Documentation of the information-theoretic Wordle solving approach used as a benchmark for Wordle Wonk — the production GWN Wordle helper. Part of the Grande Web Network family.
Note: This is a docs-only repo. No source is published here — the live solver runs at wordlewonk.com. This README explains the algorithm for educational reference.
Pick the guess whose expected distribution of color-patterns (GREEN / YELLOW / GRAY per position) maximizes the Shannon entropy of the bucket distribution over still-possible answers. In plain words: choose the guess that, on average, eliminates the most possibilities no matter what the colors come back as.
for each candidate guess G in allowed_guesses:
buckets = {}
for each remaining possible answer A:
pattern = color_feedback(G, A) # 5-tuple of GREEN|YELLOW|GRAY
buckets[pattern] += 1
total = len(possible_answers)
entropy(G) = -sum( (n/total) * log2(n/total) for n in buckets.values() )
best_guess = argmax(entropy)
Run against the original Wordle answer list (2,315 words) and a full ~12K-word guess dictionary, pure-entropy strategy yields:
| metric | value |
|---|---|
| average guesses | ~3.61 |
| worst case | 5 guesses |
| max-entropy first guess | TARES or SOARE (tie-break) |
Wordle Wonk uses positional letter-frequency heuristics instead — faster per guess, ~93% as optimal as pure entropy, and feels more natural to humans following along.
- Wordle Wonk — production GWN Wordle helper
- A2Z Words — daily Wordle companion blog
- A2Z Word Finder — dictionary API
- 3Blue1Brown's Wordle video — canonical explainer
CC0-1.0 — docs are free to reuse.
Educational docs by GWN.