Title: Neural network data analysis and mathematical modelling for Wordle games

Authors: Chen Weijun; Jin Jiangtao; Lei Yaxin; Tao Jian

Addresses: Department of Basic Education, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, China ' Information Technology Institute, Wenzhou Business College, Wenzhou, Zhejiang, China ' School of Engineering and Technology, Jiyang College of Zhejiang A&F University, Zhuji, Zhejiang, China ' School of Intelligent Manufacturing, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, China

Abstract: Wordle is a popular daily puzzle in the New York Times. The 'Predicting Wordle Results' problem in the 2023 Mathematical Contest in Modelling (MCM) focused on developing a model to estimate the reported headcount in the difficult mode. The model considered three attributes: word frequency, letter repetition and letter frequency. The analysis showed that these attributes influenced the reported headcount. Correlation analysis revealed a strong relationship between the number of participants and word frequency and letter repetition. A neural network time series model was developed, using word frequency and letter frequency as inputs to predict the reported results. The model achieved a high accuracy with an R²-value of 0.95. The study found that the number of participants had a linear relationship with the number of participants in the difficult mode. Most word frequencies in the questions were below 0.0002.

Keywords: neural network; time series; information theory; entropy topsis; distance discriminant method; data analysis.

DOI: 10.1504/IJGUC.2026.152701

International Journal of Grid and Utility Computing, 2026 Vol.17 No.2, pp.159 - 173

Received: 08 Dec 2023
Accepted: 08 Jan 2024
Published online: 07 Apr 2026 *

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