La estimación de los resultados establecidos en los juegos de la liga Vestel Venus Sultans 2016/2017 por red neuronal artificial

Aka, Hasan

doi:10.21134/eurjhm.2021.47.4

Cited by 2 publications

(2 citation statements)

References 18 publications

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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: 56%

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: 56%

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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“…The purpose of the use of digital technologies in volleyball is mainly to analyze the matches by an expert through analysis programs (Aka, Akarçeşme, Aktuğ, Özden, 2021a; Aka, Aktuğ, Kılıç, 2021b). According to the results of a study evaluating the efficiency of analysis programs, trainers find these programs useful (Aka et al, 2021a;Aka et al, 2021b; Akarçeşme, Aka, Özden, Aktuğ, 2020). These analysis programs, developed special to volleyball, enable trainers to reach a vast amount of data about athletes.…”

Section: Introductionmentioning

confidence: 99%

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

Altundağ,

Aka,

Soylu

et al. 2024

Gazi Beden Eğitimi Ve Spor Bilimleri Dergisi

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This study investigates the efficacy of Artificial Neural Networks (ANN) in predicting volleyball league standings, focusing on the Turkish Volleyball Federation's Sultanlar and Efeler leagues over five seasons (2018-19 to 2022-23). Given the complexity and volume of performance data in volleyball, traditional analysis methods often face challenges such as data overload and high operational costs. ANN models, known for their ability to learn from and generalize data, present a promising solution to these challenges. By analyzing 23 input variables related to match performance, including points scored, services, attacks, and blocks, this study aims to identify the most influential factors on final league standings and provide a more objective, rapid, and economical analysis method. The results indicate significant potential for ANN in sports analytics, demonstrating high accuracy rates in predictions, especially for the Sultanlar League. However, the study also acknowledges limitations such as data quality and model complexity, suggesting areas for future research to enhance predictive accuracy and applicability of ANN in volleyball and other sports analytics.

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La estimación de los resultados establecidos en los juegos de la liga Vestel Venus Sultans 2016/2017 por red neuronal artificial

Cited by 2 publications

References 18 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

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

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