Synchronized populations of cells are often used to study dynamic processes during the cell division cycle. However, the analysis of time series measurements made on synchronized populations is confounded by the fact that populations lose synchrony over time. Time series measurements are thus averages over a population distribution that is broadening over time. Moreover, direct comparison of measurements taken from multiple synchrony experiments is difficult, as the kinetics of progression during the time series are rarely comparable. Here, we present a flexible mathematical model that describes the dynamics of population distributions resulting from synchrony loss over time. The model was developed using S. cerevisiae, but we show that it can be easily adapted to predict distributions in other organisms. We demonstrate that the model reliably fits data collected from populations synchronized by multiple techniques, and can accurately predict cell cycle distributions as measured by other experimental assays. To indicate its broad applicability, we show that the model can be used to compare global periodic transcription data sets from different organisms: S. cerevisiae and S. pombe.