We’ve spent the last few years of these courses together exploring the depths of Game Theory, how to generate leverage smartly, and expanding our knowledge beyond the areas of DFS considered to be approaching “Nash equilibrium” and “common knowledge” (an aspect of a game where no move away from optimal theory would generate an appreciable edge over any other player, would be considered in Nash equilibrium; while aspects of a game that every player knows, and every player also knows every other player knows this, would be considered common knowledge).
This year, however, we’re going to reverse course and get back to our (my?) roots. But first, a bit of background. My journey in DFS, like many others, began with cash games. As the general population began to understand variance, risk-reward profiles, and the art of building +EV rosters in cash games (double-ups, 50/50s, and head-to-heads), I noticed that the cash line in those contests was approaching that of the major single entry GPPs in the industry. That year (maybe five to six years ago?), I transitioned the bulk of my play to single entry as a way to get my cash game exposure. But then the field caught up again. Today, the dynamics of single entry and three-max GPP tournaments have changed – and so too should our approach to those contests!
This course will aim to explore game plan development in single entry and three-max contests by examining the various “pillars of success” in these contests (analytical frameworks, conceptual frameworks, and theoretical frameworks). It will also discuss the nuances of contest selection and how our approach should vary in contests of different sizes and structures; it will consider optimization for fields of varying sizes, study generalized field tendencies in single entry and three-max contests, emphasize forms of leverage generation based on those observations of the field, and finish with a discussion on sustainability with respect to bankroll management.
The ultimate goal of this course is to train our decision making habits and processes to maximize expected value in single entry and three-max contests. With that, let’s dive in!
