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Model Ağacı Kullanımı Asya Oyunu Milli Takım Sıralama tahmin

Predicting National Team Rank in Asian Game Using Model Tree

Journal Name:

  • Pamukkale Spor Bilimleri Dergisi

Publication Year:

  • 2011

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Many people are interested in predicting the outcome of sporting contests. However, one of the reasons that sport attracts so much attention is that the outcome of a contest is not perfectly predictable. In this paper we tried to predict the success of nations at the Asian Games through macro-economic, political, social and cultural variables. we used the information of variables include urban population, Education Expenditures, Age Structure, GDP Real Growth Rate, GDP Per Capita, Unemployment Rate, Population, Inflation Average, current account balance, life expectancy at birth and Merchandise Trade for all of the participating countries in Asian Games from 1970 to 2006 in order to build the model and then this model was tested by the information of variables in 2010. The prediction is based on the number of golden medals acquired each country. In this research we used WEKA software that is a popular suite of machine learning software written in Java. Japans's stability is entirely consistent with it's variables in all of the courses held. The value of correlation coefficient between the predicted and original ranks is 75.5%. We tried to design the pattern that: To improve sport in each country and get the better international ranks according to it's facilities, potential sources and the comparison with other countries. Managers and planners take the appropriate policies and determine long-term, middle-term and short-term goals in sport according to political, cultural, economic and social factors.
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  • 3
22-36
  • Turkish

REFERENCES

References: 

1. Abrevaya, J. (2002). Ladder tournaments and underdogs: lessons from professional bowling. Journal of Economic Behavior and Organization, 47, pp: 87-101.
2. Anderson, p., Edman, j., & Ekman, M. (2005). Predicting the world cup 2002 in soccer: performance and confidence of experts and non-experts. International Journal of Forecasting, 21, pp: 565-576.
3. Bergsgard, N. A., Houlihan, B., Mangset, P., Nodland, S. I., & Rommetveldt, H. (2007). Sport policy. A comparative analysis of stability and change. London: elsevier.
4. Bernard, A. B., & busse, M. R. (2004). Who wins the Olympic Games: economic resources and medal totals. Review of Economics and Statistics, 86, pp: 414-417.
5. Bolton, R., & Chapman, R. (1986). Searching for positive returns at the track: a multinomial logit model for handicapping horse races. Management Science, 32,
pp: 1040-1060.
6. Boulier, B., & Stekler, H. (1999). Are sports seedings good predictors? An evaluation. International Journal of Forecasting, 15, pp: 83-91.
7. Boulier, B., & Stekler, H. (2003). Predicting the outcomes of national football league games. International Journal of Forecasting, 19, pp: 257-270.
8. Cain, M., Law, D., & Peel, D. (2000). The favourite-longshot bias and market efficiency in uk football betting. Scottish Journal of Political Economy, 47, pp: 25¬36.
9. Caudill, S. (2003). Predicting discrete outcomes with the maximum score estimator: the case of the ncaa men's basketball tournament. International Journal of Forecasting, 19, pp: 313-317.
10. Caudill, S., & Godwin, N. (2002). Heterogeneous skewness in binary choice models: predicting outcomes in the men's ncaa basketball tournament. Journal of Applied Statistics, 29, pp: 991-1001.
11. Clarke, S., & Dyte, D. (2000). Using official ratings to simulate major tennis tournaments. International Transactions in Operational Research, 7, pp: 585-594.
12. Condon, E. M., Golden, B. L. & Wasil, E. A. (1999). Predicting the success of nations at the summer Olympics using neural networks. Computers & Operations Research, 26, pp: 1243- 1265.
13. Dixon, M., & Coles, S. (1997). Modelling association football scores and inefficiencies in the football betting market. Applied Statistics, 46, pp: 265-280.
14. Dyte, D., & Clarke, S. (2000). A ratings based poisson model for world cup soccer simulation. The Journal of the Operational Research Society, 51(8), pp: 993-998.
15. Forrest, D., & Simmons, R. (2000). Forecasting sport: the behavior and performance of football tipsters. International Journal of Forecasting, 16, pp: 317¬331.
16. Forrest, D., Goddard, J., & Simmons, R. (2005). Odds-setters as forecasters: the case of english football. International Journal of Forecasting, 21, pp: 551-564.
17. Forrest, D., sanz, Tena, J.D. (2010). Forecasting national team medal totals at the summer olympic games, International Journal of Forecasting, 26, pp: 576-588.
18. Goddard, J. (2005). Regression models for forecasting goals and match results in association football. International Journal of Forecasting, 21, pp: 331-340.
19. Goddard, J., & Asimakopoulos, I. (2004). Forecasting football results and the efficiency of fixed-odds betting. International Journal of Forecasting, 23, pp: 51¬66.
20. Green, M., & Houlihan, B. (2005). Elite sport development. Policy learning and political priorities. London and new york: routledge.
21. Holmes, G., hall, M., & Frank. F. (1999). Generating rule sets from model trees, proceeding ai '99 of the 12th australian joint conference on artificial intelligence: advanced topics in artificial intelligence springer-verlag London.
22. Klaassen, F., & Magnus, J. (2003). Forecasting the winner of a tennis match. European Journal of Operational Research, 148, pp: 257-267.
23. Lebovic, J., & Sigelman, L. (2001). The forecasting accuracy and determinants of football rankings. International Journal of Forecasting, 17, pp: 105-120.
24. Leitner, CH., Zeileis, A.,& Hornik, K. (2010). forecasting sports tournaments by ratings of (prob)abilities: a comparison for the euro 2008, International Journal of Forecasting, 26, pp: 471-481
25. Leitner, CH., Zeileis, A.,& Hornik, K. (2010). Forecasting the winner of the FIFA world cup 2010, research report series / department of statistics and mathematics, 100. Institute for statistics and mathematics, wu vienna university of economics and business, vienna.
26. Oakley, B., & Green, M. (2001). The Production of Olympic Champions: International Perspectives on Elite Sport Development System. European Journal for Sport Management, 8, pp: 83-105.
27. Ould-Ahmed-Vall, E., Woodlee, J., Yount, C., Doshi, K. A., & Abraham, S.. (2007). Using model trees for computer architecture performance analysis of software applications. In Proceedings of IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS'07).
http://pjss. pau. edu.tr
Pamukkale Journal of Sport Sciences
35
Hematinezhad et al. 2011;2(3): 22-36
28. Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, pp: 343-348.
29. Rue, H., & Salvesen, O. (2000). Prediction and retrospective analysis of soccer matches in a league. The statistician, 49, Part 3, pp: 399-418.
30. Smith, T., & Schwertman, N. (1999). Can the ncaa basketball tournament seeding be used to predict margin of victory? The american statistician, 53(2), pp: 94-98.
31. Song. CH., Boulier, B. L., & Stekler, H. O. (2007). The comparative accuracy of judgmental and model forecasts of american football games, International Journal of Forecasting, 23, pp: 405-413.
32. Strumbelj, E., & Sikonja, M. R. (2010). Online bookmakers' odds as forecasts: the case of european soccer leagues, International Journal of Forecasting, 26, pp: 482¬488.
33. Wang, Y., Witten, I.H. (1997). Induction of model trees for predicting continuous lasses. In: Proceedings of the Poster Papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
34. Witten, I.H., & frank, E. (2005). Data mining: Partical mashine learning tools and techniques. 2nd edition. Morgan kaufmann, sanfrancisco, CA.
35. Zhang, D., Tsai, J.J.P., (2007). Advances in machine learning applications in software engineering, The United States of American by Idea Group Publishing (an Important of Idea Group Inc).

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