Information theory yields Wordle-solving strategy with 99% success rate
Researchers at Binghamton University developed a mathematical approach to Wordle that prioritizes guesses revealing the most information rather than likely answers. Using Shannon entropy, the strategy rapidly narrows possible solutions and achieved a 99% success rate in simulations. The method demonstrates how information theory can optimize decision-making in games.
The strategy relies on Shannon entropy, a concept from information theory that quantifies uncertainty. In Wordle, each guess's value is measured by how effectively it splits the remaining candidate words into smaller, more manageable groups. A guess with uncommon letters may outperform a seemingly obvious answer because it yields more feedback across multiple positions.
Assistant Professor Congyu "Peter" Wu and doctoral student Donald Stephens led the work at Binghamton University's Watson College. Their simulations achieved a 99% solve rate by treating each guess as a data-gathering step rather than an attempt at the final answer. The approach mirrors how information theory optimizes communication systems, where efficiency depends on reducing uncertainty with each transmitted signal.
This research could reshape how casual players approach Wordle and similar deduction games, potentially shifting enjoyment from lucky guesses toward systematic reasoning. Educators may use the example to make information theory accessible to students, demonstrating abstract mathematics through a familiar puzzle. The underlying principle—prioritizing informative actions over seemingly obvious choices—could extend to fields like search algorithms, diagnostic testing, or data collection, where each step's value depends on how much uncertainty it removes. However, the strategy's practical appeal may remain limited for players who value spontaneity over optimization.