This page focuses on the course 15.S50 Poker Theory and Analytics as it was taught by Kevin Desmond during IAP 2015 .
This course takes a broad-based look at poker theory and applications of poker analytics to investment management and trading.
MIT Sloan News published an article on how Kevin Desmond links poker strategy to risk management in this course: Game Theory: What Poker and Finance Have in Common.
Management and leadership positions, finance, trade, global markets. The skills learned in this course are especially good for careers with high-pressure decision-making.
Permission of the instructor
Graduate subject credit
Offered during IAP when an instructor is available to teach the course
The students' grades were based on the following activities:
Assessment in this course was an interesting puzzle. Poker is inherently a game where the results are not perfectly correlated with skill, but it is not in line with MIT’s academic standards for course credit to be determined probabilistically. However, gameplay was an important learning tool and the students needed to be incentivized to play their best. The solution was to split into two leagues, one for credit, and one for prizes, where students would earn credit based on volume of play, but earn prizes based on success. This would ensure every student had the capacity to earn credit in reasonable time. In addition to gameplay, the homework assignments were key to assessing the students’ understanding and ability to implement concepts taught during lectures.
The breakdown of student in this course was about 60% graduate students, 40% undergrads.
The graduate students were about 50% Management, 50% Engineering/Math/Science. The undergraduates were about evenly split among years and primarily from Engineering. A small number of students were visitors from other universities.
Students primarily had an interest in competitive games and practical applications of statistics. Some students intended to work in areas where poker knowledge was critical (trading or gaming industry), whereas other students had a more academic or recreational interest in poker. Most students had a strong background in statistics/math.
The ideal class size is around 100-300 students. Since the class is heavily focused on gameplay, the goal is for students to be playing against a mix of familiar players and new opponents. By the end of the course, most students had a general level of familiarity of the different playing styles of students in the class.
During an average week, students were expected to spend between 19.5 and 24.5 hours on the course, roughly divided as follows:
The majority of in-class time was spent reviewing core concepts and solving example questions. Lecture attendance was mandatory for enrolled students and optional for listeners.
Outside of the classroom, a significant amount of time was spent playing poker in the online league, hosted by PokerStars. The average player played approximately 5000 hands of poker (the equivalent of about a year’s worth of live play). In addition, each of the three homework assignments required about 1-2 hours of work. For some students, about 2 hours per week was spent in review sessions.
The following calendar reflects the way this course was taught on campus. The sessions and materials were reorganized for the OCW version of the course.
In the following pages, Kevin Desmond describes various aspects of how he taught 15.S50 Poker Theory and Analytics.
Usually a graduate student with a background in professional poker.
Faculty sponsor who generally oversees the course, but doesn’t necessarily have an active role in teaching sessions.