A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches

In response to the NCAA’s 2023 rule change allowing softball pitchers to legally disengage from the playing surface while delivering a pitch, this study develops a framework to analyze and compare the legal drag, legal leap, and illegal replant pitching techniques. By developing a pose estimation al...

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Bibliographic Details
Main Author: Pendowski, Katia D.
Other Authors: Hosoi, Anette "Peko"
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/156649
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author Pendowski, Katia D.
author2 Hosoi, Anette "Peko"
author_facet Hosoi, Anette "Peko"
Pendowski, Katia D.
author_sort Pendowski, Katia D.
collection MIT
description In response to the NCAA’s 2023 rule change allowing softball pitchers to legally disengage from the playing surface while delivering a pitch, this study develops a framework to analyze and compare the legal drag, legal leap, and illegal replant pitching techniques. By developing a pose estimation algorithm and Recurrent Neural Network (RNN) for use on videos of real collegiate pitchers, we aim to distinguish physiological differences between these types of pitches and use our RNN to automatically detect illegal pitches. Our pose estimation results demonstrate the algorithm's effectiveness in extracting patterns from pitching videos. Key features such as the distance between the pitcher’s right knee and right toe, as well as the right toe x-position vs. time, emerge as crucial indicators for distinguishing legal and illegal pitches. The RNN achieved an accuracy of 71.4%, with a loss rate of 0.875. This framework offers a data-driven approach to softball pitching mechanics, providing valuable insights for researchers and coaches alike.
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spelling mit-1721.1/1566492024-09-04T03:03:46Z A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches Pendowski, Katia D. Hosoi, Anette "Peko" Massachusetts Institute of Technology. Department of Mechanical Engineering In response to the NCAA’s 2023 rule change allowing softball pitchers to legally disengage from the playing surface while delivering a pitch, this study develops a framework to analyze and compare the legal drag, legal leap, and illegal replant pitching techniques. By developing a pose estimation algorithm and Recurrent Neural Network (RNN) for use on videos of real collegiate pitchers, we aim to distinguish physiological differences between these types of pitches and use our RNN to automatically detect illegal pitches. Our pose estimation results demonstrate the algorithm's effectiveness in extracting patterns from pitching videos. Key features such as the distance between the pitcher’s right knee and right toe, as well as the right toe x-position vs. time, emerge as crucial indicators for distinguishing legal and illegal pitches. The RNN achieved an accuracy of 71.4%, with a loss rate of 0.875. This framework offers a data-driven approach to softball pitching mechanics, providing valuable insights for researchers and coaches alike. S.B. 2024-09-03T21:14:36Z 2024-09-03T21:14:36Z 2024-05 2024-07-10T17:33:33.727Z Thesis https://hdl.handle.net/1721.1/156649 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Pendowski, Katia D.
A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title_full A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title_fullStr A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title_full_unstemmed A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title_short A Framework for Analysis of Softball Pitching, as Applied to Legal and Illegal Pitches
title_sort framework for analysis of softball pitching as applied to legal and illegal pitches
url https://hdl.handle.net/1721.1/156649
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