Dynamical systems analysis and the study of time series data provide a rich source of information for researchers interested in studying psychological phenomena. While many methods exist for quantifying linear behavior from a given time series, considerably fewer methods exist for quantifying complex and nonlinear time series. Of existing methods for quantifying complex and nonlinear time series, most either: a) require a restrictive number of data points, b) are highly sensitive to noise, c) are technically challenging to implement, or d) require researchers to heuristically set many hyper-parameters. Each of these requirements impedes the utility of measures of temporal complexity in psychological science. We propose a new complexity metric, tangle, that overcomes these restrictions. Tangle is applicable to time series with as few as 50 time points, is robust to noise, and requires only iterative matrix multiplication to be calculated.