Wordle data analysis and prediction based on SIR infectious disease model and grey wolf optimization algorithm

Yongjun Pu; Long Zhang; Hengdi Hu; Caizhi Lin; Bingyi Zhou

doi:10.1117/12.2686004

28 July 2023 Wordle data analysis and prediction based on SIR infectious disease model and grey wolf optimization algorithm

Yongjun Pu, Long Zhang, Hengdi Hu, Caizhi Lin, Bingyi Zhou

Author Affiliations +

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563Q (2023) https://doi.org/10.1117/12.2686004
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

Wordle is a free web crossword that has exploded into global popularity in 2022, and it has a lot to offer in terms of gameplay. In order to explore the reasons for the popularity of Wordle games, the bionic SIR Epidemic model is used to predict the number of Wordle players and analyze the reasons for the change of the number of players in combination with the mode of game transmission. Information entropy is used to process Wordle word data in the past, and the relationship between word attributes and players' completion rate is analyzed. A more accurate model is then built to predict the specific distribution of the number of times players in Wordle correctly guess the word. The input data set was constructed from the word attributes that mainly affect Wordle players, and the multi-output prediction model was built based on the bionic encircle tracking method of GWO. The mathematical model above is used to systematically predict the future trend of Wordle.

Citation Download Citation

Yongjun Pu, Long Zhang, Hengdi Hu, Caizhi Lin, and Bingyi Zhou "Wordle data analysis and prediction based on SIR infectious disease model and grey wolf optimization algorithm", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563Q (28 July 2023); https://doi.org/10.1117/12.2686004

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