The extent to which the sequence of tracks in music playlists matters to listeners is a disputed question, nevertheless a very important one for tasks such as music recommendation (e.g., automatic playlist generation or continuation). While several user studies already approached this question, results are largely inconsistent. In contrast, in this paper we take a data-driven approach and investigate 704,166 user-generated playlists of a major music streaming provider. In particular, we study the consistency (in terms of variance) of a variety of audio features and metadata between subsequent tracks in playlists, and we relate this variance to the corresponding variance computed on a position-independent set of tracks. Our results show that some features vary on average up to 16% less among subsequent tracks in comparison to position-independent pairs of tracks. Furthermore, we show that even pairs of tracks that lie up to 12 positions apart in the playlist are significantly more consistent in several audio features and genres. Our findings yield a better understanding of how users create playlists and will stimulate further progress in sequential music recommenders.