Unveiling the Complexity of Wordle: An Analysis of Player Behavior and Word Difficulty Based on Predictive Models and the RSR Comprehensive Evaluation System

This paper presents a novel approach to analyzing and predicting player outcomes in Wordle, a widely popular word puzzle game featured daily by the New York Times. We developed a predictive model that incorporates word frequency, temporal dynamics, and player behavior. The model uses a combination of statistical methods and machine learning techniques to forecast result distributions and categorize word difficulty. We implemented a Results Distribution Prediction Model to assess how word frequency and game characteristics impact player performance over time. Furthermore, a Difficulty Area Division Model was developed using Spearman correlation analysis and the Relative Strength Ratio (RSR) evaluation method to classify words based on their difficulty. This dual-model framework enhances the accuracy of game outcome predictions and provides insights into how specific word attributes, such as repetition of letters and grammatical properties, affect player success rates. Our findings indicate that these word attributes significantly impact player performance, offering a quantitative measure of word difficulty beyond simple frequency analysis. The model's predictions were validated through machine learning, specifically logistic regression, ensuring robustness and adaptability to variations in player behavior. This research provides valuable insights for game developers to refine game design and enhance player engagement while offering players a deeper understanding of the game's challenges. Future work may extend this methodology to other linguistic and cognitive variables, further enhancing the predictive power of the model.