Episode 310 - Data-Driven Drinks with Dr. Kevin Peterson

In this data-driven conversation with Dr. Kevin Peterson, some of the topics we discuss include: How winding down Castalia and Sfumato after a decade of service provided the impetus for this new project, and why Kevin has set out to address a very different set of questions than in his last book, Cocktail Theory. Why matching a person with their ideal drink is very different (and much more difficult, it turns out) from trying to make the optimal Old Fashioned or Negroni or Daiquiri. This leads to a conversation about all the variables that go into cocktail preference: sweetness, acidity, bitterness, booziness, effervescence, egg white, spiciness, and so much more - how does one begin to build such a high-dimensional beverage algorithm in pursuit of aesthetic pleasure? We also discuss some of the algorithmic tests that good bar programs and bartenders can implement in order to zero in on guest preferences more quickly. These include good menu writing, the classic bartender's choice speed interview, and the possibility for creating a guest profile with likes and dislikes. Along the way, we explore why spicy drinks are a trap, the sea change I experienced the first time I tasted Green Chartreuse, the idea of perceptual thresholds in cocktails and in life, and much, much more.