Ordinal analysis is a statistical method for analysing the complexity of time series. This method has been used in characterising dynamic changes in time series, with various applications such as financial risk modelling and biomedical signal processing. Ordinal pattern encoding is a fundamental calculation in ordinal analysis. It is computationally demanding particularly for high query orders and large time series data. This paper presents the first reconfigurable accelerator for this encoding calculation, with four main contributions. First, we propose a two-level hardware-oriented ordinal pattern encoding scheme to avoid sequence sorting operations in the accelerator, enabling theoretically best code compactness. Second, we develop a hardware mapping method by promoting data reuse, by parallelising arithmetic operations, and by pipelining the data path. Third, we conduct an experimental implementation of the proposed system, showing promising accelerated performance compared to software solutions. Finally, we apply the proposed accelerator to the computation of permutation entropy, demonstrating the significant potential for acceleration that would benefit such computation.