Tac-Simur: Tactic-based Simulative Visual Analytics of Table Tennis

Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base

Table tennis is one of the most popular sports in the world, and it plays a positive role in the overall development of people's physical and mental health. This study develops an AI table tennis coaching system using a Multimodal Large Language Model with a table tennis knowledge base, aiming to provide precise training guidance and match strategies for table tennis beginners.

METHOD

By using visual recognition technology, motion capture technology, and advanced multimodal large language models with a comprehensive professional table tennis knowledge base, the system accurately identifies common errors made by beginners and provides targeted training guidance.

RESULT

The AI Table Tennis Coaching System demonstrates high accuracy in identifying mistakes made by beginner players, particularly in recognizing arm-related errors and racket-related errors, with accuracies reaching 73% and 82% respectively.

CONCLUSION

The system operates at low costs, is easy to deploy, and offers a high cost-performance ratio, providing effective technological support for table tennis teaching and training. The AI table tennis coaching system is expected to play a significant role in enhancing training efficiency, promoting athlete skill improvement, and popularizing the sport. Future research will focus on improving the accuracy of footwork recognition in AI table tennis coaching systems and expanding their capability to provide training guidance for high-level athletes, thereby promoting the overall advancement of table tennis.

Sportify: Question Answering with Embedded Visualizations and Personified Narratives for Sports Video

As basketball's popularity surges, fans often find themselves confused and overwhelmed by the rapid game pace and complexity. Basketball tactics, involving a complex series of actions, require substantial knowledge to be fully understood. This complexity leads to a need for additional information and explanation, which can distract fans from the game. To tackle these challenges, we present Sportify, a Visual Question Answering system that integrates narratives and embedded visualization for demystifying basketball tactical questions, aiding fans in understanding various game aspects. We propose three novel action visualizations (i.e., Pass, Cut, and Screen) to demonstrate critical action sequences. To explain the reasoning and logic behind players' actions, we leverage a large-language model (LLM) to generate narratives. We adopt a storytelling approach for complex scenarios from both first and third-person perspectives, integrating action visualizations. We evaluated Sportify with basketball fans to investigate its impact on understanding of tactics, and how different personal perspectives of narratives impact the understanding of complex tactic with action visualizations. Our evaluation with basketball fans demonstrates Sportify's capability to deepen tactical insights and amplify the viewing experience. Furthermore, third-person narration assists people in getting in-depth game explanations while first-person narration enhances fans' game engagement.

Effect of core strength training on the badminton player's performance: A systematic review & meta-analysis

BACKGROUND

Core strength training (CST) has been shown to improve performance in several sports disciplines. CST is recognized as one of the crucial elements that enhance athletic performance, particularly impacting badminton skills. Despite its popularity as a strength training method among badminton players, there is a lack of comprehensive studies examining the effectiveness of CST on the performance of these athletes.

OBJECTIVE

This study aims to ascertain CST's effects on badminton players' performance.

METHOD

This study followed PRISMA principles and conducted comprehensive searches in well-known academic databases (SCOPUS, Pubmed, CNKI, Web of Science, Core Collection, and EBSCOhost) up to August 2023. The inclusive criteria were established using the PICOS framework. Following their inclusion based on PICOS criteria, the selected studies underwent literature review and meta-analysis. The methodological quality of the assessments was evaluated using Cochrane Collaboration's risk of bias tools bias risk tools and recommendations for a graded assessment, development, and evaluation.

RESULTS

The analysis included participants aged 10-19 years from 13 studies of moderate quality, totaling 208 individuals. The CST intervention s lasted between 4 to 16 weeks, with a frequency of 1 to 4 sessions per week and each session lasting 20 to 120 minutes. Sample sizes across these studies ranged from 8 to 34 participants. According to the meta-analysis, CST significantly influenced badminton performance, particularly in areas of explosive power (ES = 0.03 P = 0.04), front-court skill (ES = 2.53, P = 0.003), and back-court skill (ES = 2.33, P = 0.002).

CONCLUSION

CST enhances badminton players' fitness (strength, power, balance, and stability), in situ (front/back-court) skills, and movement position hitting. However, its effects on speed, endurance, agility, flexibility, and coordination are unclear, revealing a research gap. The precise benefits of CST, especially on flexibility and specific hitting skills (smashes, clears, drives, net shots, crosscourt, push, and lift shots), need more investigation. Additionally, research on CST's impact on female athletes is significantly lacking.

TacPrint: Visualizing the Biomechanical Fingerprint in Table Tennis

Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players' habitual movement patterns and characteristics, allowing them to identify and improve technical weaknesses. Despite the potential, few studies have developed effective methods for generating such fingerprints. To address this gap, we propose TacPrint, a framework for generating a biomechanical fingerprint for each player. TacPrint leverages machine learning techniques to extract comprehensive features from biomechanics data collected by inertial measurement units (IMU) and employs the attention mechanism to enhance model interpretability. After generating fingerprints, TacPrint provides a visualization system to facilitate the exploration and investigation of these fingerprints. In order to validate the effectiveness of the framework, we designed an experiment to evaluate the model's performance and conducted a case study with the system. The results of our experiment demonstrated the high accuracy and effectiveness of the model. Additionally, we discussed the potential of TacPrint to be extended to other sports.

SimuExplorer: Visual Exploration of Game Simulation in Table Tennis

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.

Effects of technical-tactical skills on enhancing scoring efficiency when competing with opponents using different handedness in table tennis matchups

Technical-tactical skills are key to determining table tennis match performance. Identifying the skills that can increase scoring probability when competing against opponents with different racket handedness is vital. The aim of this study was to investigate the technical-tactical actions that could significantly improve scoring efficiency in SH (same-handedness) matches and OH (opposite-handedness) matches. The statistics of 72 top-level men's singles matches were collected (40 SH matches and 32 OH matches). An independent samples t-test was performed to evaluate the differences in the scoring efficiency of the point-winning shot. The results showed that the following maneuvers were crucial technical-tactical skills to increase scoring probability: (1) during the mutual-restriction phase, executing a forehand short push to forehand during service round and a backhand long push to backhand during receiving round against SH opponents (p < 0.05, ES: 0.59-0.70), and a backhand short push to middle in receiving round against OH opponents (p < 0.01, ES = 0.66); (2) during the initial-attack-and-counterattack phase, performing an initial forehand flip and a loop (drive) to forehand, a counter backhand loop (drive) to backhand during both types of rounds, and a forehand block (lob) to forehand during service round against SH opponents (p < 0.05, ES: 0.52-1.18), and an initial forehand loop (drive) to backhand and a counter backhand loop (drive) to forehand during both types of rounds, an initial forehand flip and a loop (drive) to backhand during service round, and an initial backhand flip to middle during receiving round against OH opponents (p < 0.05, ES: 0.45-0.99); and (3) during the topspin-exchange phase, hitting a forehand loop (drive) to forehand during service round and a backhand loop (drive) to backhand during both types of rounds against SH opponents (p < 0.05, ES: 0.51-1.32), a forehand loop (drive) to backhand during both types of rounds against OH opponents (p < 0.01, ES: 0.77-0.91). Professionals are recommended to concentrate on the enhancement of the abovementioned key technical-tactical skills to achieve better match performance.

Is he or she the main player in table tennis mixed doubles?

BACKGROUND

Since mixed doubles have been set up in the table tennis competition of the 2020 Tokyo Olympic Games, coaches and players have paid increasing attention to mixed doubles matches. This study aims to compare and analyse male and female performance in the different contexts of table tennis mixed doubles as well as the impact of their performance on the probability of winning matches.

METHODS

100 matches between the top 30 mixed doubles were selected (based on the world rankings for 2019 to 2021) as samples. According to the stroke order of a mixed doubles match, the players are divided into four groups: male versus male (P_m-m), male versus female (P_m-f), female versus male (P_f-m) and female versus female (P_f-f). Then, new methods with concepts are proposed to analyse stroke performance by four groups of players in various competition contexts of mixed doubles.

RESULTS

(1) The stroke performance in the first four strokes was much better than that in the after four strokes (P < 0.05), and males performed better than female players in the first four strokes (P < 0.05). (2) The stroke performance of each group for winning matches was significantly better than that for losing matches (P < 0.01). (3) Players in each group performed better (P < 0.01) in the ahead and under control states than in the behind and lost control states. However, most stroke performance within the four groups was not significantly different in different states. (4) The impact of scoring rates by different groups on the winning probability of a mixed doubles match from high to low was P_m-f > P_f-f > P_m-m > P_f-m. (5) In the actual competition, the percentage of female players serving first in each game is 79.64%, and the percentage of the stroke group of female players serving to female players receiving (P_f-f) is 58.25%.

CONCLUSION

This study considers several competition contexts to analyse the performance of male and female players in table tennis mixed doubles. We propose that the stroke performance of male versus female players is the most important factor affecting the results of mixed doubles matches. In addition, selecting the first server or first receiver in each game reasonably and analysing the stroke orders emphatically are also very important in mixed doubles.

ABDOMINAL CORE MUSCLE STRENGTH TRAINING IN BADMINTON PLAYERS

ABSTRACT Introduction: Badminton is a sport of intense confrontation and variable transitions between attack and defense, where the athlete maintains an extreme body dynamic. Objective: Study the effects of abdominal core muscle strength training on badminton players. Methods: A screening method was used by randomly selecting 20 male badminton players. The effects of abdominal core muscle strength training and the athletes' specific skills were evaluated by a protocol after 10 weeks of strength training. Results: The results of the two groups were significantly different by t-test, p<0.05. Both improved in all indicators. The experimental group showed higher scores in 6 indicators than the control group, with p<0.01 having a highly significant difference. Conclusion: Abdominal core muscle strength training can effectively improve body stability, optimize the technical movements of badminton players, and play a positive role in body stability. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

INFLUENCE OF COMPOUND TRAINING ON PHYSICAL FITNESS IN BADMINTON ATHLETES

ABSTRACT Introduction: Currently, compound rapid stretching training has been explored by sports researchers. However, most research focuses on soccer, basketball, volleyball, and other sports that require vertical jump fitness, but little attention is paid to badminton. Objective: Study the influence of compound rapid stretching training on fitness in professional badminton athletes. Methods: By experimental and statistical methods, 16 outstanding college badminton players were randomly divided into composite training and control groups. The sports performance test (T-shaped run) and short-distance mobility test (5-10-5), were evaluated and compared before and after the intervention, adding a specific rapid stretching training protocol to the composite group. Results: Comparatively, the T-run and hexagonal jump tests were significantly improved in the composite group (P<0.01). The time required to complete the 5-10-5 moving test progressed from 3.980s to 3.763s, significantly longer than the control group (P<0.05). Conclusion: 6-week training with rapid stretching was shown to significantly improve the fitness of college badminton athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

UNSTABLE PLATFORM TRAINING IN TENNIS PLAYERS

ABSTRACT Introduction: The constant maintenance of muscular strength is a highly relevant care in training for tennis practice. The unstable platform is developed with the improvement of modern and technological resources, and the need to verify its effects on the athletes’ physical fitness is requested. Objective: Study the effect of unstable platform training on tennis players’ balance ability and assertiveness. Methods: 12 male tennis-practicing students of the Beijing Sports University School of Education 2020 class were randomly selected as research volunteers. Through a literature search, data statistics, and other methods, they were randomly divided into an experimental group and a control group. After 6 weeks of traditional training in the control group and the addition of unstable platform balance training in the experimental group, the results were compared. Results: The ability to stand with eyes open on the dominant leg was significantly improved in the experimental group. After routine training and balance training, the athletes in the experimental group had a great improvement in the assertiveness of passes (P<0.05), and the stability of the service was also improved (P<0.05). Conclusion: After training, all physical abilities were improved. The unstable platform training is a valid resource to improve athletes’ balance and assertiveness ability. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Tac-Trainer: A Visual Analytics System for IoT-based Racket Sports Training

Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA.

RASIPAM: Interactive Pattern Mining of Multivariate Event Sequences in Racket Sports

Experts in racket sports like tennis and badminton use tactical analysis to gain insight into competitors' playing styles. Many data-driven methods apply pattern mining to racket sports data - which is often recorded as multivariate event sequences - to uncover sports tactics. However, tactics obtained in this way are often inconsistent with those deduced by experts through their domain knowledge, which can be confusing to those experts. This work introduces RASIPAM, a RAcket-Sports Interactive PAttern Mining system, which allows experts to incorporate their knowledge into data mining algorithms to discover meaningful tactics interactively. RASIPAM consists of a constraint-based pattern mining algorithm that responds to the analysis demands of experts: Experts provide suggestions for finding tactics in intuitive written language, and these suggestions are translated into constraints to run the algorithm. RASIPAM further introduces a tailored visual interface that allows experts to compare the new tactics with the original ones and decide whether to apply a given adjustment. This interactive workflow iteratively progresses until experts are satisfied with all tactics. We conduct a quantitative experiment to show that our algorithm supports real-time interaction. Two case studies in tennis and in badminton respectively, each involving two domain experts, are conducted to show the effectiveness and usefulness of the system.

OBTracker: Visual Analytics of Off-ball Movements in Basketball

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

A Visible Analysis Approach for Table Tennis Tactical Benefit

Tactics are one of the winning factors in table tennis matches because they can reflect the technical strength and strategies of players. However, the current evaluation method of tactics is relatively simple, and it can be difficult to distinguish the pros and cons of different tactics. To solve these issues, this study used information from 60 matches played by top-25 men's right-handed shake-hand offensive players as samples and proposed a tactical benefit algorithm and a visible evaluation model to analyse tactical performance. The model could help coaches and players find meaningful tactics quickly and easily and make reasonable comparisons between them. The results showed that 1) the numbers of tactics based on the tactical association-mining model and the L-shaped curve were 37 in the first six strokes. Among them, there were 9 selected tactics in the first to third strokes, the third to fifth strokes, and the second to fourth strokes, respectively, and 10 selected tactics in the fourth to sixth strokes. 2) The visible evaluation model of tactical benefit can determine the difference between the frequency, scoring rate, and benefit of different tactics as mapped into the model as well as quickly and effectively discover the distribution of tactics and measure the advantages and disadvantages of tactics from multiple dimensions.

Interactive Visual Exploration of Longitudinal Historical Career Mobility Data

The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts.

An overview of Human Action Recognition in sports based on Computer Vision

Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and tennis to detect players and recognize their actions and teams' activities during training, matches, warm-ups, or competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the action's duration, and identify the action type. The main idea of HAR in sports is to monitor a player's performance, that is, to detect the player, track their movements, recognize the performed action, compare various actions, compare different kinds and skills of acting performances, or make automatic statistical analysis.

As an action that can occur in the sports field refers to a set of physical movements performed by a player in order to complete a task using their body or interacting with objects or other persons, actions can be of different complexity. Because of that, a novel systematization of actions based on complexity and level of performance and interactions is proposed.

The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose.

Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training

The Internet of Things is an important link in the future development of the Internet and will have a significant impact on development over the next five years or so. The purpose of this article is to learn how to use the Internet of Things to learn and practice table tennis and find new ways to integrate table tennis and the internet. This article proposes to build a detection system for IoT sensors and collect information about the ball's movement state and the athlete's movement state during table tennis. Through data analysis, we better guide athletes to develop table tennis training. At the same time, it also uses knowledge innovation to integrate the Internet of Things with the reality of life and better derive the development of the Internet of Things artificial intelligence. Based on this, this paper designs the construction of IoT sensors to collect the sports status of table tennis enthusiasts and verify the sports experience of table tennis enthusiasts by comparing factors such as physical training, basic skills and mobile training, interest, and confidence. The experimental results of this article show that the Internet of Things artificial intelligence has great potential, and it can innovatively promote industrial change. It better realizes the teaching and training of table tennis players and improves the teaching and training ability of table tennis by 20%.

Compass: Towards Better Causal Analysis of Urban Time Series

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.

TIVEE: Visual Exploration and Explanation of Badminton Tactics in Immersive Visualizations

Tactic analysis is a major issue in badminton as the effective usage of tactics is the key to win. The tactic in badminton is defined as a sequence of consecutive strokes. Most existing methods use statistical models to find sequential patterns of strokes and apply 2D visualizations such as glyphs and statistical charts to explore and analyze the discovered patterns. However, in badminton, spatial information like the shuttle trajectory, which is inherently 3D, is the core of a tactic. The lack of sufficient spatial awareness in 2D visualizations largely limited the tactic analysis of badminton. In this work, we collaborate with domain experts to study the tactic analysis of badminton in a 3D environment and propose an immersive visual analytics system, TIVEE, to assist users in exploring and explaining badminton tactics from multi-levels. Users can first explore various tactics from the third-person perspective using an unfolded visual presentation of stroke sequences. By selecting a tactic of interest, users can turn to the first-person perspective to perceive the detailed kinematic characteristics and explain its effects on the game result. The effectiveness and usefulness of TIVEE are demonstrated by case studies and an expert interview.

TacticFlow: Visual Analytics of Ever-Changing Tactics in Racket Sports

Event sequence mining is often used to summarize patterns from hundreds of sequences but faces special challenges when handling racket sports data. In racket sports (e.g., tennis and badminton), a player hitting the ball is considered a multivariate event consisting of multiple attributes (e.g., hit technique and ball position). A rally (i.e., a series of consecutive hits beginning with one player serving the ball and ending with one player winning a point) thereby can be viewed as a multivariate event sequence. Mining frequent patterns and depicting how patterns change over time is instructive and meaningful to players who want to learn more short-term competitive strategies (i.e., tactics) that encompass multiple hits. However, players in racket sports usually change their tactics rapidly according to the opponent's reaction, resulting in ever-changing tactic progression. In this work, we introduce a tailored visualization system built on a novel multivariate sequence pattern mining algorithm to facilitate explorative identification and analysis of various tactics and tactic progression. The algorithm can mine multiple non-overlapping multivariate patterns from hundreds of sequences effectively. Based on the mined results, we propose a glyph-based Sankey diagram to visualize the ever-changing tactic progression and support interactive data exploration. Through two case studies with four domain experts in tennis and badminton, we demonstrate that our system can effectively obtain insights about tactic progression in most racket sports. We further discuss the strengths and the limitations of our system based on domain experts' feedback.

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.

Tac-Miner: Visual Tactic Mining for Multiple Table Tennis Matches

In table tennis, tactics specified by three consecutive strokes represent the high-level competition strategies in matches. Effective detection and analysis of tactics can reveal the playing styles of players, as well as their strengths and weaknesses. However, tactical analysis in table tennis is challenging as the analysts can often be overwhelmed by the large quantity and high dimension of the data. Statistical charts have been extensively used by researchers to explore and visualize table tennis data. However, these charts cannot support efficient comparative and correlation analysis of complicated tactic attributes. Besides, existing studies are limited to the analysis of one match. However, one player's strategy can change along with his/her opponents in different matches. Therefore, the data of multiple matches can support a more comprehensive tactical analysis. To address these issues, we introduced a visual analytics system called Tac-Miner to allow analysts to effectively analyze, explore, and compare tactics of multiple matches based on the advanced embedding and dimension reduction algorithms along with an interactive glyph. We evaluate our glyph's usability through a user study and demonstrate the system's usefulness through a case study with insights approved by coaches and domain experts.

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a team's employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a team's passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.

A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.

Dodge and survive: Modeling the predatory nature of dodgeball

The analysis of games and sports as complex systems can give insights into the dynamics of human competition and has been proven useful in soccer, basketball, and other professional sports. In this paper, we present a model for dodgeball, a popular sport in U.S. schools, and analyze it using an ordinary differential equation (ODE) compartmental model and stochastic agent-based game simulations. The ODE model reveals a rich landscape with different game dynamics occurring depending on the strategies used by the teams, which can in some cases be mapped to scenarios in competitive species models. Stochastic agent-based game simulations confirm and complement the predictions of the deterministic ODE models. In some scenarios, game victory can be interpreted as a noise-driven escape from the basin of attraction of a stable fixed point, resulting in extremely long games when the number of players is large. Using the ODE and agent-based models, we construct a strategy to increase the probability of winning.