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With the rising popularity of Wordle, people have eagerly taken to Twitter toreport their results daily by the tens of thousands. In this paper, we developa comprehensive model which uses this data to predict Wordle player performanceand reporting given any word and date. To do so, we first decompose words intoquantifiable traits associated with relevant difficulty characteristics. Mostnotably, we formulate a novel Wordle-specific entropy measure which quantifiesthe average amount of information revealed by typical players after initialguesses. We also develop a method to represent the distribution of playerattempts, and hence the observed difficulty of a word, using just two valuescorresponding to the cumulative mass function of the Beta distribution.Consequently, we are able to use a preliminary Lasso regression to isolate themost relevant predictors of word difficulty, which we then use in a Bayesianmodel.  For a given word and date, our Bayesian model predicts the distribution ofthe number of guesses by players (i.e. the reported player performance), thenumber of player reports, and the number of players reporting playing inhard-mode. To accomplish these three tasks, it is made up of three submodelswhich are conditionally independent given the data, making it efficient tosample from its posterior using Markov Chain Monte Carlo (MCMC). Our model isable to predict outcomes for new data and retrodict for old data, and weempirically demonstrate the success of our model through retrodictions onunseen data. Most notably, our model does not just provide such simple pointestimates and prediction intervals, but full posterior distributions.

How many Wordle words will Wordle guessers guess if Wordle's Wednesday Wordle word is "Eerie"?