  With the rising popularity of Wordle, people have eagerly taken to Twitter to
report their results daily by the tens of thousands. In this paper, we develop
a comprehensive model which uses this data to predict Wordle player performance
and reporting given any word and date. To do so, we first decompose words into
quantifiable traits associated with relevant difficulty characteristics. Most
notably, we formulate a novel Wordle-specific entropy measure which quantifies
the average amount of information revealed by typical players after initial
guesses. We also develop a method to represent the distribution of player
attempts, and hence the observed difficulty of a word, using just two values
corresponding to the cumulative mass function of the Beta distribution.
Consequently, we are able to use a preliminary Lasso regression to isolate the
most relevant predictors of word difficulty, which we then use in a Bayesian
model.
  For a given word and date, our Bayesian model predicts the distribution of
the number of guesses by players (i.e. the reported player performance), the
number of player reports, and the number of players reporting playing in
hard-mode. To accomplish these three tasks, it is made up of three submodels
which are conditionally independent given the data, making it efficient to
sample from its posterior using Markov Chain Monte Carlo (MCMC). Our model is
able to predict outcomes for new data and retrodict for old data, and we
empirically demonstrate the success of our model through retrodictions on
unseen data. Most notably, our model does not just provide such simple point
estimates and prediction intervals, but full posterior distributions.
