Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

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Survey_Accepted_Version.pdf (864.37 KB)

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https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1496

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https://doi.org/10.1002/widm.1496
 http://hdl.handle.net/11603/28017

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UMBC Information Systems Department
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https://orcid.org/0000-0003-2868-3766

Date

2023-03-21

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journal articles
postprints
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Citation of Original Publication

Ghosh, I., Ramasamy Ramamurthy, S., Chakma, A., & Roy, N. (2023). Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective. WIREs Data Mining and Knowledge Discovery, e1496. https://doi.org/10.1002/widm.1496.

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This is the peer reviewed version of the following article: Ghosh, I., Ramasamy Ramamurthy, S., Chakma, A., & Roy, N. (2023). Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective. WIREs Data Mining and Knowledge Discovery, e1496. https://doi.org/10.1002/widm.1496 , which has been published in final form at https://doi.org/10.1002/widm.1496. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Access to this item will begin on 03/21/2024

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Abstract

The rapid and impromptu interest in the coupling of machine learning algorithms with wearable and contactless sensors aimed at tackling real-world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state-of-the-art literature on machine learning algorithms, methodologies, and hypotheses adopted to solve the research problems & challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless & mobile-based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real-time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open-research opportunities and emerging technologies that could contribute to the domain of sports analytics.