Analysis Of Wordle Games Based on Multiple Logistic Regression and Clustering

Abstract

The development of social media has led to an increasing number of people participating in the online guessing game Wordle, which has become a popular phenomenon on Twitter. Understanding the difficulty of the Wordle game is important for both players and game designers. This article aims to gain an in-depth understanding of the game by analyzing its gameplay reports and word difficulty. Firstly, this article predicts the distribution of Wordle report results. Using a multivariate logistic regression model, the probability distribution prediction is converted into a classification problem, with word attributes and the average number of guesses per player as independent variables for prediction. At the same time, this article introduces random noise into the model prediction to address low probability issues. The model is evaluated using MAE and RMSE, and sensitivity analysis is conducted using k-fold cross-validation. In order to further evaluate the difficulty of words in the game, this article uses the entropy weight-TOPSIS method combined with the Bi-Kmeans classification model to classify word difficulty. The model results show that word difficulty can be divided into five categories. Finally, this article evaluates the performance of the model using Davies-Bouldin Index and the Silhouette Coefficient, and the results show that the model has strong reliability and persuasiveness.