Human performers introduce temporal variability in their performance of music. The variability consists of both long-range tempo changes and microtiming variability that are note-to-note level deviations from the nominal beat time. In many contexts, microtiming is important for achieving certain preferred characteristics in a performance, such as hang , drive, or groove ; but this variability is also, to some extent, stochastic. In this paper, we present a method for quantifying the microtiming variability. First, we transcribed drum performance audio files into empirical data using a very precise onset detection system. Second, we separated the microtiming variability into two components: systematic variability (SV), defined as recurrent temporal patterns, and residual variability (RV), defined as the residual, unexplained temporal deviation. The method was evaluated using computer-performed audio drum tracks and the results show a slight overestimation of the variability magnitude, but proportionally correct ratios between SV and RV. Thereafter two data sets were analyzed: drum performances from a MIDI drum kit and real-life drum performances from professional drum recordings. The results from these data sets show that up to 65 percent of the total micro-timing variability can be explained by recurring and consistent patterns.