Sports Economics (EC 370.01 – Spring 2009)
O’Neil 253: T Th (9 – 10:15)
Christopher Maxwell Office:
http://www.cmaxxsports.com Hrs: T 2-3:30 and by appt.
Course Description: This applied economics course explores various aspects of the economics of sports and sports leagues, with a focus on empirical analysis. We will consider a number of topics, including the business and economics of professional team sports and sports broadcasting, analysis of competitive balance policies, player relations issues including analysis of the drivers of players' salaries, the public finance aspects of sports teams and stadium financing, antitrust issues, and issues in collegiate sports. A term-long empirical research project is an important part of the course. This is not a sports history or trivia class.
Prerequisites: EC201 or EC203, and EC151 or EC155. This course makes extensive use of Excel. You should not take this course if you do not
have strong Excel skills. Statistical
languages such as Stata,
While EC228 is not required, it is advisable. Students are expected to know how to run simple econometric models. This course is not open to students who have taken a Sports Economics course abroad.
Office Hours: Tuesday 3-4:30 and by arrangement
(Available at the bookstore; will be placed on reserve at O’Neill.)
Useful Websites: to name a few (you will find the first two especially useful)
There are no exams in this course.
· Tues, Feb 10: Term paper prospectus due (one-two pages: topic, literature review (one paper in detail), and data sources); presentations (move fast)
· Tues, Mar 17: Term paper updates – progress report; some results presented
BlackboardVista: Everything distributed in class will eventually be posted on the course’s BlackboardVista site. In addition you’ll find a large amount of data and additional material posted there. Let me know if you have trouble accessing that material.
Academic Integrity: The Deans have
requested that I remind you that you will be held to
Term Papers: The term paper is an empirical project and counts towards 40% of your course grade. Term papers should have six parts:
There is no page requirement, though it is hard to do a good job covering all of these dimensions of the assignment without writing at least 12-15 pages. Empirical work is slow going. Be sure to leave yourself enough time to complete the assignment to your satisfaction.
Term papers submitted by April 16 will be graded and returned by Monday Apr 27th.. Students may then revise these papers if they wish. All term papers are due by May 11.
This is an Economics course. Papers that are merely sabermetric exercises will not receive a grade above B+.
Exercises: The course is structured around a set of empirical exercises, which count towards 40% of your course grade. These will typically (but not always) be team assignments (with 2-3 students per team) and will be graded. Teams will be assigned in class. Most Exercises will divide into two parts, so that something is due almost every week (Thursday will be Exercise Day). No late work accepted (low score dropped in determining your course grade). If we do not get through as many exercises as anticipated, the course grade weights may be changed. Final grades on Exercises are curved. The Exercises:
Other possible exercises:
Tuesday Talks: Every Tuesday, we’ll devote the first 10 minutes or so of class time to a discussion of a current relevant issue. The discussion will be led by a team of two-to-three students (team assignments will be distributed on Tuesday Jan 27th). The team leading the discussion may want to distribute a brief set of “talking points” (five bullet points max) to guide and focus the discussion. To provide a sense of what’s expected, I’ll do the first two presentations on Jan 20th and Jan 27th.
NCAAB Points Competition: More details to follow, but briefly: The challenge in this competition is to build a forecasting model that best predicts points scored by NCAA DI men’s basketball teams over the course of the 2008-2009 season, and in particular, in the post-season. Predictions are out-of-sample, so models are based on results up to some point in time and forecasts are for games after that point in time.
Teams will be assigned on Thursday Feb 5th. In the first week of the competition, models will be estimated on results from games played through Monday Feb 9th. The estimated models will then be used to forecast results for the following week (beginning Tues Feb 10th and ending Mon Feb 16th). Results will then be presented in class on Tues, Feb 17th. This process is subsequently repeated two more times (models revised; new forecasts generated for the week ahead), with presentations on Tues Feb 24th and Tues Mar 10th (welcome back from Spring Break!). I will be working along side you and reporting as well on my efforts and success along the way.
In the weekly updates, teams will reveal their sum squared forecasting error (SSE) for the week being estimated and to some extent, the nature of their model (in wk one of the competition, their two most significant explanatory variables; in wk two, their three most significant explanatory variables; and in wk three, their four most significant explanatory variables). On March 17th, teams will provide forecasts for the first two rounds of the NCAA tournament (played Mar 19 – 22); on March 24th they do the same for rounds three and four (played Mar 26-29); and again for the final two rounds, submitting final forecasts on Mar 31st.
Final presentations will be on Tuesday April 7th (the day after the NCAA Final). The winning team will receive a prize! Grades will be determined by the ratio of my SSE to your team’s SSE for scores during the NCAA tournament (grades will be capped at 100%).
Feb 5 (Th): Teams assigned
Feb 17 (Tu): Report #1
Feb 24 (Tu): Report #2
Mar 10 (Tu): Report #3
Apr 7 (Tu): Presentations & Awards