• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1271-1281
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• 2016

Wins above replacement (WAR) is one of the most widely used statistic among sabermatrics statistics that measure the ability of a batter in baseball. WAR has a great advantage that is to represent the attack power of the player and the base running ability, defensive ability as a single value. In this study, we proposed a hitter ability index using the sabermetrics statistics that can replace WAR based on Korea Baseball Record Data of the last three years (2013-2015). First, we calculated Batter ability index through the arithmetic mean method, the weighted average method, principal component regression and selected the method that had high correlation with WAR.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.143-146
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• 2016

본 연구에서는 대용량의 야구 데이터를 R 패키지인 googleVis를 이용하여 시각화하는 웹페이지를 구축하고, 버블 차트로 시각화하여 표현하였다. 웹페이지에서는 시각화하는 객체를 버블로 나타내며, 객체는 타자, 투수, 팀 3가지이다. 각 객체의 속성들을 버블 색상, 버블 사이즈, X-Y좌표, 연도에 설정함으로써 5차원으로 시각화하여 표현할 수 있게 한다. 웹페이지 기능 중 타임슬립 애니메이션을 사용하여 시간의 흐름에 따른 기록 변화를 한 눈에 관찰할 수 있으며, 선수 검색 기능을 통해 특정 선수들을 선택하여 비교 및 분석하는 것이 가능하다.

• 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 Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.165-172
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• 2014

The purpose of this paper is to identify whether the national baseball squad made up of the best players may get the outstanding results in the international competitions or not. Using Data Envelopment Analysis, the relative efficiency of players in Korean Baseball Organization is estimated as performance measure, and compared with the efficiency scores of national squad members. The paper focuses on the proper choice of DEA model in a baseball setting, thereby selecting the BCC model with non-discretionary variable. Two input variables(plate appearances and stolen bases to enforce) and three output variables(runs, on-base plus slugging and stolen base percentage) are used to evaluate the efficiency of the baseball players. Results showed that 22 players among 97 players were classified as efficient and 8 players among 12 national squad members were as efficient. These findings indicate a potential for DEA to be a major part of the analytical approaches in evaluating the relative efficiency of players.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.285-298
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• 2012

This research deals with a relationship between the performance of Korean professional baseball players and their annual salaries. It is based on the sabermetrics, which measures the performance of baseball batters in a refined way. We collect the records of batters of eight professional baseball clubs during the season 2009 and 2010. Then, we calculate every index of the sabermetrics. Principal component analysis is used for examining the relationship between those indexes of sabermetrics and annual salary for the next year. In general, batters who show higher performance get more salary. The result of this research can be useful in order to reach an agreement on salary between a player and his club partner.

• 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.

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• 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.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.65-71
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• 2020

Recently, the field of using data has begun to attract attention in professional sports. In the field of data utilization, in addition to the classic records obtained within the economy, secondary records that emphasize efficiency are also actively used. Therefore, in this study, we try to study the correlation with the pitcher's strikeout ability through the daily average humidity, which is data outside the competition. For this reason, referring to the daily average record of the area of the home base of 10 teams belonging to the KBO league and the auxiliary stadium, the top 5 in the win, hold, save section to grasp the characteristics of the starting pitcher and the rescue pitcher We analyzed K / 9 records for each person. Through the results of this study, we found a significant difference in the K / 9 record between the starting pitcher and the rescue pitcher, and we can expect to investigate the use of professional sports data and develop the industry in general.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1501-1509
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• 2017

Wins above replacement (WAR) is the most commonly used statistics of the sabermetrics that measure baseball players' abilities. The advantage of a WAR is that it enables to compare performances of players even though they have different roles such as pitcher and hitter. However, WAR is difficult to obtain with common records. Thus, a past studies (Lee and Kim, 2016) suggested the batting ability index to determine the ability of the batter focused on the sabermetrics statistics WAR. In this paper, we selected the best hitter with applying Korea baseball 2016 data based on a proposed model and then observed a total raking of others according to BAI. We are assured that BAI is very excellent statistics through comparing BAI and WAR which is in the spotlight in evaluating performances of players.