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Statistical analysis of Korean ultra marathon records
Journal of the Korean Data & Information Science Society 2022;33:505-16
Published online May 31, 2022;  https://doi.org/10.7465/jkdi.2022.33.3.505
© 2022 Korean Data and Information Science Society.

Chong Sun Hong1 · Ju Hyun Gil2 · Yoon Hee Lee3 · Ye Won Choi4

14Department of Statistics, Sungkyunkwan University
23Power Institute of Health and Sports Science
Correspondence to: 1 Professor, Department of Statistics, Sungkyunkwan University, 25-2, Sungkyunkwan-Ro, Jongno-Gu, Seoul 03063, Korea. E-mail: cshong@skku.edu
2 Director, Power Institute of Health and Sports Science, 66, Seongsui-ro, Seongdong-gu, Seoul 05541, Korea.
3 CEO, Power Institute of Health and Sports Science, 66, Seongsui-ro, Seongdong-gu, Seoul 05541, Korea.
4 Master course student, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea.
Received March 15, 2022; Revised April 25, 2022; Accepted April 28, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The ultra marathon is a running event longer than the marathon distance, and there are various events such as crossing the country over 600km, and up to 4,960km in the world. This study statistically analyzes ultra marathon record data which are held and managed by a certain marathon club. The total finishers are 52, with the youngest being 34 years old and the oldest being 74 years old. The number of finishes are 2,921, with an average of 56 completed per individual. The ultra distances in this data are from 50km to 1,504km, the longest. Analysis of the valuable data of the few enthusiasts who enjoy extreme sports around the world is statistically significant based on Statistics in Sports. Analyze the records and number of finishes by ultra distances, exercise periods and ages. In addition, ultra finishers were divided into three groups using cluster analysis and analyzed by these clusters.
Keywords : Average speed, cluster, completion period, event.