• 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.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.201-210
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• 2001

스포츠에서 통계적 개념이 부적절하게 사용되고 있는 몇 가지의 경우들을 살펴보고, 이를 바로잡기 위한 방안을 제시한다. 먼저 야구의 경우를 자세히 살펴본 뒤 축구와 권투의 경우에 관해서도 논의한다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.357-363
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• 2014

OPS is a sabermetric baseball statistic calculated as the sum of a player's on base percentage (OBP) and slugging percentage (SLG). One of the frequently cited problem with OPS is that OPS gives equal weight to its two components, OBP and SLG. In fact, OBP contributes significantly more to scoring runs than SLG does. This paper provides some exploration into the correct weighting of OBP to SLG when adding the two together. By correlating different coefficients of OBP to runs scored per game, the weighted OPS that weighting OBP 56% in two place more than SLG produced the highest correlation. We found that the weight of OBP increases as RPG increases. Also we suggest the linear regression equation of the best OBP coefficient against RPG.

• Journal of Industrial Convergence
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• v.19 no.4
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• pp.17-21
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• 2021

Recently, in the field of sports, the use of data in conducting games, planning seasons, and operating teams has increased significantly. Also, in order to develop better players, it has become necessary to use data to accurately analyze their performance. Therefore, in this study, various data about rookie players was collected and pre-processed in order to analyze and visualize their performance. Additionally, an analysis was conducted to determine at least how many opportunities should be given to foster rookie players. Then, a data analysis method was presented for nurturing athletes by using data in the field of sports. It is expected that this study will contribute to fostering rookie players by utilizing data.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.78-98
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• 2018

On the 855 pure Korean commercial fictional movies, excluding diversity films, released in Korea from 2004 to August 2017, I conducted deciles distribution analysis of box office performance of those movies and average box office performance of directors, producers and lead actors who involved in making them. Deciles distribution analysis of average box office performance might be helpful to predict their next box office performance of newly produced Korean movies and to evaluate their contribution to box office performance. In baseball, the various index such as winning rate, on-base percentage, slugging percentage, stolen base percentage, battling average, earned run average is used for predicting and reviewing of professional players. In this study, I evaluate the script's narrative quality by the indirect method of insight and judgment of creative manpower involved in making the movies. For the more productive prediction, direct statistical analysis method on the narrative of the script needs to develop. Time series analysis is required to evaluate the rise and fall of creative manpower and network analysis is also necessary to see the interaction among creative people.

• 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.

• 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.