# How to Make Sense of Team Sport Data:
## 6 Discussion and Conclusions  
The analysis of team sport data is a potentially very useful, yet inherently difficult problem. In this paper, we gave a high-level overview to this fields as well as to identify future research aspects to contribute on. Team sport analytics involves many challenges and problems. First, the choice of data acquisition is difficult, as the type and quality of data available determines the potential analysis that can be done. Current acquisition includes video analysis, using position sensors or manual encoding. There is much additional data one can think of to include in a meaningful analysis, e.g., biomedical and physiologic measurements of players could be taken into account. In the wider sense, data acquisition also needs to consider data modeling, integrating, and cleaning, each representing significant work steps. Being aware of data quality and comparisons of data sources is of critical importance. With regards to this work quality observations are especially needed when it comes to the trajectories of players and balls from video image sources. While we recognize the need for detailed benchmarks, such analysis has not yet been done in related literature and was beyond the scope of this article. In the future we will strive to fill this gap. For meaningful analysis, domain requirements coming from external experts are important and must be taken into account. Specifically, analysts nowadays are, for example, interested in the analysis, prediction and performance monitoring of their team. Each problem, however, has many sub problems. The problem of data acquisition, for example, translates into video processing, classification and annotation problems. Nevertheless, team sport analysis is a highly interesting research field where experts from many different computer science subjects (computer vision, graph theory, network analysis, visual analytics,...) can bring in their expert knowledge. An important problem of doing analysis relates to modeling--what are the aspects in the sports events/matches which are of importance? How can we qualitatively and quantitatively assess individual player performance or the development of tactic and strategic capabilities of a team? Answers to these questions need to consider both data-driven approaches from Data Science, but also,models and concepts developed in Sport Science. Eventually, we see that both can go hand in hand to design appropriate analysis systems. Future work needs to better characterize the role and influence that both fundamental approaches have and how to combine them. In surveying existing analysis techniques, it may be instructive to distinguish these according to the level of detail on which they operate. Single events and situations (e.g., success rate of corner kicks) can be easily statistically computed. However, it is a difficult problem to assess strategic factors than can _explain_ why a specific success rate is observed, or how it could be influenced. In particular, the development of explanatory models for different temporal scales from short- to long term, and considering involved dimensions, is seen an important problem for future work. We also discussed visualization technology to help analysts interactively explore relevant sports data and help with interpreting patterns. We note that many analysis goals cannot be statically defined once and for all, but depend highly on the context of the analysis. For example, to prepare a team for a match against an upcoming other team, one coach needs to analyze or predict the strength of ones team in context of the other team. To this end, the analyst needs ways to define the context in which to do the analysis, for example, on the offensive of defensive sides. Interactive visualization is a key technology to provide adaptive analysis systems. In Section 2.1.2 we provided a soccer event taxonomy based on previous data-driven soccer analysis systems we implemented so far.