UCLA Data Science 102
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2023
In an ongoing exploration of Major League Baseball (MLB) dynamics, the focus is on the critical relationship between a pitcher's age and their on-field performance. Recognizing that pitchers, despite having some of the longest active playing periods in sports, can see a dramatic fluctuation in their performance over time, the project attempts to illuminate patterns that could guide team decisions. By developing a unique metric system, the project evaluates performance based on critical baseball statistics, including Earned Run Average (ERA), Innings Pitched (IP), and Win-Loss Percentage (W-L%). The motivation for this research was stimulated by notable contracts like that of Steven Strasburg, and the financial implications such decisions can have on teams. Drawing from a diverse sample of players whose careers spanned the 21st century, the intent is to craft a predictive model that can serve as a resourceful tool for teams and enthusiasts alike.
(Metric system criteria)
(Graphical representation of metrical results)
Project link: https://docs.google.com/document/d/1PZsDA56mTV-SHA0VUyxCRUNJjKMeoo-hCo-Y1XFwc0E/edit
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