Image Courtesy: Binghamton University, State University of New York
A team of researchers has developed a mathematical strategy that can solve the popular word game Wordle with a 99% success rate, significantly outperforming conventional approaches that rely on guessing the most common letters.
The research was conducted by scientists at Binghamton University, State University of New York, who applied principles of information theory to determine the most effective guesses. Instead of choosing words that are most likely to be correct, their method prioritizes guesses that reveal the greatest amount of information and eliminate the largest number of possible answers.
After each guess, colored tiles indicate whether letters are correct, misplaced, or absent from the solution. These clues help narrow down the remaining possibilities until the puzzle is solved or all attempts are used.
Assistant Professor Congyu “Peter” Wu and his team based their approach on Shannon entropy, a mathematical concept that measures uncertainty. Rather than chasing the most probable answer, the algorithm selects words that are expected to provide the most useful information for future guesses. Doctoral student Donald Stephens explained that the best guess is not necessarily the most likely solution, but the one that reduces uncertainty the fastest.
To use the strategy, players enter Wordle’s color-coded feedback into a separate program after each guess. The software then recommends the next word that is expected to maximize information gain and quickly narrow the list of possible answers.
In computer simulations, the information theory-based method successfully solved 99% of Wordle puzzles, compared with roughly 90% for a traditional strategy focused on frequently used letters such as A, E, and R.
The project began as a classroom assignment challenging students to apply information theory to a practical problem before evolving into a published research paper. Co-author Talal Aladaileh said the work demonstrates how classroom concepts can lead to meaningful research, while Wu noted that the project highlights how a scientific principle like Shannon entropy can be transformed into a practical tool for improving everyday problem-solving.
