In order to improve the accuracy of students' placement into California Community College English as Second Language (ESL) courses and move them more efficiently toward completion, the statewide Common Assessment Initiative (CAI) is developing placement processes for student assessment in credit ESL that contain a multiple measures component. As part of this work, the Multiple Measures Assessment Project (MMAP) has created a set of multiple measures to be used in assessment that are based upon high school transcripts. While these measures cannot be applied to all ESL students due to lack of data, they offer value for those who do have high school data available. This brief describes the variation in credit ESL sequences and how the MMAP team addressed the analytical challenges of these variations. There is also discussion of the source data set, inclusion criteria for the predictive model data sets, and a short description of the decision tree predictive analysis. Also addressed is the question of the influence of high school origin and college destination on community college ESL course success.