Suggesting pitches in Major League Baseball
| dc.contributor.advisor | Caramanis, Constantine | |
| dc.contributor.advisor | Dimakis, Alexandros G. | |
| dc.creator | Palomares, Javier, Jr. | |
| dc.creator.orcid | 0000-0001-6820-9781 | |
| dc.date.accessioned | 2020-05-18T20:21:06Z | |
| dc.date.available | 2020-05-18T20:21:06Z | |
| dc.date.created | 2019-12 | |
| dc.date.issued | 2020-03-26 | |
| dc.date.submitted | December 2019 | |
| dc.date.updated | 2020-05-18T20:21:06Z | |
| dc.description.abstract | Pitchers in Major League Baseball need to keep batters from anticipating the next pitch. They do this by selecting a good pitch type and zone to throw. Pitchers often make this selection haphazardly. In this paper, we present a machine learning model using the data from the PITCHf/x system installed in Major League stadiums to first predict good and bad pitches, and then to suggest the following pitch type to throw that will result in good outcomes | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/81283 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/8291 | |
| dc.language.iso | en | |
| dc.subject | Baseball | |
| dc.subject | MLB | |
| dc.subject | Neural network | |
| dc.subject | Machine learning | |
| dc.subject | Artificial intelligence | |
| dc.subject | XGBoost | |
| dc.subject | LSTM | |
| dc.title | Suggesting pitches in Major League Baseball | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Engineering |
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