Hanged Man Game

Playing the Hanged Man Game: What It Is and How to Actually Build One

The Hanged Man Game, often just called hangman, is a simple letter-guessing game where a player tries to deduce a hidden word one character at a time. Most people encounter it as a childhood pastime, but in the AI and NLP community, it has become a surprisingly useful benchmark for testing language models, letter frequency analysis, and decision-making strategies under uncertainty. If you have ever tried to build an AI that can play this game well, you quickly discover that the problem is deceptively complex. I spent several months experimenting with different approaches to an automated Hanged Man Game solver, mostly because I wanted to see how far a straightforward probabilistic model could get without resorting to massive pre-trained language models. The short version is that it can get quite far, but only if you pay attention to the details most beginners ignore. The longer version involves a lot of dictionary pruning, wrong-letter tracking, and some messy edge cases that are not covered in any tutorial.

Setting Up the Hanged Man Game Environment

The first thing you need is a word list. The quality of your word list determines almost everything about how well your game or solver performs. Most people grab a generic English word list from the internet and start coding, which works fine for casual use but produces terrible results if you are actually trying to build something that plays at a high level. The lists you find online usually contain thousands of obscure words, proper nouns, hyphenated terms, and foreign words that a real player would never encounter. Your first practical step should be filtering that list down to something reasonable. I recommend starting with a filtered list of common English words between four and twelve letters. That range covers the vast majority of hangman games you will see in practice. Once you have your list, you need to load it into your program and strip out duplicates, uppercase variations, and any words containing characters outside the standard 26-letter alphabet. Spaces, hyphens, and apostrophes should be removed entirely or treated as non-game characters depending on how strict you want the rules to be. I stripped them out completely because it simplified the logic significantly and matched the version of the game I was trying to simulate. After filtering, you should generate a frequency map of letters across your entire word list. This map is the backbone of any decent Hanged Man Game solver. It tells you which letters appear most often in your pool of possible words. The standard English letter frequency chart (E, T, A, O, I, N, S, H, R, and so on) gives you a rough idea, but your actual distribution will differ because your word list is constrained by length and commonness. I found that building the map from my filtered list changed the ranking of several mid-tier letters enough to matter over hundreds of games.

How a Basic Hanged Man Game Works Under the Hood

The core loop of the game is straightforward. You pick a random word from your filtered list, display it as a series of underscores, and let the player submit one letter at a time. Each correct guess reveals all instances of that letter in the word. Each wrong guess increments the hang count. The player wins by revealing the full word before the hang count reaches the maximum, usually six or seven wrong guesses depending on how you set the rules. The computer version follows the same structure, except the computer is the one making guesses and you are watching it prune the word list after each submission. For the AI side, every guess is a two-step process. First, you determine which letter to guess next. Second, you update your possible word list based on the feedback. The letter selection step is where most implementations stumble. A naive approach picks the most frequent remaining letter across the entire alphabet. That works okay in the early rounds but falls apart quickly because it ignores the information already revealed by previous guesses. A better approach calculates the expected information gain of each candidate letter, which accounts for both how common the letter is and how well it splits your remaining word pool into smaller groups. This is essentially a simplified entropy calculation, and it makes a noticeable difference in win rate. The word list update step is equally important and often handled incorrectly. When a guessed letter appears in the hidden word, you need to keep only the words that match the revealed pattern exactly. If the word is six letters long and the first and fourth positions are now known to be E, you eliminate every word that does not have E in those positions and nowhere else unless E also appears elsewhere in the word. I once wrote a solver that only checked whether the guessed letter existed somewhere in each candidate word, without verifying the positions. That bug cost me an enormous number of games because the pool never shrank properly, and the solver kept making the same redundant guesses.

Practical Implementation Details

If you are building this in Python, which is the most common choice for this kind of project, the data structures you use will shape your implementation. A list of strings works for the word list, but you will do yourself a favor by converting each word to a tuple or frozenset of characters for comparison operations. Python string indexing is fast, and iterating over a list of words with list comprehensions is clean enough for a solo project. If you plan to run thousands of simulations, you will want to move to a more optimized structure, but for a first build, simplicity wins. Here is how the main game loop typically looks in code form. You select a random word, initialize an empty set for guessed letters, and enter a loop where you pick the next guess based on your strategy, check it against the target word, update the revealed pattern, prune the word list, and increment the wrong guess counter if needed. The loop exits when the word is fully revealed or the hang limit is reached. Rendering the current state as a ASCII hangman figure or simple text display is trivial and mostly decorative, but it helps humans follow what the solver is doing. One thing I want to mention specifically because I ran into this and it is easy to overlook: words with repeated letters behave very differently from words with unique letters, and your solver needs to account for that. When you guess a letter that appears multiple times in the target word, you reveal all instances at once, which dramatically reduces the remaining ambiguity. But if your frequency model does not properly weight the probability of multi-occurrence letters in your current word pool, you will underweight them and waste guesses. I fixed this by scoring each remaining letter not just on raw frequency but on the average number of positions it would reveal across the candidate words. That small adjustment improved my win rate by roughly eight to twelve percent over a few hundred test games.

Another edge case that caught me off guard involves words that share the same revealed pattern at some point during gameplay. Two different words in your list might look identical after five correct guesses, but they diverge on the sixth letter. At that moment, your solver effectively has no way to distinguish between them based on the visible information alone. The best it can do is pick the letter that is more probable across both words. This happens more often than you might expect, especially with longer words and mid-game states. It is not a bug in your logic, it is an inherent limitation of the game itself, but knowing that it exists changes how you evaluate solver performance. A solver that loses in that situation did not make a mistake, it hit a hard information boundary.

Common Pitfalls and What to Watch For

The biggest mistake people make when building a Hanged Man Game solver is treating it as purely a letter frequency problem. It is not. It is a dynamic information-state problem. The optimal guess at turn three is completely different from the optimal guess at turn six, even if the same letters remain unguessed. Your strategy needs to adapt based on how much of the word is revealed, how many wrong guesses remain, and how narrow or broad your candidate pool has become. Static frequency tables only help you in the first two or three guesses. A secondary issue is dictionary size. Larger dictionaries give your solver more knowledge but also slow it down and introduce more ambiguity. Smaller dictionaries speed up computation and reduce false matches but limit the range of words your solver can handle. I found that a curated list of around three to five thousand common words struck the best balance for a single-machine implementation. Going beyond that point produced diminishing returns because the extra words mostly added obscure terms that the game would rarely use anyway. Going below two thousand made the solver brittle against any word outside the narrow set. There is also the question of whether your solver should use a static alphabetical order for ties or a dynamic one. When two letters have identical scores in your selection algorithm, the tiebreaker matters. Using a fixed order like alphabetical is predictable and easy to debug. Using a random tiebreaker prevents your solver from being exploited if someone studies its patterns over time. I went with a shuffled frequency order that recalculates ties based on secondary metrics, which felt like the most honest approach for a general-purpose solver.

If you are looking to download a reference implementation or study existing code, searching for "hangman solver python" or "hanged man game github" will turn up several community projects. None of them are perfect, and most are educational rather than production-grade, which is exactly what you want when you are learning. The valuable ones are the ones that show their pruning logic clearly and do not hide the word list filtering behind opaque dependencies. Avoid projects that import heavy NLP libraries just to play a letter-guessing game. The whole point of this exercise is usually to understand the mechanics, not to wrap a transformer around a kindergarten game.

Advanced Nuances That Separate Decent Solvers From Good Ones

A decent solver wins about sixty to seventy percent of games against a random word selection. A good solver pushes that into the eighties. The gap comes down to three things: better pattern matching, smarter tiebreaking, and aware handling of low-frequency edge cases. Pattern matching means your solver can distinguish between a word like _A_A_ and _AA__ even when the visible letters are the same, because the internal structure differs. Smart tiebreaking means your solver does not blindly default to the same letter every time scores are equal. Low-frequency edge case awareness means your solver does not panic when it encounters a word like quixotic or psychopathy where standard frequency models break down. I also learned that running bulk simulations and logging the failures is the fastest way to improve your solver. Playing fifty games by hand teaches you nothing about systematic weaknesses. Playing five hundred games with detailed failure logging shows you exactly which words and which game states cause problems. I found that my initial solver struggled most with five and six letter words containing uncommon consonant clusters, particularly words with Q and X. Once I identified that through logs, I adjusted the scoring to give slightly more weight to rare letters in short words, which closed that gap without breaking performance on longer words. The Hanged Man Game might look simple on the surface, but it is an effective way to learn about probabilistic reasoning, state-based filtering, and the difference between theoretical letter frequency and practical word-level frequency. Building a working version is something you can do in an afternoon. Building one that consistently beats a human player takes a bit more care, mostly because the small oversights compound quickly over many games. If you stick with a filtered dictionary, track your failures, and resist the urge to overcomplicate the early stages, you will end up with something that works well enough to be useful and simple enough to actually understand.