Visualization of players' game performance based on random forest and particle swarm models Article Swipe
Yunliang Tan
,
Junfeng Ni
,
Yitian Zhang
,
Jinyi Gao
,
T. Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.54097/kb9ecd48
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.54097/kb9ecd48
In this study, a model combining Particle Swarm Optimization (PSO) and Random Forest Algorithm (RF) is proposed for processing and selecting features in a sports competition dataset and optimizing the model parameters. First, the scale differences between different features were eliminated by data normalization, followed by optimizing the parameters of the random forest model using the PSO algorithm, which significantly improved the prediction accuracy of the model. The study also introduced the performance level scoring model, which assigns weights according to the importance of selected features, and the momentum scoring model, which explores the relationship between dynamic changes in the game and the outcome of the game. The validity of the models was verified through real-world data analysis, showing that this approach can accurately predict the outcome of a match. In addition, Wilcoxon rank sum test and Spearman correlation coefficient were applied to statistically validate the model, and the results showed that the selected features were significantly correlated with the match results. The application of these methods not only improves the explanatory ability of the model, but also provides a new research direction and methodological guidance for the field of sports data analysis. In summary, the integrated modeling framework developed in this study demonstrates high efficiency and accuracy in handling large-scale sports data, demonstrates its potential for application in competitive sports data analysis, and provides a solid foundation and new perspectives for subsequent research in related fields.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54097/kb9ecd48
- https://drpress.org/ojs/index.php/HSET/article/download/25466/24947
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4403986713Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.54097/kb9ecd48Digital Object Identifier
- Title
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Visualization of players' game performance based on random forest and particle swarm modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-28Full publication date if available
- Authors
-
Yunliang Tan, Junfeng Ni, Yitian Zhang, Jinyi Gao, T. YangList of authors in order
- Landing page
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https://doi.org/10.54097/kb9ecd48Publisher landing page
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https://drpress.org/ojs/index.php/HSET/article/download/25466/24947Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://drpress.org/ojs/index.php/HSET/article/download/25466/24947Direct OA link when available
- Concepts
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Visualization, Random forest, Swarm behaviour, Computer science, Simulation, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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