Real-time decision making in motorsports : analytics for improving professional car race strategy
Name
931596281-MIT.pdf
Description
Full printable version
Size
5.19 MB
Format
Adobe PDF
Checksum (MD5)
d85a763989f7f31a19452154219db8c8
Author(s)
Choo, Christopher Ledesma Weisen
Advisor(s)
Cynthia Rudin.
Alternative Title
Analytics for improving professional car race strategy
Date Issued
2015
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Engineering Systems Division.
System Design and Management Program.
MIT Department
System Design and Management Program.
Massachusetts Institute of Technology. Engineering Systems Division
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