P&A: Make Wordle Game Better

Abstract

This study delves into enhancing the gaming experience by strategically predicting and analyzing Wordle outcomes. Rooted in prediction and analysis principles, our P&A model employs a systematic approach. Beginning with an exploration of statistical patterns in game results, we construct a predictive model for March 1 using methods such as trend analysis, median, variance, paired sample t-tests, and chi-square independence tests. Short-term outcome predictions incorporate time series ARIMA and support vector machine models for improved accuracy. The subsequent phase develops a model to predict future Wordle solutions, addressing uncertainties through practical examples and neural networks. Difficulty classification of Wordle solution words is tackled by associating attributes with each category, employing K-MEANS clustering, and optimizing it for better performance. Modified K-MEANS clustering assesses indicators' significance, and machine learning with XGBoost assigns importance scores. In conclusion, our predictive and analytical exploration of Wordle questions 1, 2, and 3 culminates in a dataset listing and description. The integration of the P&A Model, K-MEANS clustering, ARIMA, and various methodologies represents a significant advancement in unraveling the dynamics of Wordle.