Deep Learning Algorithms for Sports Data Analysis and Prediction in Sports Competitions Article Swipe
Sports data analysis and prediction are essential for gaining a competitive advantage in today’s sports. Artificial Neural Networks (ANNs) have shown promising outcomes in several disciplines, including sports analytics. Sports data is dynamic and complex, making it difficult for standard ANNs to identify minute patterns in it. We introduce a new Puzzle-Optimized Artificial Neural Network (PO-ANN) in this work, which is intended for sports data processing and prediction. The PO-ANN is optimized using a puzzle-inspired method to enhance the network’s ability to identify and comprehend complex patterns in the data. The technique constantly modifies the weights and network topology, enabling the model to better react to the shifting dynamics of sports competitions. The Indian Premier League provided the dataset, which consists of 950 matches and 20 variables (IPL). We implemented our proposed PO-ANN and forecast accuracy in sports data analysis and prediction using Python. We performed a comparison analysis between our suggested PO-ANN approach and other existing methods, using numerous metrics, including MSE, MAE, and MAPE. The suggested POANN technique produced better outcomes than the previous approaches.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2478/amns-2024-0013
- OA Status
- gold
- Cited By
- 2
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- 18
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4395702841Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2478/amns-2024-0013Digital Object Identifier
- Title
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Deep Learning Algorithms for Sports Data Analysis and Prediction in Sports CompetitionsWork title
- Type
-
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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2024-01-01Full publication date if available
- Authors
-
Shuguang WeiList of authors in order
- Landing page
-
https://doi.org/10.2478/amns-2024-0013Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2478/amns-2024-0013Direct OA link when available
- Concepts
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Artificial neural network, Computer science, Python (programming language), Machine learning, Artificial intelligence, League, Data analysis, Data mining, Algorithm, Physics, Astronomy, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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18Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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