• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.1-10
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• 2015

This paper offers some long term perspective on what has been happening to some baseball statistics for Korean professional baseball. The data used are league summaries by year over the period 1982-2013. For the baseball statistics, statistically significant positive correlations (p < 0.01) were found for doubles (2B), runs batted in (RBI), bases on balls (BB), strike outs (SO), grounded into double play (GIDP), hit by pitch (HBP), on base percentage (OBP), OPS, earned run average (ERA), wild pitches (WP) and walks plus hits divided by innings pitched (WHIP) increased with year. There was a statistically significant decreasing trend in the correlations for triples (3B), caught stealing (CS), errors (E), completed games (CG), shutouts (SHO) and balks (BK) with year (trend p < 0.01). The ARIMA model of Box-Jenkins is applied to find a model to forecast future baseball measures. Univariate time series results suggest that simple lag-1 models fit some baseball measures quite well. In conclusion, the single most important change in Korean professional baseball is the overall incidence of completed games (CG) downward. Also the decrease of strike outs (SO) is very remarkable.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.441-453
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• 2008

We analyzed Korean professional basketball and baseball players salary under the assumption that it depends on the personal records and contribution to the team in the previous year. We extensively used data visualization tools to check the relationship among the variables, to find outliers and to do model diagnostics. We used multiple linear regression and regression tree to fit the model and used cross-validation to find an optimal model. We check the relationship between variables carefully and chose a set of variables for the stepwise regression instead of using all variables. We found that points per game, number of assists, number of free throw successes, career are important variables for the basketball players. For the baseball pitchers, career, number of strike-outs per 9 innings, ERA, number of homeruns are important variables. For the baseball hitters, career, number of hits, FA are important variables.