Heavy alcohol consumption can lead to many severe consequences. In this paper, we study the phenomenon of heavy drinking at night (4+ drinks for women or 5+ for men on a single evening), using a smartphone sensing dataset depicting about nightlife and drinking behaviors for 240 young adult participants. Our work has three contributions. First, we segment nights into moving and static episodes as anchors to aggregate mobile sensing features. Second, we show that young adults tend to be more mobile, have more activities, and attend more crowded areas outside home on heavy drinking nights compared to other nights. Third, we develop a machine learning framework to classify a given weekend night as involving heavy or non-heavy drinking, comparing automatically captured sensor features versus manually contributed contextual cues and images provided over the course of the night. Results show that a fully automatic approach with phone sensors results in an accuracy of 71%. In contrast, manual input of context of drinking events results in an accuracy of 70%; and visual features of manually contributed images produce an accuracy of 72%. This suggests that automatic sensing is a competitive approach.