This study has been conducted to develop methods and techniques for the analysis of data related to baseball performance using the winning and losing games. The purposes of the study were to examine differences of athlete performance for semi playoff, playoff, and Korean professional baseball series and to develop optimal forecasting model for the short term series. Data used in the study were taken from Korean professional baseball association. Three data sets including semi play off from 1982 to 2012, play off from 1989 to 2012, and Korean series from 1982 to 2012 were used. To compare athlete performance by winning and losing games for short-term series t-test was applied. This study created new parameters by weighted value through the equalization process to calculate skill related variables as a predicted variable. Three predicted models such as discriminant, binary logistic regression and artificial neural network models were developed to clarify the suggested models. The results showed that the number of significant parameters increased as the series continued. In particular, a variable related to error was added as a significant variable at the Korean Series. A third base hit in the play-off and a second base hit were also added as significant parameters in the play-off and the Korean series, respectively. In addition, W/L a major variables affecting a given technology area, the pitching PO, PO, the inertia, KS, the pitching, respectively. An artificial neural network model was finally selected with the highest accuracy and lowest input of estimated parameters in the semi play-off. In the play-off, artificial neural network model that applied technical area parameters by specialist criteria had better accuracy rate than two others. In the Korean series, artificial neural network model that created estimation parameters by applying all parameters was chosen as the final model. When the overall accuracy level of semi-play off, play off and Korean series was figured out, binary logistic regression model had higher accuracy of classification than discriminant model, but artificial neural network model had the higher accuracy of classification than binary logistic regression model.
