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

Discovering sequential patterns and interrelations among multiple diseases in electronic medical records using cSPADE algorithm

BACKGROUND

The intricate relationships between diseases are characterized by the sequence and temporal intervals of their onset, which are critical for understanding the essence of comorbidity and predicting disease progression. This study seeks to investigate the interdependencies and chronological order of various diseases that occur in the same patient by employing sequential pattern mining algorithms. Specifically, the research endeavors to delineate the disparities in the time intervals between the onset of distinct disorders and to scrutinize the concordance and discordance in disease sequence patterns across gender groups.

METHODS

Patient identity information, visit dates, and diagnostic data were aggregated from the electronic medical record databases of three large general hospitals. The diagnostic information included the International Classification of Diseases, Tenth Revision (ICD-10) codes, along with their corresponding descriptions. A total of 1,060,344 diagnostic entries from 269,973 patients who visited during 2012-2022 were incorporated into the mining model, which was constructed using the Sequential Pattern Discovery using Equivalence Classes (SPADE) algorithm.

RESULTS

A total of 212 highly supported sequential pattern rules were ultimately identified, most of which were related to disorders of the endocrine and circulatory systems. In 66 patterns, the order of disease incidence or diagnosis was relatively well-defined. The time interval between the onset of two diseases ranged from 1 to 2 years in most patterns. For patterns with short-term relationships, the interval was less than 2 months, whereas in some cases, the interval extended to 5 to 10 years. Among the extracted patterns, 176 exhibited stronger support in the male dataset compared to the female dataset. Patterns related to cardiovascular and liver diseases were more prevalent in males, while those associated with orthopedic and endocrine disorders showed higher prevalence in females.

CONCLUSION

Our findings demonstrate the effectiveness of the constrained SPADE (cSPADE) algorithm in comorbidity research and highlight several clinically significant sequential comorbidity patterns. These patterns are expected to contribute to disease prevention, etiological research, and the development of clinical decision support systems.

Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting

In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.

Smartboard: Visual Exploration of Team Tactics with LLM Agent

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

Determining the tactical and technical level of competitive tennis players using a competency model: a systematic review

Introduction

The aim of this systematic review is to provide an evidence-based synthesis of the literature on the topic of technical and tactical competencies of tennis players and to answer the following research questions: (1) What is the state of the art of research on technical and tactical competencies (i.e., skills and knowledge) and tennis; (2) What are the most important topics related to technical and tactical competencies in tennis players.

Methods

Electronic searches were conducted in Web of Science, PubMED and SPORTDiscus (August to September 2023). This systematic review was conducted in accordance with PRISMA guidelines. To reduce risk, all published literature was searched and primary studies were included. The search terms included skills or competencies, match or play, player and tennis and excluded studies on non-competitive tennis players-notation analysis, AI method, systematic review and validation of tools.

Results and discussion

Of the 390 publications found in these searches, 13 articles were considered relevant and included in this study. They were divided into three categories: (1) technical-tactical skills, (2) match situations and (3) match performance. There was clear evidence that there is a test instrument for analyzing tactical-technical skills that has sufficient reliability and validity and is of practical value to tennis coaches. The development of tactical-technical skills is influenced by method (variability between/within skills), conditions (court size, ball type) and areas of development (situational awareness, anticipation, decision making). There are differences in match and stroke performance between different quality groups (professionals, juniors), which can also be influenced by mental strength. For a comprehensive study of tennis players' abilities, the use of modern technologies is possible and necessary in the future. Future research should focus on the creation of competency models for the playing level of tennis players, which could include at least three key elements: (1) key competencies, (2) description of standards, (3) evidence.

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.

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

Action-Evaluator: A Visualization Approach for Player Action Evaluation in Soccer

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

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.

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.

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.