A survey of competitive sports data visualization and visual analysis

Du, Meng; Yuan, Xiaoru

doi:10.1007/s12650-020-00687-2

Cited by 52 publications

(23 citation statements)

References 47 publications

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“…e huge user scale brings a huge amount of information and data, which contains great value. At present, sports health applications [7][8][9][10] are more focused on data collection [11] and data visualization display [12][13][14] and lack further analysis and mining of user health data. Reasonable advice and help is the main direction of sports and health applications in the intelligent age.…”

Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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Intelligent sports equipment and software have emerged in the field of sports as a result of the advancement of information technology, allowing professional athletes to collect and visually display the movement and physical signs of the human body to aid in the planning of sports strategies. Intuitive data, on the other hand, cannot assist ordinary people who lack professional knowledge in exercising correctly. As a result, in the field of intelligent sports and health, effective use of collected exercise and physical sign data to analyze the user’s personal physical condition and generate reasonable exercise suggestions has emerged as a research direction. In humans, the heart sound signal is a biological signal. It can help people detect and monitor heart health problems by analyzing the characteristics of heart sound signals. The goal of this paper is to use heart sound to identify and analyze athletes’ training health. It provides a revolutionary health analysis algorithm based on heart rhythm feature extraction and convolutional neural networks, which is based on exercise training. It greatly improves the accuracy of the recognition and prediction of the athlete’s training health status.

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Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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“…While information system architectures for sports specific environments have been discussed on the macro-level (Blobel and Lames, 2020 ), limited practical case studies exist to highlight how such digitization attempts can be implemented by sport practitioners in professional sporting environments at the micro-level (Mullane et al, 2011 ). Furthermore, in current contexts, implementing data visualization systems to illustrate sport specific information appears to be the favorable method to optimize the information flows in sporting environments (Perin et al, 2018 ; Du and Yuan, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Digitally Optimizing the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

Ranaweera¹,

Weaving²,

Zanin³

et al. 2022

Front. Sports Act. Living

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Practical case studies elaborating end-to-end attempts to improve the quality of information flows associated with athlete management processes are scarce in the current sport literature. Therefore, guided by a Business Process Management (BPM) approach, the current study presents the outcomes from a case study to optimize the quality of strength and conditioning (S&C) information flow in the performance department of a professional rugby union club. Initially, the S&C information flow was redesigned using integral technology, activity elimination and activity automation redesign heuristics. Utilizing the Lean Startup framework, the redesigned information flow was digitally transformed by designing data collection, management and visualization systems. Statistical tests used to assess the usability of the data collection systems against industry benchmarks using the System Usability Scale (SUS) administered to 55 players highlighted that its usability (mean SUS score of 87.6 ± 10.76) was well above average industry benchmarks of similar systems (Grade A from SUS scale). In the data visualization system, 14 minor usability problems were identified from 9 cognitive walkthroughs conducted with the High-Performance Unit (HPU) staff. Pre-post optimization information quality was subjectively assessed by administering a standardized questionnaire to the HPU members. The results indicated positive improvements in all of the information quality dimensions (with major improvements to the accessibility) relating to the S&C information flow. Additionally, the methods utilized in the study would be especially beneficial for sporting environments requiring cost effective and easily adoptable information flow digitization initiatives which need to be implemented by its internal staff members.

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“…The main goal of competitive sports is to yield a higher ranking sporting performance getting promotions or winning relevant competitions. Data concerning athletes' performance and their behavior are the essential core for competitive sports (Du & Yuan, 2021). Technological enhancements concerning remote sensor technology or motion capture systems have been developed together with advanced statistical approaches to model, infer or predict perfor-mance outcomes in sport [e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from the value of the performance indicators used to categorise athletes' and teams' achievements, there is still a lack of understanding regarding why and how some behaviours emerge in performance contexts (McGarry, 2009). It provides a basis for the study of the law of athletes' life and the habits of athletes' beings (Du & Yuan, 2021).…”

Section: Introductionmentioning

confidence: 99%

Dyadic analysis for multi-block data in sport surveys analytics

2022

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Analyzing sports data has become a challenging issue as it involves not standard data structures coming from several sources and with different formats, being often high dimensional and complex. This paper deals with a dyadic structure (athletes/coaches), characterized by a large number of manifest and latent variables. Data were collected in a survey administered within a joint project of University of Naples Federico II and Italian Swimmer Federation. The survey gathers information about psychosocial aspects influencing swimmers’ performance. The paper introduces a data processing method for dyadic data by presenting an alternative approach with respect to the current used models and provides an analysis of psychological factors affecting the actor/partner interdependence by means of a quantile regression. The obtained results could be an asset to design strategies and actions both for coaches and swimmers establishing an original use of statistical methods for analysing athletes psychological behaviour.

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A survey of competitive sports data visualization and visual analysis

Cited by 52 publications

References 47 publications

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Digitally Optimizing the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

Dyadic analysis for multi-block data in sport surveys analytics

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