A Study of Winning Percentage in the MLB Using Fuzzy Markov Regression

Seung Hoe Choi () and Seo-Kyung Ji
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Seung Hoe Choi: School of Liberal Arts and Science, Korea Aerospace University, Goyang 10540, Republic of Korea
Seo-Kyung Ji: Department of Smart Drone Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea

Mathematics, 2025, vol. 13, issue 6, 1-10

Abstract: In this study, we analyze the winning percentage of 16 teams that have participated in Major League Baseball since 1901. First, 69 variables for each team are classified into pitching, batting, and fielding using factor analysis, and then the effect of the newly classified variables on the winning percentage is analyzed. In addition, after expressing each team’s winning rate as a fuzzy number using a fuzzy partition, the linear relationship between the previous year and the next year using the fuzzy probability is investigated, and we estimate the fuzzy regression model and Markov regression model using the Double Least Absolute Deviation (DLAD) method. The proposed fuzzy model describes variables that affect the winning percentage of the next year according to the winning rate of the previous year. The estimated fuzzy regression model showed that the on-base percentage allowed by the pitcher and the on-base percentage of the batter had the greatest effect on the winning percentage.

Keywords: winning percentage; factor analysis; fuzzy partition; DLAD method; fuzzy Markov regression model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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