Title of article :
Predicting the Medals of the Countries Participating in the Tokyo 2020 Olympic Games Using the Test of Networks of Multilayer Perceptron (MLP
Author/Authors :
Fazlollahi, Pouya Department of Sports Science - Islamic Azad University - South Tehran Branch - Tehran, Iran , Afarineshkhaki, Akbar Department of Sports Science - Islamic Azad University - South Tehran Branch - Tehran, Iran , Nikbakhsh, Reza Department of Sports Science - Islamic Azad University - South Tehran Branch - Tehran, Iran
Abstract :
International successes, especially in the Olympic Games, have become significantly important to many
countries. Hence, the prediction can be better planning to gain this goal. Objectives. This study was conducted to
predict the success of the participating countries in the Tokyo Olympic Games and this it was done using smart
methods. Methods. This study was conducted in two stages of qualitative (determination of indicators) and quantitative
(collecting data on selected countries). In the first stage of the research, through a study of research background and
collecting of library data, a preliminary list of predictive indicators was identified. In the next step, semi-structured
and in-depth qualitative interviews as non-random purposive were conducted with four elites aware of the subject of
the research. The discussions continued until theoretical saturation. Results. According to the results of the research,
the United States, China, and England will be ranked first to third in these games. The Islamic Republic of Iran will
also be ranked 21 among the participating teams. Also, the coefficients of the predictive indicators of the rank of the
countries participating in the Tokyo 2020 Olympic Games were calculated. Olympic Hosting. GDP per capita and the
unemployment rate had the highest share in predicting countries, with 24.15%, 10.04% and 9.74%, respectively.
Conclusion. Using the theoretical model (PEST+S) and the neural network model, the countries’ sports policymakers
were enabled to use the identified indicators and components in their future planning to successfully participate in the
Olympics Games.
Keywords :
Tokyo 2020 , Prediction Indicators , Olympic Games , Multilayer Perceptron Networks
Journal title :
Annals of Applied Sport Science