 2024

Zakres stron: 124 - 138

DOI: https://doi.org/10.2478/ijcss-2023-0014

Słowa kluczowe
HALF-MARATHON, RUNNING, PACING PATTERNS, RESULTS DATA, MACHINE LEARNING

© 2023 Moa Johansson et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dodatkowe informacje o materiałach

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

Data publikacji: 03 lut 2024

Zakres stron: 124 - 138

DOI: https://doi.org/10.2478/ijcss-2023-0014

Słowa kluczoweHALF-MARATHON, RUNNING, PACING PATTERNS, RESULTS DATA, MACHINE LEARNING

© 2023 Moa Johansson et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dodatkowe informacje o materiałach

Słowa kluczowe
HALF-MARATHON, RUNNING, PACING PATTERNS, RESULTS DATA, MACHINE LEARNING