Analyzing the offensive playing style of teams is an important task within soccer analytics that has various applications in match preparation and scouting. Existing data-driven approaches typically quantify style by looking at individual events that occur during a match in isolation. This approach has two shortcomings. First, it ignores the sequential aspect of the game, as patterns of play are a crucial aspect of playing style. Second, it fails to generalize over the limited amount of data in order to model slight variations of the observed patterns that a team may employ in the future. This is particularly important when considering rare actions like shots and goals, which are the key success criteria of an offensive style. This paper proposes a novel approach for analyzing playing style that addresses these shortcomings. First, it captures the sequential patterns of a team's style by modeling the observed behavior of a team as a discrete-time Markov chain. Second, it characterizes the offensive style of teams in a number of features that are based on domain knowledge. It applies a combination of analytical techniques and probabilistic model checking to reason about a team's model in order to extract values for these features. As the model allows for a generalization of a team's past behavior, the extracted style is less influenced by the rarity of shots and goals. Using event stream data of the 2019/20 English Premier League, we empirically show that the proposed approach can capture a team's positional and sequential style, as well as reason about the style's efficiency and similarities with other teams.