• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.731-741
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• 2014

We suggested an adjusted OPS and weighted adjusted OPS as indices to explain run productivity of teams using the slugging average and adjusted OBP from Korean professional baseball. First, we defined adjusted OBP by modifying currently used OBP. Next, we defined adjusted OPS as the sum of adjusted OBP and slugging average. We also defined weighted adjusted OPS as the weighted average of adjusted OBP and slugging average. Analysis of the data from all games in the regular seasons from 1982~2013 shows that adjusted OPS better explains runs than OPS. For 25 seasons out of 32 seasons, adjusted OPS explains runs better than OPS. Further, weighted adjusted OPS consisting of adjusted OBP (with weight 60%) and slugging average (with weight 40%) gives the best explanation of run productivity. Weighted adjusted OPS has been found to explain run productivity better than weighted OPS proposed in Kim (2012).

• 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.

• 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.

• 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.25 no.2
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• pp.349-356
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• 2014

In baseball, sabermetric batting statistics are used to compare an offensive performance of players. There exist dozens of sabermetric statistics, but baseball fans don't like the complexity of an abundance of measures. This paper provides a batting grade index (BGI) using principal component based on eight batting statistics. These are OPS, ISO, SECA, TA, RC, RC/27, wOBA and XR. We show that how standardized batting statistics are aggregated and weighted to arrive at a single composite measure of BGI. Also our result allows for segmentation of players into groups using the K-means clustering algorithm.