A Sport Model for Predicting the Sprint Time for Winning 200m Race of a Summer Olympic Games


Published: 2023-04-07

Page: 73-87

M. O. Ogofotha *

Department of Mathematics and Statistics, Federal University Wukari, Nigeria.

O. D. Ogwumu

Department of Mathematics and Statistics, Federal University Wukari, Nigeria.

M. A. Nwaokolo

Department of Mathematics and Statistics, Federal University Wukari, Nigeria.

*Author to whom correspondence should be addressed.


Olympic Games are leading international sporting events featuring track and field events. Beyond this, it has a positive connection with the mental health of an individual and it fosters peace and justice. Thus, this research proposed a sports model for predicting the sprint time (in seconds) to win the Olympics 200m race. Parameters such as age, weight, height and event year were considered in the formulation of the model using the principle of algebra. A dataset of gold medallists for the summer Olympic Games was obtained and computer software was developed for the model. To this end, the model was validated using some suitable statistical techniques such as Root Mean Square Error (RMSE) and the Correlation Coefficient. The results for RMSE and the Correlation Coefficient are 0.5794, and 84.38% respectively. These however indicate our model predictions have a higher degree of agreement with the actual data and it forms a reasonable guide for predictive training, and sprint time forecast.

Keywords: Prediction, olympic games, track event, sprint time

How to Cite

Ogofotha, M. O., Ogwumu, O. D., & Nwaokolo, M. A. (2023). A Sport Model for Predicting the Sprint Time for Winning 200m Race of a Summer Olympic Games. Asian Journal of Pure and Applied Mathematics, 5(1), 73–87. Retrieved from https://globalpresshub.com/index.php/AJPAM/article/view/1791


Download data is not yet available.


Devon D, John H. Field and Track Activities. Online article; 2021.Retrieved June 13, 2022.

Available:https://study.com/learn/lesson/track-field-events-list-types activities.html

Keller JB. Optimal Velocity in a Race. American Mathematics Monthly. 1974;81(474):331-333.

John E. Efiong, Emmanuel A. Olajubu, Felix O. Aranuwa. Formulation of sprint time predictive model for Olympic Athletic Games. International Journal of Information Technology and Computer Science. 2019;11(4):33-43.

Retrieved on June 3, 2022 Available:https://www.mecs-press.org/ijitcs-v11n4/IJITCS-V11N4-4pdf

Parry J. Sport and olympism: Universals and multiculturalism. Journal of the Philosophy of Sport. 2006;33(20):188-204.

Reid H. Olympic sport and its lessons for peace. Journal of the Philosophy of Sport. 2006;33:205-214. Retrieved June 4, 2020. Available:http://academia.ed/1096353

Center for Disease Control. Adults need more physical activity; 2020 Retrieved on 20 September, 2022.


Tamadher Abduaziz, Muroj Muhsen. The Impact of Physical Activity and Sport on Mental Health. Journal of physical Education. 2020;32(3):160-165. Retrieved February 22, 2023.


Harris MA. The relationship between physical inactivity and mental wellbeing: Findings from a gamification-based community-wide physical activity intervention. Health Psychology Open. (018;5(1):1-8.

Retrieved September, 13, 2022. DOI:https://doi.org/10.1177/2055102917753853

Jetzke M, Mutz M. Sport for pleasure, fitness, medals, or slenderness? Differential Effects of Sports Activities on Well-Being. Applied Research in Quality of Life. 2019;15(2020):1519-1534. Retrieved 13 September, 2020.


Westera W. Where Bolt and Bekele meet: the analytical basis of running performance estimates. International Journal of Sports Science and Engineering. 2011;4(3):139-152.

Axler S. Linear Algebra Done Right. (2nd ed.): Springer Science & Business Media, USA. 1997;1-198.

King RB. Beyond the quartic equation. Mathematics, Springer Science & Business Media, USA. 2009;1-165.

Galvan MG, Rojas AL, Rojas AL, Chavarra SL, Elizondo MM, Mendoza JBR, Borrego AMG, Mancilla GA, Lundez JLV. Mathematical models at the olympic games to predict road events. International Journal of Innovative Computing, Information and Control. 2018;14(5):1905-1915. Retrieved on September 20, 2022.


Heisler K. Modelling lower bounds of world record running times. Senior Research Paper Presented to the Department of Mathematics and Computer Science of Stetson University in Partial Fulfilment of the Requirements for the Degree of Bachelor of Science. 2009;1-35.

Jeremy Richmond. Modelling a Sub-10 Second 100m Sprinter Using Newton’s Equations of Motion. New Studies in Athletics. 2011;1(2):69-75. Retrieved on 3 August, 2022. Available:https://www.scribd.com/document/510144941/

Schmidtbleicher D. Training for power events in: Strength and power in sport. P.V. Komi, ed. Boston: Blackwell Scientific. 1992;381395.

Jeremy Richmond. Newtonian model of an elite sprinter: How much force do athletes need to produce each step to be world class? Fitness First Randwick Australia, University of Sydney. 2010;1-10.

Dilwyn Edward, Mike Hamson. Introduction to mathematical modelling. The Macmillan Press LTD, Houndmills, Basingstoke, Hampshire RG21 2XS and London; 1989. ISBN: 0-333-45935-0

Han H, Kim H. The Solution of Exponential Growth and Exponential Decay by Using Laplace Transform. International Journal of Difference Equation. 2020;15(2):191-195. Retrieved August 15, 2022.