OPER 6610.01: Sports Analytics (Fall 2014)
Fulton Hall 423: M (
Christopher Maxwell Maloney
Hall, 337
maxwellc@bc.edu Hrs: M
http://www.cmaxxsports.com x2-8058
Course description: This course is about
working with data. Our focus will be on the
development and use of quantitative methods (particularly mathematical and
statistical models) that are widely used to assist in decision making and
tactical analysis at all levels in the management of professional sports
organizations. We work with sports data
because there’s so much of it, and it’s so much fun to work with. But the techniques and insights developed
could just as easily come from working with more traditional business data (if
only they were so readily available).
Analytic approaches: The analytic focus will largely be on
univariate and multivariate statistics and regression analysis, optimization
methods, game theory and if time permits, matrix methods.
Prerequisites: A working
knowledge of basic probability and statistics as well as strong Excel
skills. Almost all of the analytic work
in this course will be done with Excel. To
brush up on your Excel skills, you might look at the materials assembled by the
ITS department: http://www.bc.edu/offices/its/tandc/training/course_materials.html
.
Unfortunately, the features
offered by Excel differ somewhat across platforms and over time. For this course, you may need to install the Analysis
ToolPak and the SolverAdd-in if your Excel does not
already offer those features. Let me
know if you need help with these installations.
Data, data, data: We will be working with publicly
available data from the four major North American professional sports leagues
(MLB, NBA,
Topics: This
course is data-driven and built around a series of quantitative analyses, which
address a wide variety of sport-related topics.
The following list provides some sense the sorts of topics that we might
cover:
·
performance
drivers (e.g. Pythagorean Theorem; corner kicks)
·
strategy and
tactics (e.g. play/shot selection; icing kickers; 3-0 fastballs; penalty
kicks)
·
performance
assessment and forecasts (e.g. the
·
pricing in risk
markets ( e.g. the efficiency of wagering markets)
·
referee/umpires
bias (e.g. home field advantage; called balls and strikes; fouls, cards and
stoppage time)
·
player
compensation (e.g. valuing performance; superstar effects)
·
peer effects
(e.g. valuing player synergies)
This is not a sports history or
trivia class. It is a data analysis
course. It just so happens that we work
with sports data.
Texts:
·
Wayne Winston, Mathletics: How Gamblers, Managers, and Sports
Enthusiasts Use Mathematics in Baseball, Basketball, and Football,
·
Tobias Moskowitz and Jon Wertheim, Scorecasting: The Hidden
Influences Behind How Sports Are Played and Games Are Won, Three Rivers
Press (paperback), 2012.
We will closely follow the Winston text; the
Moskowitz/Wertheim text will come into play from time to time.
BlackboardVista err
Canvas: Everything distributed in class, and more,
will eventually be posted somewhere… said where yet to be
determined. Stay tuned.
Accommodations: If you are a
student with a documented disability seeking reasonable accommodations in this
course, please contact Kathy Duggan (x2-8093; dugganka@bc.edu) at the Connors
Family Learning Center regarding learning disabilities and ADHD, or Paulette
Durrett, (x2-3470; paulette.durrett@bc.edu) in the Disability Services Office
regarding all other types of disabilities, including temporary
disabilities. Advance notice and
appropriate documentation are required for accommodations.
Academic Integrity: You will be
held to
Class Structure: We will be following the topics and analysis
in Winston’s text fairly closely.
Classes will typically divide into two parts: In the second half or so we’ll be discussing
new material from the Winston text.
There will be a weekly assignment based on that material
(typically involving updated datasets), which will be discussed in the first
half of the following class. The pace
will certainly vary over the course of the semester; I anticipate that we’ll
cover three-four chapters of the Winston text per week.
Course Structure: There are three components to the course (%’s
of course grade are in parentheses). They
are:
1. Weekly short assignments (25%)
2. Exercises (35%)
3. Term paper and presentation
(40%)
1. Weekly Short Assignments: There
will be ten or so weekly short assignments, which build on our discussion of
the Winston material and typically involve some sort of replication of that
analysis with updated datasets. These
graded assignments are primarily intended to give you some practice applying
the analytic skills developed in class (as opposed to breaking new
ground). They will count equally towards
your course grade, with the lowest score dropped in the course grade
calculation. Feel free to work with
others on these, but please submit your own work product. Final grades on weekly short assignments
(which count towards 25% of your course grade) will be curved.
2. Exercises: Exercises
count equally towards 35% of
your course grade. Exercises will count
equally towards your course grade. We
may drop one of these Exercises if we get behind in the schedule. To protect you from yourselves, Exercises
will have Answer Sheets. Feel free to
collaborate on the exercises, but please prepare your individual submissions independently.
Subject to revision, here’s the proposed set
of Exercises and the schedule (you will typically have two weeks to complete
each Exercise).
#1:
Pythagorean Theorem (distributed:
9/15; due: 10/6)
#2:
Run and Win Expectancy (and Leverage)
(distributed: 10/6;
due: 10/20)
#3:
Game Strategy (Play Selection) (distributed: 10/20;
due 11/3)
#4:
Sports Team Ratings and the
#5:
Wagering Market Efficiency (distributed: 11/17;
due 12/1)
In many cases, there are faster and slower
ways to complete the exercises. Let me
know if progress is painfully slow, and I’ll be happy to make suggestions to
help speed things up. Final grades on
exercises are curved.
2. Term Paper: The term paper is an empirical project and
counts towards 40% of your course grade.
This is a team assignment, with two students per team (I will assign
teams in early October). Your topic is
your choice, but it should feature the sorts of analytic techniques that we
develop and implement over the course of the semester. We’ll set aside one mid-semester class to
allow students to present their term paper topics in class and discuss progress
to date. I’ll be happy to work with you
if you are having trouble developing a topic.
Term papers should have six parts:
1. Introduction (description of
topic and summary of results)
2. Brief literature review
3. Description of your analytic
model and nature of analysis
4. Discussion of data
5. Presentation of results
6. Conclusion
There is no page requirement, though it is hard to do a good job covering
all of these dimensions of the assignment without writing 15-20 pages or so (remember,
shorter is always better!). Empirical
work is slow going. Be sure to leave
yourself enough time to complete the assignment to your satisfaction.
Students will present their term papers in-class at the end of the
semester on Dec 8th .
Presentations should last no more than eight minutes (talk fast!) and
not feature more than 10 or so slides.
Please submit your presentation with your term paper. Both submissions are due in hardcopy form by
the end of the day, Dec 10th (so you’ll have a couple days to clean
things up after your presentation).
Some urls, perhaps of interest:
·
Sports Business Daily: http://www.sportsbusinessdaily.com/Daily.aspx (expensive but informative; two week
trial subscription; student rates (still expensive))
·
Sports Business
Journal: http://www.sportsbusinessdaily.com/Journal.aspx
(I believe the library has acquired a subscription to this journal)
·
SportsBiz: http://thesportsbizblog.blogspot.com/
·
Sports Law: http://sports-law.blogspot.com/
·
Journal of Quantitative Analysis in Sports (JQAS): http://www.degruyter.com/view/j/jqas
·
Rodney Fort: https://sites.google.com/site/rodswebpages/codes
·
John
Vrooman: http://www.vanderbilt.edu/econ/faculty/Vrooman/sports.htm
·
and http://www.cmaxxsports.com/misc/misc.html (you’ll find useful web pages (more or less
up to date) devoted to MLB, the NBA, the NCAA, the NFL, and European
football/soccer… and more)