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High dimensional sports statistics and machine learning in NBA
- 1 Chinese University of Hong Kong
* Author to whom correspondence should be addressed.
Abstract
In this study, we delve into the intersection of high-dimensional statistics and machine learning within the realm of sports analytics, with a particular focus on real-time prediction of NBA game outcomes. We harness cutting-edge data techniques and innovative AI models to boost our predictive capabilities and real-time performance. By combining advanced data processing with the latest in machine and deep learning, we're able to deliver more accurate and timely insights across a range of complex scenarios. Our approach integrates Bayesian statistical methods to quantify prediction uncertainty, ensuring robust and interpretable models. We utilize a combination of traditional machine learning models, such as Random Forest and Logistic Regression, alongside advanced deep learning architectures, including CNNs, RNNs, LSTMs, and Transformer networks. Our comprehensive preprocessing pipeline includes advanced statistical techniques for handling missing values and outliers, ensuring data consistency, and feature selection and dimensionality reduction methods like PCA and RFE. Implementing real-time data streaming technologies such as Apache Kafka and distributed databases like Apache Cassandra ensures high availability, scalability, and efficient handling of large volumes of data. This study highlights the significant potential of integrating high-dimensional statistics and deep learning in sports analytics, offering deeper insights, more accurate predictions, and real-time analysis capabilities, paving the way for future innovations and applications in the field.
Keywords
high-dimensional statistics, machine learning, sports analytics, deep learning, Bayesian method
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Cite this article
Zhu,Z. (2024). High dimensional sports statistics and machine learning in NBA. Advances in Engineering Innovation,11,78-94.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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