• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.663-676
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• 2016

In Korean professional baseball the championship team of the year is determined by the four series of games: semi-semi-playoff, semi-playoff, playoff and korean series. To the top 5 teams in a regular season privileges are given to play the games at post season. At semi-semi playoff the winner of two teams which are ranked at 4th and 5th place in the regular season can advance to the game of semi playoff. The winner at semi playoff advances to the playoff to play with the second place team in the regular season. Finally, the championship team is to be determined in the Korean series between the winner of the playoff and the first ranked team in the regular season. We propose methods of how to calculate the winning probabilities of each of high ranked 5 teams advancing to Korean series. From our proposed methods we can estimate the championship probabilities of each of high ranked 5 teams advancing to the Korean series only if we know the winning probabilities between two teams in the regular season or the post season.

• 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.

• 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.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.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.325-328
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• 2000

프로야구경기의 승패 예측의 문제는 그리 쉬운 일이 아니다. 왜냐하면 경기에 영향을 미치는 요소가 무한하기 때문이다. 예를 들어, 경기당일의 선수들의 컨디션이나 사기, 경기당일의 날씨, 구장요건, 상대팀에 대한 심리적 요인등 사전에 경기영향을 미치는 요소가 무한하다. 본 연구실에서는 과거 경기기록 자료를 기반으로 유용한 규칙을 찾아내어 분류트리를 만들어 학습하는 ID3 알고리즘을 프로야구 승패예측 시스템 구성에 사용하여 보았으나, 이산적인 자료의 처리로 인해 연속적인 경기자료를 고려하지 못하는 문제로 예측율이 더이상 향상되지 않았다. 따라서, 본 논문에서는 휴리스틱 방법을 이용한 경기전 예측과 경기중 예측을 이닝별 득점으로 세분화하여, 실제 경기상황을 고려한 일반적인 예측모형을 만들어 예측율을 향상시키고자 한다. 향후에는 더욱 세분화시켜 Case-based에 의한 예측을 하고자 한다.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1283-1294
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• 2016

We studied fielding indices to explain runs lost for Korean professional baseball teams, successively motivated by OPS and weighted OPS obtained by combining on-base percentage and slugging average that can adequately explain the run productivity of teams. We considered several combined indices made by combining fielding indices highly correlated with the runs lost of teams. Data analysis from all games in the regular seasons of 1982-2015 shows that weighted adjusted WPH 2 (defined as weighted average of adjusted WHIP and number of home runs allowed per inning) best explains runs lost. Weighted adjusted WPH 2 consisting of adjusted WHIP (with weight 34%) and number of home runs allowed per inning (with weight 66%) was found to be optimal weighted adjusted WPH 2 having correlation coefficient 0.95362 with average runs lost per game. This result is an improvement of the result of the index obtained in Kim and Kim (2015a). Analysis by chronological periods provides results that are not much different. Also we made a list of top 10 pitchers for each of the recent three years, based on the obtained index.

• 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 the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.99-110
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• 2016

This study examined the factors affecting the team identification of professional baseball fans. The factors were divided into the fan factor, game factors, and other factors. For the analysis, two investigators visited stadium three times in total and a total number of 297 spectators were sampled using a convenience sampling method from three baseball teams. For the fan factor, a fan who began supporting his/her team from childhood or after childhood was used as a dummy variable. The interaction effects between the fan factor and other variables were investigated to offer a stereoscopic understanding about the role of kids marketing. In addition, three game factors and four non-game factors were analyzed. The results regarding fan variable and interaction effects were obtained. Fans from their childhood have much stronger team identification, and show interaction effects with the players. Regression analysis revealed player, promotion and fan service, price, and regional connection to have positive relations with team identification. This study is especially meaningful in a sense that it has proposed positive results regarding marketing to children, and the results will contribute to both the academic field and the industry.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.485-492
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• 2014

In baseball, the traditional measure of pitchers are wins and ERA. But these statistics are influenced by luck or team power. So sabermetrician proposes a number of indicators that predict future performance. We determine a new measure, which we call pitching grade index (PGI) that efficiently summarizes a pitcher's performance on a numerical scale using principal components analysis. The PGI statistic can often be useful to assessing a pitcher's individual contribution. Also K-means clustering algorithm are used for segmentation of players into groups.