2026-09-19T14:05:11Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1180802026-06-16T18:52:34Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

John Guttag. Brooks, Joel David Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-09-17T15:56:37Z 2018-09-17T15:56:37Z 2018 2018 http://hdl.handle.net/1721.1/118080 1051773251 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 95-100). Historically, much of sports analytics has aimed to find relationships between discrete events and outcomes. The availability of high-resolution event location and tracking data has led to many new opportunities in sports research. However, it is often challenging to apply machine learning to understand a particular aspect of a sport. These tasks typically require learning on high-dimensional data, scarce labels, and multiple interacting agents. In this thesis, we present applications of machine learning to derive insights from location data in soccer and basketball. In each case, we chose a data representation that allows the models to discover the importance of particular features and patterns. We demonstrate how new quantitive metrics can be derived from predictive models. We built a model that uses the location of passes in soccer to predict shots with an AUROC of 0.79. From this model we defined a novel metric that can evaluate the value of any pass. We also trained a model for predicting shot quality using non-shooting player trajectories in basketball. This allows us to calculate offensive contributions by player movement alone. We also developed an encoder-decoder architecture for learning a low-dimensional encoding of player and ball trajectories. When trained in an unsupervised setting, the model learned a representation that lends itself well to possession querying and clustering. We show that these clusters characterize different team styles on offense. Additionally, we trained this same architecture in a semi-supervised setting for set play classification. Compared to only training on labeled data, this framework improved six-way classification accuracy from 69% to 78%. While we chose specific applications of machine learning to sports data for this thesis, the methods described could generalize to other sports-related tasks or other real-world domains with tracking or multi-agent data. by Joel Brooks. Ph.D. 100 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Using machine learning to derive insights from sports location data Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="12b56d51-3aaa-4038-97c4-32af063879ab"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Using machine learning to derive insights from sports location data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Brooks, Joel David</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Historically, much of sports analytics has aimed to find relationships between discrete events and outcomes. The availability of high-resolution event location and tracking data has led to many new opportunities in sports research. However, it is often challenging to apply machine learning to understand a particular aspect of a sport. These tasks typically require learning on high-dimensional data, scarce labels, and multiple interacting agents. In this thesis, we present applications of machine learning to derive insights from location data in soccer and basketball. In each case, we chose a data representation that allows the models to discover the importance of particular features and patterns. We demonstrate how new quantitive metrics can be derived from predictive models. We built a model that uses the location of passes in soccer to predict shots with an AUROC of 0.79. From this model we defined a novel metric that can evaluate the value of any pass. We also trained a model for predicting shot quality using non-shooting player trajectories in basketball. This allows us to calculate offensive contributions by player movement alone. We also developed an encoder-decoder architecture for learning a low-dimensional encoding of player and ball trajectories. When trained in an unsupervised setting, the model learned a representation that lends itself well to possession querying and clustering. We show that these clusters characterize different team styles on offense. Additionally, we trained this same architecture in a semi-supervised setting for set play classification. Compared to only training on labeled data, this framework improved six-way classification accuracy from 69% to 78%. While we chose specific applications of machine learning to sports data for this thesis, the methods described could generalize to other sports-related tasks or other real-world domains with tracking or multi-agent data.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>