# How to Make Sense of Team Sport Data:
## 3 Abstracting the Data Space
The technical challenges of tracking individuals in animal groups still exists: even under laboratory conditions only parts of collectives can be marked and tracked. Consequently, the field of behavioral biology focuses on animal movement based on recent advances in data acquisition enabling a high temporal and spatial resolution of animal movement. GPS-tracking devices and classical tracking are used to record animal movements in the wild on a more global scale while video tracking analysis are used for rather locally restricted movements in the wild or under laboratory conditions. The increase in resolution led to an increased data volume to be analyzed. One of our visions is to research similarities and differences of team sport and animal collectives. The question is which patterns occur in both domains and which techniques are suitable in which analysis scenario. On the highest abstraction level, movement in general is defined according to [PERSON] et al. [42] as the path of moving entities through space and time. [PERSON] et al. [43] suggest analyzing movement at two different granularities, of the individuals and of the group as a whole. Nevertheless, we also have to deal with pure time-series or time-stamped event data and with spatial topology data. There exists many works dealing with pure time series analysis mostly focusing on similarity calculation and pattern analysis. A good overview over state of the art methods in temporal data mining is given by [PERSON] [44] and visualization methods for time series are discussed for instance by [PERSON] et al. [45]. Temporal analyses alone cannot explain all the behavior observed as important spatial aspects are neglected. Nevertheless, temporal visualizations are crucial to convey temporal patterns. From a pure spatial perspective, computational geometry can be the starting point to analyze trajectories in team sports data as described by [PERSON] et al. in [46]. However, pure geometric approaches do not cover the temporal aspects of movement. Consequently, spatio-temporal analysis are key for a successful analysis. For instance, [PERSON] et al. [47] represent the movement of soccer players as trajectories and propose a model which quantitatively expresses the performance of players based on the relationships between the player trajectories and the ball. Another player network based analysis with respect to performance is performed by [PERSON] et al. [48; 49]. Football strategies are investigated based on network theory analysis by [PERSON] et al. in [50]. A different spatial topology based approach would be the use of graph theory in sports. [PERSON] et al. [51] analyze basketball matches and [PERSON] et al. [52] use graph theory to analyze soccer matches. Working with team sport data, we can apply methods of and contribute to different areas of computer science. One obvious example could be the research field of big data analytics [53] proposed by [PERSON]. According to [PERSON] _Big Data_ is defined by the three \"_Vs_\", namely _Volume, Velocity_ and _Variety_. Often a fourth \"_V_\" is mentioned, namely the _Veracity_, which is the uncertainty in the data, see e.g., [PERSON] et al. [54]. These are all attributes which we are facing when working with team sport data. For instance, we have to work with large volumes of data when analyzing video data of several leagues for multiple seasons or we have to consider the veracity of social media data. Figure 5: Two recent systems that aim to improve the understanding of sport data by several visualization techniques. (**a**) TenniVis [55]; (**b**) SoccerStories [56]. Figure 6: Example uses of different information visualization techniques in recent publications in the field of visual sport analytics. (**a**) Enhanced parallel coordinates [57] displaying, for a single player, the average values of four features within a phase.