Abstract
Association football has been the subject of many research studies. In this work we present a study on player similarity using passing sequences extracted from games from the top-5 European football leagues during the 2017/2018 season. We present two different approaches: first, we only count the motifs a player is involved in; then we also take into consideration the specific position a player occupies in each motif. We also present a new way to objectively judge the quality of the generated models in football analytics. Our results show that the study of passing sequences can be used to study player similarity with relative success. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.

Abstract
Many complex systems exist in the physical world and therefore can be modeled by networks in which their nodes and edges are embedded in space. However, classical network motifs only use purely topological information and disregard other features. In this paper we introduce a novel and general subgraph abstraction that incorporates spatial information, therefore enriching its characterization power. Moreover, we describe and implement a method to compute and count our spatial subgraphs in any given network. We also provide initial experimental results by using our methodology to produce spatial fingerprints of real road networks, showcasing its discrimination power and how it captures more than just simple topology.