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Baseball Informatics—From MiLB to MLB Debut

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Analytics Enabled Decision Making

Abstract

Drafted baseball players typically begin their professional baseball career with Minor League teams and are not guaranteed opportunities in the Major League. Accurate estimation of players’ likelihood to advance to the Major League debut can reduce the cost and increase value for both players and franchises. We mined both baseball performance stats and non-baseball data of players drafted from 2001 to 2010. We applied machine learning techniques to analyze and rank stats and data variables. We compared four sets of variable selections to train and validate our models, which predict the likelihood of a drafted player reaching the Majors. We fitted extreme gradient boosting, random forest, decision tree, and support vector machine to determine the high impact variables in the prediction. We successfully translated our model results into guidance for drafted players in the Minor League on what they should improve to increase their chances to play in the Major League.

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Acknowledgements

The authors thanked Drs. Paul Shapiro, Margrét Bjarnadóttir, and Teddy Helfers for their critiques on both baseball and writing.

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Correspondence to Woei-jyh Lee .

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lee, CH., Lee, Wj. (2023). Baseball Informatics—From MiLB to MLB Debut. In: Sharma, V., Maheshkar, C., Poulose, J. (eds) Analytics Enabled Decision Making. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-9658-0_5

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