# How to Make Sense of Team Sport Data:
## 2 Team Sport Data
### Video and Sensor Data
#### 2.1.2 Event Data  
Sport games can be described by an ordered sequence of events. We define events as actions being match-relevant and happening during the match. Events typically are derived from movement data by automatic video analysis; also manual annotation is possible and done professionally by some data providers. From a technical perspective, events are timestamped occurrences of previously known and defined categories optionally annotated with spatial coordinates or additional information as involved players. Most events will be directly ball related and corresponding to actions with the ball (for instance passes or dribbling). Other events may be time-dependent (e.g., start and end of a play period) or not directly dependent on the ball (e.g., a foul situation during a free kick). A multitude of methods to detect such events exist (in fact too many to list them here) ranging from manual annotation to fully automated systems [13; 14; 15; 16; 17]. The resulting streams of semantic data are already widely used in industry and scientific communities [18; 19; 20].  
In practice, events might lack in accuracy, as they are usually annotated manually or as fully automatic recognition may produce false positive and negative events. The company Opta, for example, is commercially providing event data. As event data contains mostly information about players interacting with the ball, event data enables us to conduct overall game statistics (e.g., passing networks, pass accuracy, or time between gaining the ball and shot on target).  
We give an example of a set of potentially relevant events for soccer analysis in Figure 3. This list of events derived from our previous works on a continuously expanded system for feature- and event-based soccer analysis [21; 22; 23]. Although arguably it may be extensible, and is not tailored towards a specific model from Sport Science, we believe it is a practical starting point for reasoning about the types of events potentially useful for analysis. The table contains the type of event, a short description of when the event is recognized, as well as a proposed categorization of events that share similar characteristics. We distinguish the following event categories:  
* _Rule-induced events_ are events that occur as a result of the match rules. For example, if the ball passes the sideline of the soccer pitch, it has to be thrown in again by the opposite team.  
Figure 3: Various different kinds of events categorized by their characteristics.  
* Events tagged with _prosecution_ indicate that there was a foul behavior of the related player(s) which is penalized.
* _Player interactions with ball_ is about events that happen when a player is touching the ball. Observable, almost every event that gets tagged falls under this category besides yellow and red cards, the end of a halftime and a substitution.
* Events that interrupt the match gets marked as _gameplay interruption_.
* If an event has a direct relation to scoring (e.g., a shot on the goal) we mark it as _scoring related_.  
Events from different categories may lend themselves to different analysis tasks. Also, they are subject to possible detection or description inaccuracies and may require parameters to work. For example, _running with ball_ requires appropriate threshold setting for distance between player to ball to be recognized. We observe that events may belong to different categories mandatory or optionally. In our table, we mark events dark blue if it always belongs to the respective category. If an event is marked in lighter blue instead, the event may falls under this category, but does not have to.