# How to Make Sense of Team Sport Data:
## 4 Research Aspects
### Research Challenges  
Analysts inspecting team sport data usually pursue various goals such as identifying strengths and weaknesses of their own team and of opposing teams. The insights are used to improve training and raise the team's awareness when preparing for upcoming matches. To identify strengths and weaknesses, analysts, for example, want to understand why a team won a past match. Widespread statistical approaches, however, typically provide only a basic overview (aggregation) over the characteristics of a match. Therefore, one of the most challenging tasks for statistical analysis is the identification of expressive statistical features that help to gain more insight into factors that could influence the outcome of a match. However, in order to get a better understanding of the outcome of a match, we have to analyze our data on a more fine-grained level (e.g., movement of single players). One example for the inherent complexity is shown in Table 1 with a real world example. Possession, shots and duel quota, among other statistics, are usually used to statistically compare teams. They indicate that both teams are equally strong. Considering that Germany beat Brazil 7-1 this is quite surprising. A different sight on the game outcome comes with two new statistics called Packing and IMPECT, both developed by the Impect GmbH [69]. Packing is the number of outplayed players and IMPECT is the number of outplayed defenders. A detailed explanation of both statistics is given by [PERSON] [70]. These statistics are better suited to explain the outcome of this match, with Germany having outplayed approximately 60% more defenders than Brazil. However, even improved statistical measures cannot provide more than an indication about why a team won in a match. Features like packing in our example above show that Germany did a better job in outplaying Brazilian players, but we cannot see how these attacks were played in particular. Analysts want to find out why and how something important happened in a match as well as filter out noise obscuring interesting patterns. There are potentially three different ways how analysts identify patterns. The first way is to detect the pattern completely manually supported with appropriate visualizations. The second way are semi-supervised approaches, where analysts--at the beginning of the analysis--define a template of how the pattern should look like. Based on the template the system starts to look for patterns matching the given criteria. Lastly, analysts might have no clue what to look for and therefore the system should identify everything of general interest employing high-dimensional data descriptors. Explaining movement patterns, however, is very complex, as the movement of a single actor is depending on the movement of all other actors. Because of these interdependencies, almost every action causes a reaction. Adding to the complexity is that typically, there are certain role definition in a sports team that influence movement. Particularly, team captain and goal keeper may steer or influence the movement patterns of team players, e.g., following a tactical decision to defend or press certain players. The role of such leadership effects is prominent not only in soccer analytics, but also a question of leader-follower relationships in domains like expeditions and explorations, military operations etc. This complexity in soccer analytic is added to by the fact that we do not only have to look at the interdependencies within a team but also the interdependencies between two opposing teams. We can abstract this research challenge by generalizing our moving players to moving entities. This abstracted view allows us to widen our analysis possibilities to other domains, because conflicts of interest about where to go and what to do are a primary challenge of living in a collective. Groups do occur in nature hinting to correlating biological fitness.