• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.123-135
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• 2021

The data processing of this study is focused on keywords related to 'Corona 19 and professional baseball' and 'Corona 19 and professional baseball no spectators', using text mining and social network analysis of textom program to identify problems and view quality. It was used to set the variable of For quantitative analysis, a questionnaire on viewing quality was constructed, and out of 270 survey respondents, 250 questionnaires were used for the final study. As a tool for securing the validity and reliability of the questionnaire, exploratory factor analysis and reliability analysis were conducted, and IPA analysis (importance-satisfaction) was conducted based on the questionnaire that secured validity and reliability, and the results and strategies were presented. As a result of IPA analysis, factors related to the image (image composition, image coloration, image clarity, image enlargement and composition, high-quality image) were found in the first quadrant, and the second quadrant was the game situation (support team game level, support player game level, star). Player discovery, competition with rival teams), game information (match schedule information, player information check, team performance and player performance, game information), interaction (consensus with the supporting team), and some factors appeared. The factors of commentator (baseball-related knowledge, communication ability, pronunciation and voice, use of standard language, introduction of game-related information) and interaction (real-time communication with the front desk, sympathy with viewers, information exchange such as chatting) appeared.

• Journal of Korea Fair Competition Federation
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• no.108
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• pp.40-47
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• 2004

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.134-141
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• 2023

Objectives : The purpose of this study was investigating the differences in temperament, personality, and cognitive function among athletes and non-athletes, as well as differences within athlete groups participating in different-paced sports like baseball and basketball. Methods : A total of 57 professional basketball players, 51 professional baseball players, and 44 non-athletes subjected to temperament and characteristics inventory assessments and computerized neurocognitive function test. One-way analysis of variance (ANOVA) was employed to analyze the average differences in demographic characteristics, temperament, personality traits, and cognitive functions among the three groups, followed by Bonferroni post hoc tests. Comparisons between starters and non-starters within the athlete groups were conducted using the Mann-Whitney U test. Results : In the analysis of temperament, the basketball and baseball player groups exhibited higher reward dependence and persistence compared to the control group. Additionally, in the assessment of personality traits, both basketball and baseball player groups scored higher in self-directedness and cooperativeness compared to the control group, whereas self-transcendence scores were lower. In cognitive ability assessments, baseball and basketball players outperformed the control group in emotional perception tests. Both baseball and basketball players showed lower card movement counts compared to the control group. Conclusions : This study compared the differences in temperament, personality, and cognitive abilities between professional basketball and baseball players and non-athletes. These results provide valuable insights into the temperament, personality, and cognitive abilities of professional athletes, contributing important information for athlete development and coaching goals in the future.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.493-499
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• 2014

The Pythagorean won-loss formula postulated by James (1980) indicates the percentage of games as a function of runs scored and runs allowed. Several hundred articles have explored variations which improve RMSE by original formula and their fit to empirical data. This paper considers a variation on the formula which allows for variation of the Pythagorean exponent. We provide the most suitable optimal exponent in the Pythagorean method. We compare it with other methods, such as the Pythagenport by Davenport and Woolner, and the Pythagenpat by Smyth and Patriot. Finally, our results suggest that proposed method is superior to other tractable alternatives under criterion of RMSE.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.635-645
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• 2017

In this paper, we propose batting index prediction models of 2017. Due to the insufficiency of KBO pitchers data, batting index prediction models of 2016 has been developed based on elected eight batting index collecting the past three years data of MLB and KBO. It has been found that this prediction model fits well to both MLB and KBO, and the KBO model fits better than MLB in some cases. Using these prediction models, we analyzed and compared 2016's estimated values for the batting index of MLB and KBO. With the relation results between batting index prediction and batter's age for MLB and KBO, it can be determined that there is no relationship between the significant batting index and ages.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.119-126
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• 2023

In this paper, a new model was presented to objectively evaluate the defense ability of defenders in Korean professional baseball. In the proposed model, using Korean professional baseball game data from 2016 to 2019, a representative defender was selected for each team and defensive position to evaluate defensive ability. In order to evaluate the defense ability, a method of calculating the defense range for each position and dividing the calculated defense area was proposed. The defensive range for each position was calculated using the Convex Hull algorithm based on the point at which the defenders in the same position threw out the ball. The out conversion score and victory contribution score for both infielders and outfielders were calculated as basic scores using the defensive range for each position. In addition, double kill points for infielders and extra base points for outfielders were calculated separately and added together.

• The Journal of Bigdata
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• v.7 no.1
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• pp.49-61
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• 2022

In sports, the proportion of data analysis in team management such as team strategy planning and marketing is increasing. In KBO(Korea Baseball Organization) league, in particular, plans such as recruiting players and fostering players are established to devise team strategies for the next year, such as FA and trade, at the end of a season. For these reasons, it is very important to predict players' performance for the next year. In this study, the target was limited to only the batter and tried to find out how to predict whether the performance of the next year will improve. As a standard record for rising and falling, OPS(On-Base Plus Slugging), which is easy to calculate and has a high relationship with team score, was used. In this study, 40 years of regular season data from 1982 to 2021 were used as data, and 11 machine learning classification models were used as experimental methods. Predicting the rise and fall of OPS, RBF SVM, Neural Net, Gaussian Process, and AdaBoost were more accurate than other classification models, and age did not significantly affect accuracy.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1065-1074
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• 2012

The purpose of this paper is to statistically analyze the effects of on-base and slugging ability on the run productivity in Korean professional baseball. In Section 2, we have investigated the OPS (On-base percentage Plus Slugging average) and introduced new indices of batting ability by modifying the OPS. In Section 3, we have examined the correlation which the batting average, on-base percentage, slugging average, IsoP (Isolated Power), OPS and the indices introduced in Section 2 have with the average runs per game, using the data from all the games of the regular seasons in 2007~2011. In addition, by generalizing the OPS and the indices introduced in Section 2, we have analyzed the correlation of the indices with various weights between the average runs per game. As a result, the weighted OPS consisting of on-base percentage (with weight 57%) and slugging average (with weight 43%) has been found to give the best explanation of the run productivity.