2026-09-23T17:19:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1003102022-01-13T07:54:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cynthia Rudin.
Choo, Christopher Ledesma Weisen
System Design and Management Program.
System Design and Management Program.
Massachusetts Institute of Technology. Engineering Systems Division
2015-12-16T15:54:53Z
2015-12-16T15:54:53Z
2015
2015
http://hdl.handle.net/1721.1/100310
931596281
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 77).
We discuss features contained in a machine learning software developed at MIT for professional car racing, to improve the predictions of track position changes within a race. We study pit crew performance and driver performance within selected races, and find that good combined performance for both correlates to better finish positions. Secondly, we classify tracks based on tire wear and the ratio of 2 versus 4 tire change decisions for pit stops. We find that a driver's performance in early stages of the race is similar to performance in later stages, suggesting that final race outcomes may be inferred from earlier stages of the race. Thirdly, we look at how tire change decisions vary from track to track depending on tire wear, caution periods, and stages of the race to understand how teams adapt their tire change strategies as each race progresses. We propose heuristics based on these observations that may be used to improve the software. Next, we test whether the construction of the machine learning dataset using similar and different track characteristics has a discernible impact on the predictive capability of the software. Our tests indicate that it may be preferable to aggregate different races together because there is no distinct difference in the results when compared to only selecting similar races. Finally, we cover ideas about how new features could be implemented in the software, and touch on other factors affecting pit stop strategy in the quest for better predictive capability in the software.
by Christopher Ledesma Weisen Choo.
S.M. in Engineering and Management
100 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
System Design and Management Program.
Real-time decision making in motorsports : analytics for improving professional car race strategy
Analytics for improving professional car race strategy
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="904c778f-7049-43f1-b062-6123a03a5967">
<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>Real-time decision making in motorsports : analytics for improving professional car race strategy</Title>
<Subtitle>Analytics for improving professional car race strategy</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Choo, Christopher Ledesma Weisen</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>Engineering Systems Division.</Keyword>
<Keyword>System Design and Management Program.</Keyword>
<Abstract>We discuss features contained in a machine learning software developed at MIT for professional car racing, to improve the predictions of track position changes within a race. We study pit crew performance and driver performance within selected races, and find that good combined performance for both correlates to better finish positions. Secondly, we classify tracks based on tire wear and the ratio of 2 versus 4 tire change decisions for pit stops. We find that a driver's performance in early stages of the race is similar to performance in later stages, suggesting that final race outcomes may be inferred from earlier stages of the race. Thirdly, we look at how tire change decisions vary from track to track depending on tire wear, caution periods, and stages of the race to understand how teams adapt their tire change strategies as each race progresses. We propose heuristics based on these observations that may be used to improve the software. Next, we test whether the construction of the machine learning dataset using similar and different track characteristics has a discernible impact on the predictive capability of the software. Our tests indicate that it may be preferable to aggregate different races together because there is no distinct difference in the results when compared to only selecting similar races. Finally, we cover ideas about how new features could be implemented in the software, and touch on other factors affecting pit stop strategy in the quest for better predictive capability in the software.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>