We explore possibilities for placing community college students in math using non-cognitive measures, such as indicators of motivation or students' educational plans. First, we use supplemental administrative data gathered during routine placement testing in a large urban community college district in California to conduct predictive exercises that assess whether utilizing non-cognitive measures can reduce placement error. We find that using high school background and non-cognitive measures to predict success reveals as many as one-quarter of students may be mis-assigned to their math courses by status quo practices. We then move beyond prediction and evaluate student outcomes in two of the colleges where non-cognitive measures were directly factored into placement algorithms. The results show that considering non-cognitive measures in addition to placement test scores increased student access to higher-level courses without compromising the likelihood of success. We interpret this as a marginal increase in placement accuracy and discuss how non-cognitive measures can be used to improve selection into community college math courses.