# Recognizing Events in Spatiotemporal Soccer Data
## 2 Background and Related Works  
Sports provide a vast amount of diverse information, ranging from statistical tables reflecting the performance of individuals and teams to video streams of competitions. However, different sources of information provide different types of data, and it is not always possible to combine them to obtain a comprehensive picture of certain phenomena. The reasons for such fragmentation are both technological and legal. For example, the English Premier League compiles and publishes basic statistical facts about their soccer players, and, in general, such data is often reported in newspapers and various \"yearbooks\". Some research works are focused on analyzing soccer video streams and detecting the events directly from them [8,9]. However, video recordings of complete matches of a particular event might be hard to obtain, and their distribution is typically strictly controlled by the copyright holder. Soccer video recordings that capture the whole field are rarely available at all. Often the content of a particular dataset reflects the collectors' understanding of what constitutes \"interesting\" information. For example, an often-cited F24 soccer feed [10] provided by Opta Sports [11] includes specific types of in-game events, manually annotated by the experts [12]. Copyright issues limit the availability of such datasets, and the question of ownership in borderline cases remains open [13]. We are specifically interested in spatiotemporal soccer player tracking data with ball-event markup. Player tracking data is available from several providers, such as StatsPerform.com, DataStadium.co.jp, and Chyronhego.com. Typically, it is obtained by the software-assisted digitization of video streams, recorded by several fixed cameras, installed at a stadium [14]. This technological process presumes no additional post-processing, therefore producing player and ball coordinates only. On the other hand, ball-event datasets, such as the aforementioned Opta's F24 feed or the _Soccer match event dataset_[15] are typically focused on events, and do not provide complete player tracking data. In cases where manual event annotation is available, time synchronization might be inaccurate, and errors in player attribution are common [16]. Event detection in soccer datasets is the subject of several recent research works. A survey made by [PERSON] and [PERSON] [17] lists numerous tasks related to the spatiotemporal analysis of team sports. However, as of early 2016, only a few attempts have been made to deal with ball-event processing. Furthermore, these works are dedicated either to categorizing and labeling known events [18,19] or predicting future events [20,21]. Certain approaches to automatic event detection have been proposed only in more recent papers [16,22,23]. [PERSON] et al. [16; 22] applied several different machine learning-based methods to recognize four event types (pass, reception, clearance, and shot on goal) in a spatiotemporal soccer dataset. They prepared a \"gold standard\" manual markup comprising 194 events within 8:47 min of active game time and used it for training and testing different methods, including Support Vector Machine, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks. Their best results were achieved with neural networks, yielding a precision of 89% and a recall of 90%. Interestingly, a significant quality improvement was obtained by applying a smoothing filter that reduced the original 25 Hz sampling rate of game recordings to 10 Hz. It is also worth mentioning that the resulting event markup did not include extended event information, such as a passer's and receiver's identity in the case of a pass event. Finally, it can be argued that the training set was relatively small (it included only seven shots on target, for example), so a more thorough evaluation might be necessary to assess the quality of the proposed approach in practical scenarios. [PERSON] et al.