We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the fol- lowing question: what can be predicted about the state of the driver and the state of the driving environment from a 6- second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learn- ing to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world condi- tions. So, it’s valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demo- graphic variables? We analyze 27 binary classification prob- lems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to pre- dict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.