• 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).

• 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.