Graphic displays of MLB pitching mechanics and its evolutions in PITCHf/x data
Fushing Hsieh, Kevin Fujii, Tania Roy, Cho-Jui Hsieh, Brenda McCowan

TL;DR
This paper introduces three novel graphic visualization methods to analyze and compare MLB pitchers' mechanics and their evolution over seasons using PITCHf/x data, aiding injury prevention and expert analysis.
Contribution
It develops three exclusive graphic displays to visualize universal patterns, seasonal changes, and individual idiosyncrasies in pitching mechanics based on data-driven entropy matrices.
Findings
Universal patterns align with physical laws and systemic characteristics.
Graphic displays differentiate pitcher clusters and individual evolutions.
Visualizations facilitate understanding of pitching mechanics and their changes.
Abstract
Systemic and idiosyncratic patterns in pitching mechanics of 24 top starting pitchers in Major League Baseball (MLB) are extracted and discovered from PITCHf/x database. These evolving patterns across different pitchers or seasons are represented through three exclusively developed graphic displays. Understanding on such patterned evolutions will be beneficial for pitchers' wellbeing in signaling potential injury, and will be critical for expert knowledge in comparing pitchers. Based on data-driven computing, a universal composition of patterns is identified on all pitchers' mutual conditional entropy matrices. The first graphic display reveals that this universality accommodates physical laws as well as systemic characteristics of pitching mechanics. Such visible characters point to large scale factors for differentiating between distinct clusters of pitchers, and simultaneously lead…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Music Technology and Sound Studies · Data Visualization and Analytics
