Data analytics have completely revolutionized professional sports in so many different aspects.The increasing volume of data generated together with data management tools and analytical methods proposed a new data-driven paradigm where players, clubs, agents, and researchers rely on this data to perform tactical, performance or management-based decisions and diagnoses. The main advantage of data is the ability to reconstruct the sports game precisely. Event data is a prominent data source of information in invasion sports games such as football, basketball or handball and consists of a detailed and ordered sequence of all actions that occurred during a match. Despite the highly granular information that event data provides, most of the current approaches fail to properly manage and model the true nature of this data source as a dynamic behaviour. This research project focuses on the applicability of Process Mining to reveal the true power of event data. Process mining is a highly applicable research discipline in the intersection of data mining and business process management. A truly process-aware analysis of event data would acknowledge the existence of actors, roles, resources and sequences in the reconstruction of the game while opening a full set of possibilities for sequence analysis, behaviour analytics and visual understanding of the in-game decision making. By moving away from process-agnostic approaches, this project aims to evaluate event data sources from the main invasion sports, present process-aware data modelling and showcase its main benefits by analyzing sequence patterns and presenting new visual tools.