Volume 43, Issue 1, July 2024, Pages 198–206
Papa Serigne Diène1, El Hadji Mama Guène2, Daouda Diouf3, Ndiack Thiaw4, Mame Ngoné Bèye5, Ndarao Mbengue6, Abdoulaye Samb7, and Abdoulaye BA8
1 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
2 Department, LGL-TPE, University of Lyon 1, Lyon, France
3 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
4 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
5 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
6 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
7 Faculty of Medicine and Pharmacy Odontostomatology, UCAD, Senegal
8 Faculty of Medicine and Pharmacy Odontostomatology, UCAD, Senegal
Original language: English
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this study, a linear regression approach is used to model 400 m performance. We have choosen to consider the time of the first 100 metres and the age of the athletes as key variables, as they are likely to play a determining role in succeeding in this specific distance. The start, symbolised by the first 100 metres, is often considered a crucial phase in the 400m. Sprinters who manage to maintain rapid acceleration in this first part of the race tend to perform more consistently over the whole 400 metres. Studies have shown that competitive experience can play a significant role in athletic performance. Athletes who have accumulated years of experience often develop more efficient running strategies and better effort management, thus positively influencing their results.
Author Keywords: machine Learning, linear regression, 400m race, modelling.
Papa Serigne Diène1, El Hadji Mama Guène2, Daouda Diouf3, Ndiack Thiaw4, Mame Ngoné Bèye5, Ndarao Mbengue6, Abdoulaye Samb7, and Abdoulaye BA8
1 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
2 Department, LGL-TPE, University of Lyon 1, Lyon, France
3 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
4 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
5 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
6 STAPS-JL Laboratory, INSEPS-UCAD, Senegal
7 Faculty of Medicine and Pharmacy Odontostomatology, UCAD, Senegal
8 Faculty of Medicine and Pharmacy Odontostomatology, UCAD, Senegal
Original language: English
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this study, a linear regression approach is used to model 400 m performance. We have choosen to consider the time of the first 100 metres and the age of the athletes as key variables, as they are likely to play a determining role in succeeding in this specific distance. The start, symbolised by the first 100 metres, is often considered a crucial phase in the 400m. Sprinters who manage to maintain rapid acceleration in this first part of the race tend to perform more consistently over the whole 400 metres. Studies have shown that competitive experience can play a significant role in athletic performance. Athletes who have accumulated years of experience often develop more efficient running strategies and better effort management, thus positively influencing their results.
Author Keywords: machine Learning, linear regression, 400m race, modelling.
How to Cite this Article
Papa Serigne Diène, El Hadji Mama Guène, Daouda Diouf, Ndiack Thiaw, Mame Ngoné Bèye, Ndarao Mbengue, Abdoulaye Samb, and Abdoulaye BA, “A predictive model for 400-metre performance: Analysis using the first 100 m and athlete age,” International Journal of Innovation and Applied Studies, vol. 43, no. 1, pp. 198–206, July 2024.
