year 8, Issue 4 (Winter 2020)                   Ann Appl Sport Sci 2020, 8(4): 0-0 | Back to browse issues page

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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)
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 ,
Abstract:   (432 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.
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  • 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 | Published: 2020/12/20 | ePublished: 2020/12/20

1. JS A. Principles of Forecasting: Springer; 2001.
2. DeLurgio SA. Forecasting principles and applications: Irwin Professional Publishing; 1998.
3. Lucas JW, Lovaglia MJ. Self-handicapping: Gender, race, and status. Curr Res Soc Psychol. 2005;10(15):234-49.
4. del Corral J, Prieto-Rodríguez J. Are differences in ranks good predictors for Grand Slam tennis matches? Int J Forecast. 2010;26(3):551-63. [DOI:10.1016/j.ijforecast.2009.12.006]
5. Song C, Boulier BL, Stekler HO. The comparative accuracy of judgmental and model forecasts of American football games. Int J Forecast. 2007;23(3):405-13. [DOI:10.1016/j.ijforecast.2007.05.003]
6. Armstrong JS. Methods to Elicit Forecasts from Groups: Delphi and Prediction Markets Compared. SSRN Electron J. 2008. [DOI:10.2139/ssrn.1153124]
7. Green BC. Building Sport Programs to Optimize Athlete Recruitment, Retention, and Transition: Toward a Normative Theory of Sport Development. J Sport Manage. 2005;19(3):233-53. [DOI:10.1123/jsm.19.3.233]
8. De Bosscher V, De Knop P, van Bottenburg M, Shibli S, Bingham J. Explaining international sporting success: An international comparison of elite sport systems and policies in six countries. Sport Manage Rev. 2009;12(3):113-36. [DOI:10.1016/j.smr.2009.01.001]
9. Klaassen FJGM, Magnus JR. Forecasting the winner of a tennis match. European J Oper Res. 2003;148(2):257-67. [DOI:10.1016/S0377-2217(02)00682-3]
10. Smith T, Schwertman NC. Can the NCAA Basketball Tournament Seeding be Used to Predict Margin of Victory? American Statistic. 1999;53(2). [DOI:10.2307/2685724]
11. Goldstein DG, Gigerenzer G. Fast and frugal forecasting. Int J Forecast. 2009;25(4):760-72. [DOI:10.1016/j.ijforecast.2009.05.010]
12. Grove SJ, Fisk RP, John J. The future of services marketing: forecasts from ten services experts. J Serv Mark. 2003;17(2):107-21. [DOI:10.1108/08876040310467899]
13. Wright G, Lawrence MJ, Collopy F. The role and validity of judgment in forecasting. Int J Forecast. 1996;12(1):1-8. [DOI:10.1016/j.ijforecast.2010.05.012]
14. Abrahart R, Kneale PE, See LM. Neural Networks for Hydrological Modeling. London: CRC Press; 2004. [DOI:10.1201/9780203024119]
15. Forrest D, Sanz I, Tena JD. Forecasting national team medal totals at the Summer Olympic Games. Int J Forecast. 2010;26(3):576-88. [DOI:10.1016/j.ijforecast.2009.12.007]
16. Derevenco P, Albu M, Duma E. Forecasting of top athletic performance. Rom j physiol physiol sci. 2002;39:57-62.
17. Seager R, Goddard L, Nakamura J, Henderson N, Lee DE. Dynamical Causes of the 2010/11 Texas-Northern Mexico Drought. J Hydrometeorol. 2014;15(1):39-68. [DOI:10.1175/JHM-D-13-024.1]
18. De Bosscher V, De Knop P, Van Bottenburg M, Shibli S. A Conceptual Framework for Analysing Sports Policy Factors Leading to International Sporting Success. Eur Sport Manage Q. 2006;6(2):185-215. [DOI:10.1080/16184740600955087]
19. Ning W, Kuan-jiang B, Zhi-fa Y. Analysis and forecast of Shaanxi GDP based on the ARIMA Model. Asian Agric Res. 2010;2(1812-2016-143365):34-41.
20. Iyer SR, Sharda R. Prediction of athletes performance using neural networks: An application in cricket team selection. Expert Syst Appl. 2009;36(3):5510-22. [DOI:10.1016/j.eswa.2008.06.088]
21. Grant A, Johnstone D. Finding profitable forecast combinations using probability scoring rules. Int J Forecast. 2010;26(3):498-510. [DOI:10.1016/j.ijforecast.2010.01.002]
22. Sotiriadou K, Shilbury D. Australian Elite Athlete Development: An Organisational Perspective. Sport Manage Rev. 2009;12(3):137-48. [DOI:10.1016/j.smr.2009.01.002]
23. Condon EM, Golden BL, Wasil EA. Predicting the success of nations at the Summer Olympics using neural networks. Comput Oper Res. 1999;26(13):1243-65. [DOI:10.1016/S0305-0548(99)00003-9]
24. Bernard AB, Busse MR. Who Wins the Olympic Games: Economic Resources and Medal Totals. Rev Econ Stat. 2004;86(1):413-7. [DOI:10.1162/003465304774201824]
25. Rivenburgh N. The Olympic Games, media and the challenges of global image making: university lecture on the Olympics [on line article], Barcelona: Centre d'Estudios Olímpics (UAB). International Chair in Olympism (IOC-UAB)2004.
26. Damisch L, Mussweiler T, Plessner H. Olympic medals as fruits of comparison? Assimilation and contrast in sequential performance judgments. J Exp Psychol Appl. 2006;12(3):166-78. [DOI:10.1037/1076-898X.12.3.166] [PMID]
27. Churilov L, Flitman A. Towards fair ranking of Olympics achievements: the case of Sydney 2000. Comput Oper Res. 2006;33(7):2057-82. [DOI:10.1016/j.cor.2004.09.027]
28. Kuper GH, Sterken E. Participation and performance at the London 2012 Olympics. SOM Res Rep. 2012;12006.
29. Strauss A, Corbin JM. Grounded theory in practice: Sage; 1997.

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