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We adopt the distribution and expectation of guessing times in game Wordle asmetrics to predict the difficulty of words and explore their influence factors.In order to predictthe difficulty distribution, we use Monte Carlo to simulatethe guessing process of players and then narrow the gap between raw and actualdistribution of guessing times for each word with Markov which generates theassociativity of words. Afterwards, we take advantage of lasso regression topredict the deviation of guessing times expectation and quadratic programmingto obtain the correction of the original distribution.To predict the difficultylevels, we first use hierarchical clustering to classify the difficulty levelsbased on the expectation of guessing times. Afterwards we downscale thevariables of lexical attributes based on factor analysis. Significant factorsinclude the number of neighboring words, letter similarity, sub-stringsimilarity, and word frequency. Finally, we build the relationship betweenlexical attributes and difficulty levels through ordered logistic regression.

Explore the difficulty of words and its influential attributes based on the Wordle game