The assault of law enforcement officers is an important but understudied topic. To better understand this form of violence, this study drew on the National Incident–Based Reporting System—the nation’s largest data set tracking assaults against police alongside detailed information on situations surrounding attacks. Risk factors were examined within a fixed-effects logistic regression framework via a case-control method in which all incidents involving assault against police (n = 20,140) were compared with a random sample of arrest encounters that did not involve this form of aggression (n = 20,118). The data showed that a number of victim, offender, and situational characteristics predicted violence against officers, and the models were able to explain a substantial portion of the variance. Implications for research are discussed.