Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

Zhao, Kai; Chun-jie, DU; Guang-xin, Tan

doi:10.3390/e25050765

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Zhao et al [ 26 ] (2023) applied Graph Neural Networks (GNN) to predict basketball game outcomes by transforming structured data into unstructured graphs to reveal the complex passing interactions among players. By integrating machine learning algorithms with Graph Convolutional Network (GCN) models, they achieved a prediction accuracy rate of 71.54%, offering a novel perspective on team interactions.…”

Section: Related Studiesmentioning

confidence: 99%

Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology

Ouyang,

Li,

Zhou

et al. 2024

PLoS ONE

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This study investigated the application of artificial intelligence in real-time prediction of professional basketball games, identifying the variations within performance indicators that are critical in determining the outcomes of the games. Utilizing games data from the NBA seasons 2021 to 2023 as the sample, the study constructed a real-time predictive model for NBA game outcomes, integrating the machine learning XGBoost and SHAP algorithms. The model simulated the prediction of game outcomes at different time of games and effectively quantified the analysis of key factors that influenced game outcomes. The study’s results demonstrated that the XGBoost algorithm was highly effective in predicting NBA game outcomes. Key performance indicators such as field goal percentage, defensive rebounds, and turnovers were consistently related to the outcomes at all times during the game. In the first half of the game, assists were a key indicator affecting the outcome of the game. In the second half of the games, offensive rebounds and three-point shooting percentage were key indicators affecting the outcome of the games. The performance of the real-time prediction model for NBA game outcomes, which integrates machine learning XGBoost and SHAP algorithms, is found to be excellent and highly interpretable. By quantifying the factors that determine victory, it is able to provide significant decision support for coaches in arranging tactical strategies on the court. Moreover, the study provides reliable data references for sports bettors, athletes, club managers, and sponsors.

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Section: Related Studiesmentioning

confidence: 99%

Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology

Ouyang,

Li,

Zhou

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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“…Physical fitness refers to the basic athletic ability of an athlete's body and is an important part of an athlete's competitive ability (Zhao et al, 2023). The physical development level of basketball players is determined by the development of body shape, physical function, and athletic quality.…”

Section: Competitive Ability Of Basketball Playersmentioning

confidence: 99%

The Rating of Basketball Players' Competitive Performance Based on RBF-EVA Method

Jia,

Chen

2023

International Journal of Information Technology and Web Engineering

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Basketball, as an offensive and defensive game centered around high altitude, has become an international mass competitive sport. Traditional methods cannot comprehensively evaluate the future potential of players, nor can they simply add up individual competitive abilities to judge the overall competitive performance of a team. To address these issues, this article proposes a video-based RBF neural network competitive scoring method, which analyzes players' past sports behavior, captures every subtle difference in their abilities, and achieves objective evaluation of players' competitive performance. Through comparative experiments, the accuracy of the test results is improved by about 5% compared to conventional RBF methods. This indicates that the improved RBF neural network designed in this article has significantly better prediction performance than traditional convolutional neural networks. This study provides a new method for evaluating the competitive performance of basketball players and has important guiding significance for basketball training and skill enhancement.

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“…Zhao and his team studied the routine teaching of footwork among students in the school's basketball elective course (Zhao et al, 2023). They found that in basketball teaching, whether it was the beginner or the proficiency period, the advance and defense movement exercises should be closely combined so the overall technique could be improved fast (Guo et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method

Feng,

Sun

2023

International Journal of Information Technology and Web Engineering

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The combination of traditional basketball footwork mobile teaching and AI will become a hot spot in basketball footwork research. This article used a deep learning (DL) unsupervised transfer method: Convolutional neural networks are used to extract source and target domain samples for transfer learning. Feature extraction is performed on the data, and the impending action of a basketball player is predicted. Meanwhile, the unsupervised human action transfer method is studied to provide new ideas for basketball footwork action series data modeling. Finally, the theoretical framework of DL unsupervised transfer learning is reviewed. Its principle is explored and applied in the teaching of basketball footwork. The results show that convolutional neural networks can predict players' movement trajectories, unsupervised training using network data dramatically increases the variety of actions during training. The classification accuracy of the transfer learning method is high, and it can be used for the different basketball footwork in the corresponding stage of the court.

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Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

Cited by 8 publications

References 31 publications

Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology

Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology

The Rating of Basketball Players' Competitive Performance Based on RBF-EVA Method

Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method

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