Growth-to-standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth-to-standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of nonlinear quantile regression models to longitudinal student test score data. This paper demystifies AGPs by deriving them in the more familiar linear regression framework. It further shows that unlike SGPs, AGPs and on-track classifications based on AGPs are strongly related to status. Lastly, AGPs are evaluated in terms of their classification accuracy. An empirical study and analytic derivations reveal AGPs can be problematic indicators of students' future performance with previously not proficient students being more likely incorrectly flagged as not on-track and previously proficient students as on track. These classification errors have equity implications at the individual and school levels.