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The Pitching Problem: Proxy Measures for the Identification of Pitcher Fatigue
Author(s) -
Aloma Zachary A.,
Baller Daniel P.,
Hughes David W.,
Thomas Diana M.
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.09449
Subject(s) - proxy (statistics) , workload , computer science , identification (biology) , machine learning , botany , biology , operating system
Objective This study assessed the utilization of accessible records and metrics in developing proxy measures for fatigue in professional starting pitchers, and to understand the associated effects within lapses in short‐term and long‐term performance. Design and Methods Leveraging MLB Statcast AI, we pulled relevant data relating to pitches thrown during the 2015 – 2018 regular and postseasons. The available data was then analyzed in three separate iterations to better understand and isolate the impact of critical metrics. This iterative approach isolated the data based on the following subcategories: anecdotal game, seasonal, and aggregate sets. Results Through the prior detailed approach, we established the significance of three unaccounted for workload factors that represented suitable proxies for the identification of fatigue in starting pitchers. These factors included: strike‐to‐ball ratio, rest period, and a hit‐to‐strike ratio. These metrics represent a means of analyzing fatigue in relation to a pitcher’s performance through the course of a single game, and subsequently, inform optimal strategies as to bullpen management. Conclusions As a starting pitcher approaches a pitch count of thirty, management staffs can begin to utilize the prior mentioned proxy metrics as indicators of the impact of fatigue on a pitcher’s performance. These measures represent an alternative to traditional methods and an accessible solution.