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Example of a soccer tournament configuration. On the left, the group stage (column with teams badges) results into a ranking after games have ended. On the right, the elimination bracket phase (for the best group stage teams) determines who the winner of the competition will be.
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An advanced interface for sports tournament predictions uses direct manipulation to allow users to make nonlinear predictions. Unlike previous interface designs, the interface helps users focus on their prediction tasks by enabling them to first choose a winner and then fill out the rest of the bracket. In real-world tests of the proposed interface...
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... consider sports tournaments with a com- mon configuration: the combination of a ranking phase and a bracket phase (see Figure 2). Usu- ally, the ranking phase precedes the bracket phase (and is called the group stage), during which teams play against each other once or twice. ...
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... ally, the ranking phase precedes the bracket phase (and is called the group stage), during which teams play against each other once or twice. The left side of Figure 2 shows such a group stage oc- curring at the beginning of a tournament. The best-ranked teams after the group phase enter the second phase where teams are assigned an opponent and are eliminated after each round. ...
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... best-ranked teams after the group phase enter the second phase where teams are assigned an opponent and are eliminated after each round. The right side of Figure 2 shows such a stage that looks like a bracket converging to the winner of the tournament. ...
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... is where there is a divergence between the current interfaces and the way users perform pre- dictions. Current interfaces only implement the structure of predictions illustrated in Figure 2 as the sum of a series of games. The user's mental model, however, follows the opposite path: it first concentrates on the tournament's outcome, and then focuses on finding individual game results given the outcome. ...
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... found that betting websites sometimes are already populated with pre- diction suggestions (for example, providing odds) and allow users to enter their predictions. Figure 2 shows the typical interface for sport brackets (both for predictions and results communica- tion) that we observed. Most of the websites used standard HTML widgets for input such as check- boxes, dropdown lists, and text inputs, and they organized team options using a bracket layout. ...
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... of the websites used standard HTML widgets for input such as check- boxes, dropdown lists, and text inputs, and they organized team options using a bracket layout. To successfully enter their predictions, users have to manually input data for each team by game and start with the first round of games to end up with the final (from left to right in Figure 2). ...
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... circle's size indicates the visitor's number of interactions (max 4,354 interactions). The horizontal axis is the bracket size (32 is a full bracket), and the vertical axis is the time spent on the website (in seconds with a logarithmic scale). We added a low opacity to circles to better show the distribution of dense areas by reducing overplotting. ...
Citations
... Visualization has long been used in sports to present data [56], including box scores [25], tracking data [18,44,55,81], and metadata [77]. Sports visualizations are mainly used for post-game analysis or in-game informing purposes. ...
We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the fndings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users' gaze, helping casual fans appreciate basketball games without being overwhelmed. We confrmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.
... The visualization of sports data can be divided into two categories from the perspective of content analysis. The first category represents the full tournament or league season, in which data either show the points and rankings of each team during the season [10] or provide support for game prediction [20]. The second category is meant to analyze a single game, in which the situational dynamics of the game and the game information of two competing teams are presented. ...
Fencing is a sport that relies heavily on the use of tactics. However, most existing methods for analyzing fencing data are based on statistical models in which hidden patterns are difficult to discover. Unlike sequential games, such as tennis and table tennis, fencing is a type of simultaneous game. Thus, the existing methods on the sports visualization do not operate well for fencing matches. In this study, we cooperated with experts to analyze the technical and tactical characteristics of fencing competitions. To meet the requirements of the fencing experts, we designed and implemented FencingVis, an interactive visualization system for fencing competition data.The action sequences in the bout are first visualized by modified bar charts to reveal the actions of footworks and bladeworks of both fencers. Then an interactive technique is provided for exploring the patterns of behavior of fencers. The different combinations of tactical behavior patterns are further mapped to the graph model and visualized by a tactical flow graph. This graph can reveal the different strategies adopted by both fencers and their mutual influence in one bout. We also provided a number of well-coordinated views to supplement the tactical flow graph and display the information of the fencing competition from different perspectives. The well-coordinated views are meant to organically integrate with the tactical flow graph through consistent visual style and view coordination. We demonstrated the usability and effectiveness of the proposed system with three case studies. On the basis of expert feedback, FencingVis can help analysts find not only the tactical patterns hidden in fencing bouts, but also the technical and tactical characteristics of the contestant.
... However, some researchers have also enabled direct manipulation of graphical encodings in other visualization types. This includes the direct manipulation of: position of cells in table visualizations to either steer the underlying ranking model [46] or explore rankings [31,44]; angle of a pie chart segment to navigate the time dimension [20]; rows and columns in matrix visualizations [30,42]; nodes in tree visualizations [45]; and position of tokens in unit based visualizations [16]. ...
We investigate direct manipulation of graphical encodings as a method for interacting with visualizations. There is an increasing interest in developing visualization tools that enable users to perform operations by directly manipulating graphical encodings rather than external widgets such as checkboxes and sliders. Designers of such tools must decide which direct manipulation operations should be supported, and identify how each operation can be invoked. However, we lack empirical guidelines for how people convey their intended operations using direct manipulation of graphical encodings. We address this issue by conducting a qualitative study that examines how participants perform 15 operations using direct manipulation of standard graphical encodings. From this study, we 1) identify a list of strategies people employ to perform each operation, 2) observe commonalities in strategies across operations, and 3) derive implications to help designers leverage direct manipulation of graphical encoding as a method for user interaction.
... However, some researchers have also enabled direct manipulation of graphical encodings in other visualization types. This includes the direct manipulation of: position of cells in table visualizations to either steer the underlying ranking model [46] or explore rankings [31,44]; angle of a pie chart segment to navigate the time dimension [20]; rows and columns in matrix visualizations [30,42]; nodes in tree visualizations [45]; and position of tokens in unit based visualizations [16]. ...
We investigate direct manipulation of graphical encodings as a method for interacting with visualizations. There is an increasing interest in developing visualization tools that enable users to perform operations by directly manipulating graphical encodings rather than external widgets such as checkboxes and sliders. Designers of such tools must decide which direct manipulation operations should be supported, and identify how each operation can be invoked. However, we lack empirical guidelines for how people convey their intended operations using direct manipulation of graphical encodings. We address this issue by conducting a qualitative study that examines how participants perform 15 operations using direct manipulation of standard graphical encodings. From this study, we 1) identify a list of strategies people employ to perform each operation, 2) observe commonalities in strategies across operations, and 3) derive implications to help designers leverage direct manipulation of graphical encoding as a method for user interaction.
... The visualization of sports data can be divided into two categories from the perspective of content analysis. The first category represents the full tournament or league season, in which data either show the points and rankings of each team during the season [10] or provide support for game prediction [20]. The second category is meant to analyze a single game, in which the situational dynamics of the game and the game information of two competing teams are presented. ...
Fencing is a sport that relies heavily on the use of tactics. However, most existing methods for analyzing fencing data are based on statistical models in which hidden patterns are difficult to discover. Unlike sequential games, such as tennis and table tennis, fencing is a type of simultaneous game. Thus, the existing methods on the sports visualization do not operate well for fencing matches. In this study, we cooperated with experts to analyze the technical and tactical characteristics of fencing competitions. To meet the requirements of the fencing experts, we designed and implemented FencingVis, an interactive visualization system for fencing competition data. The action sequences in the bout are first visualized by modified bar charts to reveal the actions of footwork and bladework of both fencers. Then an interactive technique is provided for exploring the patterns of behavior of fencers. The different combinations of tactical behavior patterns are further mapped to the graph model and visualized by a tactical flow graph. This graph can reveal the different strategies adopted by both fencers and their mutual influence in one bout. We also provided a number of well-coordinated views to supplement the tactical flow graph and display the information of the fencing competition from different perspectives. The well-coordinated views are meant to organically integrate with the tactical flow graph through consistent visual style and view coordination. We demonstrated the usability and effectiveness of the proposed system with three case studies. On the basis of expert feedback, FencingVis can help analysts find not only the tactical patterns hidden in fencing bouts, but also the technical and tactical characteristics of the contestant.
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... The background pitch with recognizable landmarks is probably the most common way to tell which sport has been picked if this cannot simply be inferred. Additional landmarks can be added, such as those specific to sport events like a trophy showing for the UEFA Champion's League [Won13] or the FIFA World Cup [VP16]. ...
... Making tournament predictions has also been investigated from an input perspective. Vuillemot and Perin [VP16] explored the direct manipulation of tournament brackets to support people making their predictions using a visualization interface (see Figure 24). Instead of specifying with text or standard widgets which team progresses to the next stage of the tournament, users directly drag and drop teams to any stage of the tournament bracket. ...
... As a result, with few exceptions (e.g., [PBV16,TSLR07,PYHZ14]), most of the research papers we surveyed do not contribute a new generic technique but instead a tailored adaptation of an existing one. The most frequent visualization technique extensions we found were of node-link graphs (e.g., [PVF13b]), heatmaps (e.g., [PSBS12, Gol12, Bes16, AAB * 16, MB13]), small Figure 24: Tournament brackets are meta-data that can be used to support an intuitive prediction-making process, by dragging and dropping teams according to their expected performance in the competition [VP16]. Image courtesy of the authors, used with permission. ...
In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially‐growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real‐world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in‐game actions and trajectories), and meta‐data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high‐level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.
... In contrast to prior research, which has primarily used interaction logs to evaluate discovery of functionality in online interactive visualizations (e.g., [6], [23], [28]), our study combines log data with eye movement data, video and audio recordings to produce detailed records of the discovery process. ...
We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.
We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ush-ered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time feedback and facilitating tactical planning. DV techniques convert complex data into visual representations, improving the understanding of performance metrics. In this paper, we explore the potential of these emerging technologies to transform sports performance analysis, offering valuable resources to coaches and athletes. We aim to enhance athletes' performance, optimize training strategies, and inform decision-making processes. Additionally, we identify challenges and propose solutions for integrating these technologies into current sports analysis practices. This narrative review provides a comprehensive analysis of the historical context and evolution of performance analysis in sports science, highlighting current methods' merits and limitations. It delves into the transformative potential of AI, VR, AR, and DV, offering insights into how these tools can be integrated into a theoretical model.
Competitive sports data visualization is an increasingly important research direction in the field of information visualization. It is also an important basis for studying human behavioral pattern and activity habits. In this paper, we provide a taxonomy of sports data visualization and summarize the state-of-the-art research from four aspects of data types, main tasks and visualization techniques and visual analysis. Specifically, we first put sports data into two categories: spatiotemporal information and statistical information. Then, we propose three main tasks for competitive sports data visualization: feature presentation, feature comparison and feature prediction. Furthermore, we classify competitive sports data visualization techniques based on data characteristics into five categories: high-dimensional data visualization, time-series visualization, graph (network) visualization, glyph visualization and other visualization, and we analyze the relationship between major tasks and visualization techniques. We also introduce visual analysis research work of competitive sports, propose the features and limitations of competitive sports data, summarize multimedia visualization in competitive sports and finally discuss visual analysis evaluation. In this survey, we attempt to help readers to find appropriate techniques for different data types and different tasks. Our paper also intends to provide guidelines and references for future researchers when they study human behavior and moving patterns.
