  We adopt the distribution and expectation of guessing times in game Wordle as
metrics to predict the difficulty of words and explore their influence factors.
In order to predictthe difficulty distribution, we use Monte Carlo to simulate
the guessing process of players and then narrow the gap between raw and actual
distribution of guessing times for each word with Markov which generates the
associativity of words. Afterwards, we take advantage of lasso regression to
predict the deviation of guessing times expectation and quadratic programming
to obtain the correction of the original distribution.To predict the difficulty
levels, we first use hierarchical clustering to classify the difficulty levels
based on the expectation of guessing times. Afterwards we downscale the
variables of lexical attributes based on factor analysis. Significant factors
include the number of neighboring words, letter similarity, sub-string
similarity, and word frequency. Finally, we build the relationship between
lexical attributes and difficulty levels through ordered logistic regression.
