Wordle solving strategies
Explore mathematically proven approaches backed by information theory and combinatorial search. Each strategy shows you how the algorithm narrows the solution space—step by step.
Entropy Maximization
Information Theory Approach
Uses information theory to maximize information gain with each guess. Calculates entropy (expected information) for all possible words and selects the one that best narrows down the solution space. Based on research by Grant Sanderson (3Blue1Brown).
Avg guesses
3.45
Max guesses
5
Win in three
58%
Success rate
100%
Max-Splits Heuristic
Pattern Maximization Approach
Selects words that create the maximum number of distinct color patterns across all remaining possible solutions. This greedy heuristic effectively partitions the solution space, and research shows it performs optimally for second-to-last moves.
Avg guesses
3.47
Max guesses
5
Win in three
60%
Success rate
99%
Optimal Decision Tree
Mathematically Proven Optimal (SALET)
Uses pre-computed decision trees to make the mathematically optimal choice at every step. This is the gold standard with the lowest possible average guess count (3.4212) while guaranteeing success within 5 guesses. Requires a large pre-computed database.
Avg guesses
3.42
Max guesses
5
Win in three
57%
Success rate
100%
The Entropy Maximization algorithm leans on information theory popularised by Grant Sanderson (3Blue1Brown). Max-Splits Heuristic draws inspiration from work by Laurent Lessard and collaborators. The Optimal Decision Tree solver mirrors the mathematically proven approach from MIT researchers and Alex Selby.
Each page lets you plug in any Wordle answer and replay how the strategy narrows the search space with annotated reasoning at every turn.