• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.915-930
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• 2011

There are a variety of methods to model game results and many methods exist for the case of paired comparison data. Among them, the Bradley-Terry model is the most widely used to derive a latent preference scale from paired comparison data. It has been applied in a variety of fields in psychology and related disciplines. We applied this model to the data of Korean Baseball League. It shows that the loglinear Bradley-Terry model of defensive rate and save is optimal in terms of AIC. Also some categorical characteristics, such as east team and west team, existence of golden glove winning players, team(s) with seasonal pitching leader, and team(s) with home advantage, influenced the game result significantly. As a result, the suggested models can be further utilized to predict future game results.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.205-218
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• 2007

It is our greatest concern of Korean team to enter round of 16. The past football results are the most important data for making a prediction. And we know that the home advantage is also considerable factor and there are many unobservable factors. However, there are few matches between the participants and even not the results for some nations. To overcome this difficulty, we model the network of results and consider other factors. We predict 2006 Germany World Cup results using modified the Bradley-Terry model.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1137-1146
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• 2010

There are a lot of methods to predict the result of a game and many forecasting researches have been studied. Among many methods, if a statistical model including some realistic random variables is used to forecast, more accurate prediction could be expected than any others. In this work, Bradley-Terry model is considered to predict results of 2010 South Africa World Cup games via paired comparison method. This prediction model includes some random variables which affect the results of games. The worth parameters for each country in this model are convergence values obtained by using Newton-Raphson algorithm. With this model, we can forecast top 16 among 32 countries and up to who will win the victory. Final results of 2010 South Africa World Cup games are compared with this prediction and discuss further works.