• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.15-21
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• 2002

We studied the relations between variables related to fan's preference in a area of pro-baseball which is the most popular one among pro-sports. The dependent variables are pro-baseball team fan's preference to both the team and the products of the company which has a pro-baseball team, while the independent variables are sex, age, and hometown of fans. Eight hypotheses are constructed with those variables. For testing them a questionnaire was used, whose reliability was checked using Kronbach Alpha. Subjects were 314 college students in a local city. For statistical analysis nonparametric tests like Mann Whitney test, Kruskal-Wallis test, etc were used. Useful and significant results among variables were obtained. Since these results have important implications about management of pro-sports teams and fans and fans Preference to products in a view of sports marketing management, more researches should be done continuously in the future in this area.

• Smart Media Journal
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• v.4 no.1
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• pp.52-57
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• 2015

Currently the gamification as marketing technique has appeared in the field non-related with game, applied to game elements. The alternate reality game pulled down the boundary between the real and the virtual has come out. New trend game has shown beyond the range of the existing game. The possibility has import into online baseball simulation game. The objective of this research is the exploratory approach to build GOMS model through three subjects to the famous pro-baseball manager game. After game playing session, three subjects' utterance with game experience was recorded. We built the goal hierarchy of goal in GOMS to pro-baseball manager game to analyze the vocabulary in three subjects' utterance. We try to find the possibility of gamification in alternate reality game interacting between the real and the virtual, and demolishing them.

• 한국체육학회지인문사회과학편
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• v.55 no.1
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• pp.221-233
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• 2016

The purpose of this study was to prove a development and initial validation of the korean version of coach-athlete relationship maintenance scale that originated from the work of Rhind & Jowett(2012) in pro baseball. The items were then administered to 132 Participants(29 coaches and 103 athletes) completed the questionnaires of the coach-athlete relationship maintenance in First preliminary investigation. Maximum likelihood estimate was used to identify the latent underlying structure. In order to verify the validity of Korean version of coach-athlete relationship maintenance was administered to an independent sample of 273 coaches and athletes. Pro baseball coach-athlete relationship maintenance is consisted of six factors(25 items) with conflict management, motivational, preventative, openness/assurance, support, and social network. SPSS18.0 and AMOS16.0 were used to analyze the exploratory factor analysis, confirmatory factory analysis and internal consistency, test-retest with bootstrapping using of the data in this study. The results of the pro baseball coach-athlete relationship maintenance scale had six factors with 25 items, and each six factor was positively correlated. Overall, this study verified pro baseball coach-athlete relationship maintenance questionnaire. Thus, suggest that path of comparing the differences between the first division and farm team by using the test of the structural model invariance across the groups.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1115-1115
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• 2016

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.865-874
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• 2015

This research is to comprehensively evaluate offensive abilities of baseball players who are expected to produce as many runs as possible by their hitting and running. To this end, we establish a simulation program to obtain the so-called scoring index of an individual player. The scoring index of a player is defined as an expected number of runs scored by an imaginary team that is composed of nine copies of the player. As a simulation input, we use 2014 season data of Korean pro-baseball. As a result, we present the scoring indices of top 10 players, 9 Korean pro-baseball teams, and overall 2014 season. The scoring index can serve as a comprehensive evaluation of offensive ability of a player or a team, selection of players for a (national) team or for a starting line-up, estimation of player's worth, and so on.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1585-1592
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• 2016

Estimation of winning percentage in baseball has always been particularly interesting to many baseball fans. We have fitted models including linear regression and Pythagorean formula to the Korean baseball data of seasons from 1982 to 2015. Using RMSE criterion for both the linear formula and the Pythagorean formula, we compared two models in predicting the actual winning percentage. Pythagorean expectation is superior to linear formula when there is either high or low winning percentage. Two methods yield very similar efficiencies when the actual winning percentage is about 50%. To understand and use for estimating winning percentage, it is easier linear formula as estimated equations.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.653-659
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• 2015

The Pythagorean formula for baseball postulated by James (1982) indicates the winning percentage as a function of runs scored and runs allowed. However sometimes, the Pythagorean formula gives a less accurate estimate of winning percentage. We use the records of team vs team historic win loss records of Korean professional baseball clubs season from 2005 and 2014. Using assumption that the difference between winning percentage and pythagorean expectation are affected by unusual distribution of runs scored and allowed, we suppose that difference depends on mean, standard deviation, and coefficient of variation of runs scored per game and runs allowed per game, respectively. In conclusion, the discrepancy is mainly related to the coefficient of variation and standard deviation for run allowed per game regardless of run scored per game.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.693-698
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• 2003

Every baseball game generates various records and on the basis of those records, win/lose prediction about the next game is carried out. Researches on win/lose predictions of professional baseball games have been carried out, but there are not so good results yet. Win/lose prediction is very difficult because the choice of features on win/lose predictions among many records is difficult and because the complexity of a learning model is increased due to overlapping factors among the data used in prediction. In this paper, learning features were chosen by opinions of baseball experts and a heuristic function was formed using the chosen features. We propose a hybrid model by creating a new value which can affect predictions by combining multiple features, and thus reducing a dimension of input value which will be used for backpropagation learning algorithm. As the experimental results show, the complexity of backpropagation was reduced and the accuracy of win/lose predictions on professional baseball games was improved.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.51-60
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• 1995

Given an $n \times p$ data matrix, if we add the $p_s$ variables somewhat different nature than the p variables to this matrix, we have a new $n \times (p+p_s)$ data matrix. Because of these $p_s$ variables, the traditional principal component analysis can't provide its efficient results. In this study, to improve this problem we review the supplementary principal component analysis putting $p_s$ variables to supplementary variable. This technique is based on the algebraic and geometric aspects of the traditional principal component analysis. So we provide a type of statistical data analysis for the records of eight teams and fourteen fields of the 1982-1992 Korean Pro Baseball Data based on the supplementary principal component analysis and the traditional principal component analysis. And we compare the their results.