Diagnosis Model of Volleyball Skills and Tactics Based on Artificial Neural Network

Jiang, Wei; Zhao, Kai; Jin, Xinlong

doi:10.1155/2021/7908897

Cited by 4 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…Authors explore how neural network-based models can be effective in predicting match results based on historical data and gameplay statistics. These studies demonstrate the potential of applying modern technologies in sports data analysis [34,35,36].…”

Section: Application Of Artificial Neural Network In Analyzing Volley...mentioning

confidence: 55%

Statistical match reports of the 2022 volleyball World Championship in the context of network analysis with Gephi

Iermakov,

Yermakova,

Wnorowski

2023

pcult

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Background and Study Aim. Modern volleyball teams participating in tournaments provide extensive statistical data about their actions. These data are available for analysis and represent a valuable source for assessing the potential and future success of both individual athletes and teams as a whole. In this context, tools such as Gephi become particularly valuable for visualizing and analyzing events at high-level tournaments. Our study aims to create and analyze network models of interaction among men's volleyball teams during the qualifying matches of the 2022 World Championship using the Gephi software. Materials and Methods. For the research, data were obtained from a volleyball statistics website renowned for its extensive database. The study centered on Group C, comprising the teams: Poland, USA, Mexico, and Bulgaria. Data from six matches were extracted into Microsoft Excel tables and then converted into CSV format. The data from these tables were processed using the PyCharm programming environment and Python code. Visualization and analysis of the data were conducted using Gephi. Results. The results of the data processing highlight the professional expertise of the teams. The average values for all primary metrics underscore the teams' proficiency in executing fundamental actions at an elevated level. Eigenvector centrality helps determine the significance of nodes in the graph. The graph's density is 0.601, suggesting a relatively dense network of connections within the team. This indicates that volleyball players frequently engage with one another and share information on the court. Such extensive interaction can lead to enhanced coordination and efficiency in team actions. The parameter ε has a value of 1.0E-4, denoting high computational precision. The average interaction degree among volleyball players stands at 46.244. This measure denotes the interaction intensity among team members, hinting at the overall court activity. The modularity measure is 0.483, which signals the structural organization of the graph rooted in modularity. The graph comprises 5 modular communities, hinting at shared characteristics and cohesion among players within these groups. The HITS (Hyperlink-Induced Topic Search) metric assesses the node significance in the graph in terms of hubs and authorities. High HITS values pinpoint pivotal players acting as hubs, signifying their numerous connections with fellow teammates. Such players are crucial for facilitating information flow and coordination within the squad. Conclusions. Utilizing statistical match reports in volleyball, combined with the Gephi software, offers a deeper insight into the dynamics of player interactions. This aids in pinpointing key players, refining team strategies, and enhancing court coordination. Coaches are advised to leverage these tools for an in-depth evaluation of gameplay instances and to make informed decisions. Network analysis methodologies might soon be indispensable tools in the contemporary coaching toolkit.

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Section: Application Of Artificial Neural Network In Analyzing Volley...mentioning

confidence: 55%

Statistical match reports of the 2022 volleyball World Championship in the context of network analysis with Gephi

Iermakov,

Yermakova,

Wnorowski

2023

pcult

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“…Meanwhile, low skills will make them difficult to beat opponents [14]. This was also reported by previous studies that the main factor that must be fostered and developed in student athletes was volleyball skills [15]. Given the importance of volleyball skills for student athletes [16], an appropriate method was needed, namely through a blended-based massed and distributed training method.…”

Section: Inrodactoinmentioning

confidence: 96%

Blended-based massed and distributed training: a strategy for teaching volleyball skills in student-athletes

Bachtiar,

Rihatno,

Samsudin

et al. 2024

HSR

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Purpose: The lack of evidence on the effectiveness of blended-based massed and distributed training in volleyball resulting a gap, which became a focus in this study. This study aims to identify the effects of blended-based massed and distributed training on changes in student-athlete volleyball skills through mixed research methods. Material and methods. This study applied mixed research methods and involved 60 female athletes who were active in volleyball from the Muhammadiyah University of Sukabumi (Indonesia). Participants were randomly allocated to the massed exercise, distributed exercise and control group. Quantitative instruments involved volleyball skill tests and qualitative instruments involved in-depth interviews. Quantitative data analysis included normality test, mean, standard deviation and ANOVA to assess differences of volleyball skills between massed, distributed and control groups. The size of effect in these three groups on volleyball skills was analyzed through the Cohen d. While qualitative data analysis was conducted through thematic analysis. Results. This quantitative research found several results. First, there was no difference in volleyball skill scores between blended based massed and distributed training group and controls group before the experiment (p>0.05). Second, there was a difference in values between the blended-based massed and distributed group as well as control group after the experiment (p<0.05). Third, the effect size test showed that the blended-based massed and distributed groups have a moderate effect while the control group has a small effect on volleyball skills. While, in qualitative research agreed that blended-based massed and distributed training had advantages, weaknesss and impacts on volleyball skills. Conclusions. This study concluded that this mixed research method was proven that blended based massed and distributed training have a positive effect for student athletes to learn every movement in volleyball skills.

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“…Each team attempts to ground a ball on the opposite team's court and to score points under the defined rules. So, detecting and analyzing the player activities [163][164][165], detecting play patterns and classifying tactical behaviors [166][167][168][169], predicting league standings [170], detecting and classifying spiking skills [171,172], estimating the pose of the player [173], tracking the player [174], tracking the ball [175], etc., are the major aspects of volleyball analysis. Predicting the ball trajectory [59] in a volleyball game by observing the motion of the setter player has been conducted.…”

Section: Volleyballmentioning

confidence: 99%

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

2022

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Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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Diagnosis Model of Volleyball Skills and Tactics Based on Artificial Neural Network

Cited by 4 publications

References 24 publications

Statistical match reports of the 2022 volleyball World Championship in the context of network analysis with Gephi

Statistical match reports of the 2022 volleyball World Championship in the context of network analysis with Gephi

Blended-based massed and distributed training: a strategy for teaching volleyball skills in student-athletes

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

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