Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.

Mediation analysis is crucial for diagnosing indirect causal relations in many scientific fields. However, mediation analysis of nominal variables requires examining and comparing multiple total effects and their corresponding direct/indirect causal effects derived from mediation models. This process is tedious and challenging to achieve with classical analysis tools such as Excel tables. In this study, we worked closely with experts from two scientific domains to design MediVizor, a visualization system that enables experts to conduct visual mediation analysis of nominal variables. The visualization design allows users to browse and compare multiple total effects together with the direct/indirect effects that compose them. The design also allows users to examine to what extent the positive and negative direct/indirect effects contribute to and reduce the total effects, respectively. We conducted two case studies separately with the experts from the two domains, sports and communication science, and a user study with common users to evaluate the system and design. The positive feedback from experts and common users demonstrates the effectiveness and generalizability of the system.

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.

We propose SimuExplorer, a visualization system to help analysts explore how player behaviors impact scoring rates in table tennis. Such analysis is indispensable for analysts and coaches, who aim to formulate training plans that can help players improve. However, it is challenging to identify the impacts of individual behaviors, as well as to understand how these impacts are generated and accumulated gradually over the course of a game. To address these challenges, we worked closely with experts who work for a top national table tennis team to design SimuExplorer. The SimuExplorer system integrates a Markov chain model to simulate individual and cumulative impacts of particular behaviors. It then provides flow and matrix views to help users visualize and interpret these impacts. We demonstrate the usefulness of the system with case studies and expert interviews. The experts think highly of the system and have obtained insights into players' behaviors using it.

How could soccer player migration impact national team performance, or vice versa? The answer to this question could play an essential role in making appropriate decisions and policies regarding the international mobility of soccer players. However, answering such a question faces two main challenges, including the complex relationship between variables in multi-attribute temporal data describing migrated players and national team performance, and the interpretation of analysis results in policymaking scenarios. In this work, we have closely collaborated with domain experts and characterized the problems of soccer player migration analysis. To address the first challenge, we adapt a cross-lagged panel analysis model into the player migration analysis problem. This cross-lagged panel analysis model is effective to evaluate the impact strength between player migration and national team performance, and straightforward to reveal the causal relationship. To address the second challenge, we design and develop a visual analytics system, MIG-Viewer, to help the experts to interpret the results of the proposed model efficiently. With MIG-Viewer, the experts can navigate the countries of interest in accordance with migration strategy, conduct comprehensive analysis with the comparison of impact strength, and adjust player migration and inspect further details of a specific country. We present two case studies using global player migration data since 1992 with three soccer analysis experts to demonstrate the effectiveness and usefulness of the system.

The rapid development of massively multiplayer online role-playing games (MMORPGs) has led operators to record huge amounts of fine-grained data from the in-game activities of players. These data provide considerable opportunities with which to study the dynamic interplay among player behaviors and investigate the roles of various social structures that underlie such interplay. However, modeling and visualizing these behavioral data remain a challenge. In this study, we propose a novel influence-susceptible model to measure the dynamic interplay among multiple behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. BeXplorer enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded.

With the rapid development of massively multiplayer online role-playing games (MMORPGs), a huge amount of fine-grained data on the in-game activities of players have been recorded by MMORPGs operators. These data provide considerable opportunities with which to study the dynamic interplay between player behaviors and investigate the roles of various social structures that underlie such interplay. However, it is challenging to model and visualize these behavioral data. This study proposes a novel influence-susceptible model to measure the dynamic interplay between behaviors. Based on this model, we introduce a new visual analytics system called BeXplorer. This system enables analysts to interactively explore the dynamic interplay between player purchase and communication behaviors and to examine the manner in which this interplay is bound by social structures where players are embedded. Three case studies and a task-based evaluation are conducted to demonstrate the effectiveness and applicability of our method.

The rapid development of information technology paved the way for the recording of fine-grained data, such as stroke techniques and stroke placements, during a table tennis match. This data recording creates opportunities to analyze and evaluate matches from new perspectives. Nevertheless, the increasingly complex data poses a significant challenge to make sense of and gain insights into. Analysts usually employ tedious and cumbersome methods which are limited to watching videos and reading statistical tables. However, existing sports visualization methods cannot be applied to visualizing table tennis competitions due to different competition rules and particular data attributes. In this work, we collaborate with data analysts to understand and characterize the sophisticated domain problem of analysis of table tennis data. We propose iTTVis, a novel interactive table tennis visualization system, which to our knowledge, is the first visual analysis system for analyzing and exploring table tennis data. iTTVis provides a holistic visualization of an entire match from three main perspectives, namely, time-oriented, statistical, and tactical analyses. The proposed system with several well-coordinated views not only supports correlation identification through statistics and pattern detection of tactics with a score timeline but also allows cross analysis to gain insights. Data analysts have obtained several new insights by using iTTVis. The effectiveness and usability of the proposed system are demonstrated with four case studies.