DROMB8131 Sports Analytics

Instructor: Mark N. Broadie

Additional Information: DROM_B8120_Fall_2013_Syllabus.pdf

Sports analytics refers to the use of data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena.  This course builds on the Business Analytics core course and is designed to help students to develop and apply analytical skills that are useful in business, using sports as the application area.  These skills include critical thinking, mathematical modeling, statistical analysis, predictive analytics, game theory, optimization and simulation. These skills will be applied to sports in this course, but are equally useful in many areas of business.

There will be three main topics in the course: (1) measuring and predicting player and team performance, (2) decision-making and strategy in sports, and (3) fantasy sports and sports betting.  Typical questions addressed in sports analytics include: How to rank players or teams? How to predict future performance of players or teams? How much is a player on a team worth?  How likely are extreme performances, i.e., streaks?  Are there hot-hands in sports performances? Which decision is more likely to lead to a win (e.g., attempt a stolen base or not in baseball, punt or go for it on fourth down in football, dump and chase or not in hockey, pull the goalie or not in hockey)?  How to form lineups in daily fantasy sports?  How to manage money in sports betting? How to analyze various ``prop'' bets?

The main sports discussed in the course will be baseball, football, basketball, hockey, and golf.  Soccer, tennis, and other sports will be briefly discussed.  Students are welcome to pursue any sport in more detail (e.g., cricket, rugby, auto racing, horse racing, Australian rules football, skiiing, track and field, or even card games such as blackjack, poker, etc.) in a project.

Class sessions will involve a mixture of current events, lecture, discussion, and hands-on analysis with computers in class.  Each session will typically address a question from a sport using an important analytical idea (e.g., mean reversion) together with a mathematical technique (e.g., regression).  Because of the “laboratory” nature of part of the sessions, students should bring their laptops to each class.

500 W. 120th St., Mudd 315, New York, NY 10027    212-854-2942                 
©2014 Columbia University