year 8, Issue 4 (Winter 2020)
Ann Appl Sport Sci 2020, 8(4): 0-0 |
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Fazlollahi P, Afarineshkhaki A, Nikbakhsh R. Predicting the Medals of the Countries Participating in the Tokyo 2020 Olympic Games Using the Test of Networks of Multilayer Perceptron (MLP). Ann Appl Sport Sci 2020; 8 (4)
URL: http://aassjournal.com/article-1-799-en.html
URL: http://aassjournal.com/article-1-799-en.html
1- Department of Sports Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
2- Department of Sports Science, Islamic Azad University, South Tehran Branch, Tehran, Iran ,akbarafarinesh@gmail.com
2- Department of Sports Science, Islamic Azad University, South Tehran Branch, Tehran, Iran ,
Abstract: (4093 Views)
Background. 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.
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.
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APPLICABLE REMARKS
APPLICABLE REMARKS
- The results of this research can be useful for the planning of the audiences of the study such as the Ministry of Sports, the National Olympic Committee, federations, boards, education and even professional clubs and even sponsors of sports communities.
- Considering that the population participating in the Olympic Games is of a specific age group, planning can focus on the age group and can be considered in future estimates.
- Given that this research uses a theoretical model (PEST+S) and a neural network model, sport policymakers in countries can use the identified indicators and components in their future planning using this research with a scientific approach to have a more fruitful participation in the Olympic Games.
- Other practical results of the research can be to modify expectations and avoid frustration with sports fans concerning the existing potentials.
Type of Study: Original Article |
Subject:
Sport Management and its related branches
Received: 2019/09/24 | Accepted: 2020/01/13
Received: 2019/09/24 | Accepted: 2020/01/13
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