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

Competition-Based Success Factors During the Talent Pathway of Elite Male Swimmers

Marginal differences in race results between top swimmers have evoked the interest in competition-based success factors of long-term athlete development. To identify novel factors for the multi-dimensional model of talent development, the aim of the study was to investigate annual variation in competition performance (ACV), number of races per year, and age. Therefore, 45,398 race results of all male participants (n = 353) competing in individual events, i.e., butterfly, backstroke, breaststroke, freestyle, and individual medley, at the 2018 European Long-Course Swimming Championships (2018EC) were analyzed retrospectively for all 10 years prior to the championships with Pearson's correlation coefficient and multiple linear regression analysis. Higher ranked swimmers at the 2018EC showed significant medium correlations with a greater number of races per year and small but significant correlations with higher ACV in 10 and nine consecutive years, respectively, prior to the championships. Additionally, better swimmers were older than their lower ranked peers (r = −0.21, p < 0.001). Regression model explained a significant proportion of 2018EC ranking for 50 m (47%), 100 m (45%), 200 m (31%), and 400 m races (29%) but not for 800 and 1,500 m races with number of races having the largest effect followed by age and ACV. In conclusion, higher performance variation with results off the personal best in some races did not impair success at the season's main event and young competitors at international championships may benefit from success chances that increase with age. The higher number of races swum per year throughout the career of higher ranked swimmers may have provided learning opportunities and specific adaptations. Future studies should quantify these success factors in a multi-dimensional talent development model

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

A Flexible Approach to Football Analytics: Assessment, Modeling and Implementation

Quantitative analysis in football is difficult due to the complexity and continuous fluidity of the game. Even though there is an increased accessibility of spatio-temporal data, scientific approaches to extract valuable information are seldomly useful in practice. We propose a new approach to building an information system for football. This approach consists of a method to extract football-specific concepts from interviews, to formalize them in a performance model, and to define and implement the data structures and algorithms in StreamTeam , a framework for the detection of complex (team) events. In this paper we present this approach in detail and provide an example for its use

A Flexible Approach to Football Analytics: Assessment, Modeling and Implementation (Abstract)

Quantitative analysis in football is difficult due to the complexity and continuous fluidity of the game. Even though there is an increased accessibility of spatio-temporal data, scientific approaches to extract valuable information are seldomly useful in practice. We propose a new approach to building an information system for football. This approach consists of a method to extract football-specific concepts from interviews, to formalize them in a performance model, and to define and implement the data structures and algorithms in StreamTeam, a framework for the detection of complex (team) events. In this paper we present this approach in detail and provide an example for its use

StreamTeam-Football: Analyzing Football Matches in Real-Time on the Basis of Position Streams

In recent years, Big Data has become an important topic in many areas of our daily lives, including sports. Almost all professional clubs analyze matches to improve the performance of their teams. However, events are still predominantly captured manually, although many sensor-based and video-based tracking systems exist which provide the positions of the players and the ball in real-time. This manual process is tedious and errorprone. In this paper, we propose STREAMTEAM-FOOTBALL, an open source football analysis application, to fill this gap. STREAMTEAM-FOOTBALL allows to analyze football matches fully automatically and in real-time on the basis of tracked position data using a data stream analysis approach. Our evaluations confirm the effectiveness of our automated analysis and further show the scalability of STREAMTEAM-FOOTBALL by its ability to analyze multiple football matches in parallel

Performance indicators of 4 matches in 2014/15 Bundesliga season.

<p>Goals (G), Shots at Goal (SG), Passing Accuracy (PA), Tackling Rate (TR) and Ball Possession (BP) provide inadequate information to assess the course of a match correctly. Match Performance (MP) allow a significantly better assessment of whether a team has been “lucky” and won through an individual action or has been able to set up many dangerous situations and has “earned” the win.</p

Performance variables in the course of the match Hannover (H96) vs. Dortmund (BVB).

<p>Danger for an interval (DA_i) is visualized by bars, Current Performance (CP) by dashed lines and Current Dominance (CD) by a solid line. DA_i and CP were inverted for the away team. CD is shown from the perspective of the home team.</p

Course of Danger in a match scenario.

<p>Spatial configuration and value of model components are shown in four key moments.</p