arXiv:2403.19433v5 Announce Type: replace Abstract: Wordle result prediction involves both daily participation and the difficulty of the solution word. We model these quantities with ARIMA forecasts of report counts, XGBoost regressors using word frequency, letter entropy, and repetition, and a decision tree trained on K-means difficulty tiers. Experiments use 355 daily outcome distributions, with a 107-word chronological holdout and four expanding-window tests. XGBoost reduces holdout mean absolute error from 4.08 to 3.84 percentage points and improves over the training mean in every temporal window. Ridge and random forests remain competitive. XGBoost systematically underestimates failure rates on difficult words. The lexical decision tree achieves 54.2% tier accuracy, compared with 40.2% when predicted histograms are assigned to the same training centroids. Bootstrap analysis shows that cluster stability does not imply that tiers are predictable from lexical attributes. For report counts, comparisons at matched horizons reveal substantial overdispersion in full-history ARIMA intervals. These results characterize the predictive value and limitations of a compact lexical model of reported Wordle performance.