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

Sáhka

Elite football today is infused with technologies for collecting data on players' performance and health. Technologies such as wearable health and fitness trackers, full-body medical scans, and positional systems mounted around a stadium give coaches a wide range of accurate data about their players. However, coaches do not have the time or ability to analyze all this data for each player to give them individualized training schedules. Hence, coaches need tools to collect, analyze, summarize, and present the data to them in a much more consumable format. Existing systems within sports technology are isolated, only collecting their own data and using it for their own purposes. Hence, Sáhka will break out of this norm and create a novel system within this domain. Sáhka is a system that federates relevant data from several different sources. The data is processed, stored, analyzed, and presented visually to the users. Additionally, by collecting a large quantity and variety of data, Sáhka acts as a Big Data repository for real-time sports data. In the future, Sáhka will be used as a platform for training Machine Learning algorithms

Futebol

Relat?rio de est?gio apresentado para obten??o do grau de Mestre em Treino Desportivo, na Escola Superior de Desporto e Lazer do Instituto Polit?cnico de Viana do Castelo.Nos ?ltimos anos, temos assistido a um desenvolvimento acentuado no panorama futebol?stico, com uma maior profissionaliza??o das diferentes dimens?es que abrangem o jogo. A Observa??o e An?lise no Futebol tem evolu?do exponencialmente e cada vez mais, existe uma maior necessidade de inser??o de equipas t?cnicas multidisciplinares que incluam um observador/analista, com o objetivo de melhorar o rendimento da equipa. Com isto, os treinadores, pretendem controlar e conhecer o m?ximo de fatores que influenciam o jogo, sejam estes da pr?pria equipa ou da equipa advers?ria e Observa??o e An?lise pode ser uma mais-valia para a concretiza??o desse objetivo. Este est?gio foi realizado no ?mbito do 2.? ciclo do Mestrado em Treino Desportivo, da Escola Superior de Desporto e Lazer, na equipa s?nior da Associa??o Desportiva de Ponte da Barca, ao longo da ?poca desportiva de 2021/2022. Os objetivos definidos foram os seguintes: desenvolver as capacidades de an?lise e observa??o in loco; desenvolver a capacidade de observa??o e an?lise atrav?s de v?deo e aperfei?oar a utiliza??o de softwares de an?lise de jogo. Atrav?s da elabora??o deste relat?rio, exponho todo o trabalho realizado, especificamente na ?rea da Observa??o e An?lise de jogo dos advers?rios e da pr?pria equipa, descrevendo os comportamentos coletivos e individuais dos atletas nos diferentes momentos do jogo atrav?s de v?deos e relat?rios escritos. Assim sendo, apresenta as seguintes partes fundamentais: i) introdu??o, ii) revis?o da literatura, iii) enquadramento da pr?tica, iv) plano de atividades, v) relat?rio das atividades, v) considera??es finais. De modo resumido pode concluir-se que a Observa??o e An?lise ? uma ferramenta decisiva para a prepara??o do treino e do jogo, que ? utilizada por treinadores de diferentes n?veis competitivos e que a transmiss?o de informa??es acerca das equipas advers?rias tem influ?ncia no rendimento dos jogadores e equipas.In recent years, we have witnessed a marked development in the football scene, with a greater professionalization of the different dimensions that encompass the game. Observation and Analysis in Football has evolved exponentially and there is an increasing need for the insertion of multidisciplinary technical teams that include an observer/analyst, with the aim of improving the team's performance. With this, the coaches, intend to know and control the maximum of factors that influence the game, be they the team itself or the opposing team and Observation and Analysis can be an asset to achieve this objective. This internship was carried out within the scope of the 2nd cycle of the Masters in Sports Training, from the Escola Superior de Desporto e Lazer, in the senior team of Associa??o Desportiva de Ponte da Barca, throughout the 2021/2022 sports season. The objectives defined were the following: develop on-the-spot analysis and observation skills; develop observation and analysis skills through video and improve the use of game analysis software. Through the elaboration of this report, I expose all the work carried out, specifically in the area of Observation and Analysis of the game of the opponents and the team itself, describing the collective and individual behaviors of the athletes in the different moments of the game through videos and written reports. Therefore, it presents the following fundamental parts: i) introduction, ii) literature review, iii) practice framework, iv) activity plan, v) activities report, v) final considerations. In summary, it can be concluded that Observation and Analysis is a decisive tool for training and game preparation, it is used by coaches of different competitive levels and the transmission of information about opposing teams has an influence on the performance of players and teams

Combining Qualitative and Quantitative Analysis in Football with SportSense

The task of performance analysts and coaches in football (and other team sports) is manifold: they need to assess the performance of individual players of their team, they need to monitor the interaction between players of their team and their tactical compliance, and they need to analyze other teams. For this, they usually have to consider various sources of information: video footage, tracking data, event data, and aggregated statistics. On the basis of this information, analysts have to generate quantitative summaries of events including their spatial and temporal distribution, and the qualitative assessment of individual events by considering the associated video footage. In this paper, we present SportSense, a system for sports video retrieval, that seamlessly combines quantitative and qualitative analysis. For this, SportSense provides dedicated filters that help analysts in selecting the events they are interested in. Moreover, it supports the comparative analysis of stored queries with respect to specific parameters. Essentially, SportSense allows to easily switch between qualitative and quantitative analyses to support coaches and analysts in a best possible way in their task. Based on a user study, we show the effectiveness of the proposed approach