A study of momentum prediction based on Markov chains

Xinyue Wang; Xiaotong Zhu; Linhan Wang

doi:10.1117/12.3035279

11 July 2024 A study of momentum prediction based on Markov chains

Xinyue Wang, Xiaotong Zhu, Linhan Wang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101E (2024) https://doi.org/10.1117/12.3035279
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

This study pioneers a comprehensive framework for momentum prediction, incorporating a multifaceted methodology that integrates Markov chains, Analytic Hierarchy Process (AHP)-TOPSIS, and Principal Component Analysis (PCA). This novel amalgamation enhances the robustness and depth of the predictive analysis. The use of AHP-TOPSIS augments decision-making processes by prioritizing critical factors influencing momentum, while PCA contributes to data dimensionality reduction, ensuring a focused and efficient analysis. This holistic approach not only advances the methodological landscape of sports momentum prediction but also sets a precedent for future interdisciplinary research in predictive analytics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyue Wang, Xiaotong Zhu, and Linhan Wang "A study of momentum prediction based on Markov chains", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101E (11 July 2024); https://doi.org/10.1117/12.3035279

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available