# How to Make Sense of Team Sport Data:
## 3 Abstracting the Data Space  
We described the multitude of facets and aspects of data in team sports in the sections above and pointed to the heterogeneity of recorded team sport events. However, what we still need to discuss are the abstract ingredients of team sport. We investigate the nested abstraction levels depicted in Figure 4 following a bottom-up approach starting with team sport and going all the way to the basic data types, namely geospatial and temporal data. In team sport data, there are two competing groups that have opposed predefined objectives, meaning that (if the match did not end in a tie) only one of the two groups can achieve their objective and the other group loses the game. The challenge of analyzing team sport data is that movement is restricted by a pitch and rules, driven by the predetermined objective, and influenced by the movement of own and opposing team players. For illustration purpose, we exemplify these properties in American football: the movement of players and teams is limited to the pitch. The movement of the two opposing groups is clearly driven by a predetermined goal. The group possessing the ball wants to cross \"the opposition's goal line with the ball, or catch or collect the ball in the end zone\" [38]. The counter-objective of the opposing team is to prevent this from happening and to gain possession of the ball. American football is a very good example to illustrate how groups and individuals influence their movement mutually. Examples include the defensive line trying to block the running back (group influencing an individual and vice versa), the offensive line pushing against the defensive line (group influencing group) and corner backs covering the receivers (one individual influencing one individual). Rugby is another example for team sport being analyzed nowadays as shown by [PERSON] et al. [39]. [PERSON] et al. [40] published recently a comprehensive survey covering several aspects in team sports data. Our paper is aimed at both a survey and a concept paper, and our main goal is to widen the scope of considerations for team sports analysis as compared to previous surveys. We build on top of this survey by discussing the possible manifold data sources and their influences on the analysis process, hence widening traditional sensor data-based analysis schemes. As we point out, existing techniques often stem from the machine learning side, and may underweight the importance of visual-interactive interfaces for understanding team sport data and bringing background knowledge to the analysis. Therefore, we show several examples for Visual Analytics interfaces dealing with team sport data, to inspire thinking about possible user-oriented sport data analysis. Figures 5-7 exemplify novel visual analytics interfaces which allow interactive analysis for aspects including spatial movement patterns, feature-based analysis and space-time segmentation, and visual comparison of events. Team Sport Analysis requires, in our view, and interdisciplinary approach where Sport Science, Behavioral Science, and Data Science including Data Visualization can all benefit to better using and understanding team sports data. By outlining this big picture, we indicate potential for interdisciplinary advance of research in the area. Figure 4: The abstract ingredients of team sport. One abstraction level higher, team sport can be seen as a specialization of group movement as shown in Figure 4. Group movement can contain either cooperative or competitive behavior or any combinations of both being exactly what can be observed in team sport. Group movement is also studied in biology and behavioral science. For example, there exists the so-called Movebank Project [41], a database about animal movement on a global scale. In nature, group movement occurs on very different scales, ranging from two (courtship or mating) to a thousand (social insects) individuals.