Analysis and Modeling of Football Team’s Collaboration Mode and Performance Evaluation Using Network Science and BP Neural Network

Zhang, Jian; Zhao, Xi; Wu, Yushuai; Cao, Peng; Wang, Xuhao; Shi, Feiting; Niu, Yu

doi:10.1155/2020/7397169

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…The key to establishing a three-layer BP network model is the determination of the input layer's neuron number, the hidden layer's number, the hidden layer's neuron number, the selection of transferring function as well as the training function. The high nonlinear processing ability of BP neural network makes it widely applied in game prediction, and techniques and tactics diagnosis for ball matches [13,24,25]. Yang & Zhang used BP neural network and multiple regression to analyze the technical and tactical ability of elite male table tennis players, and the results showed that the overall fitting accuracy of the BP neural network was higher than that of the multiple regression model [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Technical and tactical diagnosis model of table tennis matches based on BP neural network

Huang

Zeng

et al. 2021

BMC Sports Sci Med Rehabil

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Background The technical and tactical diagnosis of table tennis is extremely important in the preparation for competition which is complicated by an apparent nonlinear relationship between athletes’ performance and their sports quality. The neural network model provides a high nonlinear dynamic processing ability and fitting accuracy that may assist in the diagnosis of table tennis players’ technical and tactical skill. The main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to analyze the influence of athletes’ techniques and tactics on the competition results. Methods A three-layer Back Propagation (BP) neural network model for table tennis match diagnosis were established. A Double Three-Phase evaluation method produced 30 indices that were closely related to winning table tennis matches. A data sample of 100 table tennis matches was used to establish the diagnostic model (n = 70) and evaluate the predictive ability of the model (n = 30). Results The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high-level of prediction accuracy (up to 99.997%) and highly efficient in fitting (R2 = 0.99). Specifically, the technical and tactical diagnosis results indicated that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability. Conclusion The technical and tactical diagnosis model of table tennis matches based on BP neural network was highly accurate and efficiently fit. It appears that the use of the model can calculate athletes’ technical and tactical indices and their influence on the probability of winning table tennis matches. This, in turn, can provide a valuable tool for formulating player’s targeted training plans.

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Section: Literature Reviewmentioning

confidence: 99%

Technical and tactical diagnosis model of table tennis matches based on BP neural network

Huang

Zeng

et al. 2021

BMC Sports Sci Med Rehabil

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“…e network is a vital concept to understand modern society structure and components of the society. e concept of the network has been applied not only to people but also to social network [1][2][3][4][5][6][7], biological network [8][9][10][11][12], metabolic regulation network [13], online social network [14,15], sports social network [16,17], structure of neural network [18][19][20], etc. As a result, the data for storing network information has become complex and diverse.…”

Section: Introductionmentioning

confidence: 99%

A Simple Visualization Method for Three-Dimensional (3D) Network

Kim

Lee

Park

et al. 2021

Discrete Dynamics in Nature and Society

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The network is a concept that can be seen a lot in many areas of research. It is used to describe and interpret datasets in various fields such as social network, biological network, and metabolic regulation network. As a result, network diagrams appeared in various forms, and methods for visualizing the network information are being developed. In this article, we present a simple method with a weight of information data to visualize the network diagram for the three-dimensional (3D) network. The generic method of network visualization is a circular representation with many intersections. When dealing with a lot of data, the three-dimensional network graphics, which can be rotated, are easier to analyze than the two-dimensional (2D) network. The proposed algorithm focuses on visualizing three factors: the position and size of the nodes and the thickness of the edge between linked nodes. In the proposed method, an objective function is defined, which consists of two parts to locate the nodes: (i) a constraint for given distance, which is the weight of the relationship among all the data, and (ii) the mutual repulsive force among the given nodes. We apply the gradient descent method to minimize the objective function. The size of the nodes and the thickness of the edges are defined by using the weight of each node and the weight between other nodes associated with it, respectively. To demonstrate the performance of the proposed algorithm, the relationships of the characters in the two novels are visualized using 3D network diagram.

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Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks

Bai

2022

Complexity

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With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.

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Analysis and Modeling of Football Team’s Collaboration Mode and Performance Evaluation Using Network Science and BP Neural Network

Cited by 3 publications

References 47 publications

Technical and tactical diagnosis model of table tennis matches based on BP neural network

Technical and tactical diagnosis model of table tennis matches based on BP neural network

A Simple Visualization Method for Three-Dimensional (3D) Network

Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks

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