# How to Make Sense of Team Sport Data:
## 1 Introduction  
Recent progress in sensor development results in increasing interest in recording and analyzing movement in team sports. In this article, we focus on team sports than can be classified as invasive team ball games with two opposing teams competing against each other and trying to score more points than the opponent to win a game. We have chosen this specific focus for two reasons. First, while the interaction of opposing teams in invasive team sports makes the analysis of sports data more challenging, it also opens up more opportunities for findings. Second, many of the world's most popular team sports, e.g., soccer, football, basketball, hockey, rugby, handball, etc. are invasive. Due to the popularity of these sports, the availability of corresponding data sets and the interestin their analysis are currently on the rise. Professional team sport companies invest substantial resources to analyze the own team's performance as well as the performance of future opposing teams. Various aspects and several data sources are important descriptors for the performance of a team. In practice, some of these data sets are kept confidential by respective stakeholders, e.g., when they contain exact movement trajectories. Other data sets, e.g., basic statistics, are publicly available for analysis purposes (see Section 2.1 for several examples). Depending on the available data, different analysis tasks can be executed. Analysts usually do not only want to have information about the _what_ (e.g., \"Team A won against Team B\" or \"Player X passed more often than player Y\") but instead want to investigate the _why_ behind these facts. There is a need to understand why a certain movement happened and what the influencing factors were. For example, why did a player decide to move to Point A instead of Point B and what influence did this movement decision have on members of the own and opposing teams. The results of such analyses will help, e.g., in scouting or training. However, analysis often focuses on pure statistical approaches. For decades, movement and tactical analysis has been done manually by inspecting video recordings of past matches. In this article, we give an overview of how to work with team sport data in general. Therefore, we introduce the various data types that are available and relevant for team sport analytics. Furthermore, we highlight the challenges that need to be overcome when gathering and working with team sport data. We focus on the different research aspects arising as displayed in Figure 1 with respect to the set of heterogeneous data. Figure 1 shows that the arising research aspects can be grouped into specific domains. Data acquisition describes what needs to be done at first to get the data, e.g., through video processing. The context domain allows us to enrich the data (e.g., through data fusion) after the acquisition with useful additional information. After data acquisition and enrichment the analysis domain allows us to search for patterns. The resulting team sport analysis is on the basis of high-dimensional data that contains time series as well as trajectory data. We show which general computer science problems can be addressed while working on team sport data and propose our methodology to handle these challenges. Ultimately, research on team sport analytics not only influences the sport domain but other fields as well. We will describe the impacts on other sciences such as biology with a focus on collective behavior analysis. Throughout this article, we will use soccer as a prime example for our proposed methodology since it is a highly popular team  
Figure 1: From acquisition (e.g., video processing) and data enrichment (e.g., data fusion) through context information to in-depth analysis tasks (e.g., trajectory analysis) on the raw data, many research fields are covered when analyzing team sport data. sport. Nevertheless, we present a general overview of data, methods, and tasks that are applicable to all invasive team ball games.