Data-Driven Analytics for Decision Making in Game Sports

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses

Spatio-temporal multi data stream analysis with applications in team sports

The amount of live data about individuals which can be collected is steadily growing. These days, humans can be equipped with physical devices or observed with cameras in order to capture information such as their positions, their health state, and the state of their environment. Fitness trackers and health applications which analyze the state and the behavior of an individual on the basis of the data that are captured for this individual are already widely used. However, humans rarely act alone but rather collaborate in teams in order to achieve a common objective. For instance, football players collaborate to win a match and firefighters collaborate to extinguish a forest fire. Analyzing the collaborative team behavior on the basis of data about the individuals which form the team is not only interesting but further poses several challenges on the system that performs the analyses. The focus of this thesis is to address these challenges. We define a data model and a system model in order to provide a theoretical basis for implementing a system that is suited to serve as a foundation for developing team collaboration analysis applications. Both models are novel with respect to the fact that they take the particularities of team collaboration analysis applications, such as the semantics of their input and output data, into account. Moreover, we establish a strong foundation for using the spatial and temporal information which play a central role in analyzing the collaborative behavior of a team. More precisely, we define basic spatial functions and relations and present an extensive stream time model which goes far beyond existing literature on stream time notions and comprises a novel simultaneousness concept. After establishing the theoretical basis, we present StreamTeam, our generic real-time data stream analysis infrastructure which is designed to be used as a foundation for developing team collaboration analysis applications. The data stream analysis system at the heart of StreamTeam is a prototype implementation of our models which further introduces novel approaches to assist domain experts without a profound software engineering background in developing their own analyses. Moreover, we present StreamTeam-Football, a real-time football analysis application which is implemented on top of StreamTeam. StreamTeam-Football is the first analysis application which performs complex team behavior analyses in a football match in real-time, visualizes the live analysis results in a user interface, and stores them persistently for offline activities

The SportSense User Interface for Holistic Tactical Performance Analysis in Football

In today's team sports, the effective and user-friendly support of analysts and coaches in analyzing their team's tactics is essential. In this paper, we present an extended version of SportSense, a tool for searching in sports video by means of sketches, for creating and visualizing statistics of individual players and the entire team, and for visualizing the players' off-ball movement. SportSense has been developed in close collaboration with football coaches