MGEC45
Sports Data, Analysis and Economics
Winter 2025
Course Description:
This course uses different empirical strategies to analyse various issues in sports. There will be a special emphasis on econometric and statistical models to explore strategic questions for sports teams and players across multiple leagues. The course will conclude with teams of students presenting their own work on sports data with techniques learned in the course.
Course Learning Outcomes/Objectives:
By the end of the course, students will be expected to have a command ofR programming skills, the collection and analysis of data (particularly sports data) with advanced regression techniques to consider causal relationships. Students will also learn about the frontier of sports economics research. Lastly, the major project will provide students with an experiential learning opportunity that mirrors real-world exercises of data analysts with major sports franchises.
Organization of the Course:
This course will take place in class, and will not include an online component.
Prerequisite(s):
MGEB12H3
Textbook/Required Course Materials:
There is no required textbook for this course, but there will be several readings from different articles.
Lecture Notes and Other Announcements:
The lectures will use PowerPoint files that will be posted on the course website; please download these files for your reference. In addition, the readings covered in the lecture will be accessible through the University of Toronto’s library website
Evaluation and Grading:
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Component
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Weight/Value
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Due Date
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(1) Form. Groups for Week
11 and 12 presentation
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2%
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Monday, September 15, 11:59 pm
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|
(2) Complete R workshop #1
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2%
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TBA
Complete by Monday, September 22
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|
(3) Complete R workshop #2
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2%
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TBA
Complete by Monday, September 22
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|
(4) Obtain approval for Group Project topic
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2%
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Monday, September 29, 11:59 pm
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|
(5) Meet with Professor to review tentative results for Group Project
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2%
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Monday, November 3
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|
(6) Problem Set #1
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15%
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Friday, October 3, 11:59 pm
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|
(7) Problem Set #2
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15%
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Friday, October 24, 11:59 pm
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|
(8) Problem Set #3
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15%
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Friday, November 21, 11:59 pm
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|
(9) Group Project
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45%
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December 1 and 2
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Component (1): Form. groups for major project presented in weeks 11 and 12
During weeks 11 and 12, you will present a major data project on a topic involving sports analytics. This will involve group work, so it will be necessary to form. groups of 4 people relatively quickly, so that planning for the group project can begin as soon as possible. Once you have formed your group, please send me an email indicating your group members, and you will receive full credit. If you are late to form/join a group, you will receive a grade of zero for this component.
Component (2) and (3): Complete R workshop at The Bridge
This course will require you to do a large amount of programming work to complete the problem sets, and the group project. To assist you with your programming skills in R, I have arranged for The Bridge to run a workshop on R, and completion of this workshop will provide you with full credit for this component. If you do not attend and/or complete the workshop, you will receive a grade of zero for this component.
Component (4): Obtain approval for Group Project topic
You will need to obtain approval for your group project’s topic from the professor on or before Monday, September 29. If you obtain this approval on or before the due date, then you will receive full credit for this work. If you are late to obtain approval for a topic, you will receive a grade of zero for this component.
Component (5): Meet with Professor to review tentative results for Group Project
You and your group will need to meet with the professor to review the tentative empirical results for your group project during our class on Monday, November 3. You must have some empirical results to present to the professor during this meeting to receive full credit for this assignment. If you do not meet with the professor, or ifyou do not have any empirical results to show the professor, you will receive a grade of zero for this component.
Components (6) through (8): Problem Sets
You will be required to complete three problem sets in the course; these will be submitted through Quercus. Late penalties are specified later on in this syllabus.
Component (9): Group Project
The major project within the course is a group project that analyzes a major topic in sports analytics by applying econometric techniques learned in the course to original data you collect. The grading scheme for the presentation will be specified in a rubric that will be provided during the term.