# How to Make Sense of Team Sport Data:
## 5 Methodology
### Data Modeling  
Generally speaking, modeling is about giving structure to the problem of sport analysis, by prescribing which aspects are of importance to the analysis. Ultimately, this needs to be informed by the task of the analysis. Different examples are, e.g., the short-term performance analysis of a single team member, versus the long-term analysis of the team performance or its evolution over time. The modeling process will eventually identify a set of variables and/or events to observe, and a quantitative scheme to aggregate and/or compare the measures. We distinguish two main approaches to guide the modeling: Domain-specific modeling is based on theories and concepts from _Sport Science_[79; 80], which generalize relationships between actions and outcomes in the respective sport domain. On the other hand, _data-driven_ or _explorative_ modeling does typically not assume previous knowledge about the domain, but is guided by dependencies found in the data directly. In practice, both approaches often go hand-in-hand: There are expectation and theoretical models about typical dependencies in the sports events, and the analysis measures are derived to validate and quantify the expected dependencies. On the other hand, patterns that one may observe inconsistently in larger amounts of measurement data may lead to new domain-specific theories and hence guide the modeling approach conceptually. Team sport is fundamentally about space-time interaction of players--understood as a combination of operation, communication, and strategy--with the most influential interaction being the interference of the opposing team which excludes in many respects absolute scales of measurement. The ongoing lack of reliable key performance indicators for soccer teams points in this direction (see, e.g., the illuminative discussion in [81; 82]). A deeper understanding of the interaction structure and dynamics between and within teams during a match is meaningful for making progress in team sports analysis. Relative phases [83; 84], couplings [85], invasion profiles [86], or centralities in passing networks [87] represent exemplary initial concepts. As the data collected for a match shows only one realization of a contingent situation (\"what happened\"), the challenge is to determine the (path-dependent) set of _possible_ actions executable in that situation (\"what not happened\"). Operationally, the presence of the opposing team first and foremost restricts the space-time regions on the pitch accessible for meaningful action of a focal actor. On the communication level, selecting an action from the set of accessible actions (\"decision\") depends furthermore on the intra-team movement patterns, space cognition, and information processing. Here, coming up with realistic and feasible action models is very demanding [88; 89; 90], but worthwhile for answering to questions like \"Which pass is best to execute given a set of possible passes?\" Sole ball-oriented data is very limiting in respect thereof, as no information on players without the ball is available; the situation becomes much better with additional positional data of all players (see, e.g., [90]). Moreover, technology is available (at least in basketball) to track head positions and head orientations of the players providing data for a feasible inference of mental maps [91], e.g., which players are seen by a player. On the strategic level of interaction, expectations, style of play, tactical orientations, etc. determine a _normal_ behavior of the teams. Data-based analyses of these aspects are thus required for an unbiased evaluation of action sets. However, corresponding techniques are currently less developed. Methods of game theory could be applied, e.g., building strategic games from multi-parameter interaction models based on tactical action-reaction schemes available from expert's domain knowledge. In principle, there are two approaches to analyze the interaction of two teams in competitive dyads (matches).