The Wordle phenomenon from a few years back inspired developers worldwide to create their own word games. It inspired me to conceive and build a game, too. ‘Word Zearch’ combines elements of Boggle and word search puzzles into a web-based game where players find words on a board.
This tutorial will teach you how to build a complete game from scratch. You’ll implement advanced data structures (Trie), optimize search algorithms (recursion), and create a polished user interface (HTML/CSS). By the end, you will build a fully playable game and learn techniques that you can apply to any web project.
This tutorial covers:
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Implementing a Trie data structure for lightning-fast word search including partial word validation
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Using recursion to efficiently explore millions of possible paths through a game board
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Analyzing 20,000+ dictionary words to create balanced gameplay
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Creating a build system that pre-processes data for better performance
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Building a responsive UI that handles complex user interactions
The Inspiration
When playing Boggle with my family, I have designated myself as the ‘checker’ of other peoples’ words when we are tabulating scores. All of the other players will list the words that they found while I point them out on the board to make sure that they are valid. Once you know that a word is on the board, finding it feels a whole lot easier than searching for it blindly.
I enjoy this process almost as much as playing the game. So, I built a game that focuses on that experience.
The rules are simple. The game presents 49 random letter groupings in a 7×7 grid. Starting with the longest words that can be found, players are presented with words and are asked to find them. Players click on adjacent letter groupings (horizontally, vertically, or diagonally) to form words. Each letter group can be used once per game. The challenge is to find as many valid words as possible in the shortest amount of time.
You can play the finished game here: https://markm208.github.io/wordZearch/.
An Interactive Tutorial on Building Word Zearch
To share how I built Word Zearch, I created “How I Built It: Word Zearch“. It is a collection of annotated code playbacks that walk through the entire development process. These playbacks show every line of code I wrote, from implementing the core data structures to polishing the user interface.
To view a code playback, click on the comments in the left panel. Each comment updates the code in the editor and highlights the change. Read the explanation, study the code, and use the built-in AI assistant if you have questions. For more information about code playbacks, you can watch a short demo here.
Prerequisites
You should have some programming experience to follow along. I use object-oriented programming, recursion, and DOM manipulation throughout the tutorial.
If you’re new to web development, this offers a complete start-to-finish project that will help put all of the pieces together. If this tutorial moves too fast for you, consider starting with my introductory web development ‘book’ first: An Introduction to Web Development from Back to Front
What You’ll Learn
1. Building a Trie Data Structure
https://playbackpress.com/books/wordzearchbook/chapter/1/1
Learn how to implement a Trie (prefix tree) for fast word validation. This data structure is the foundation of the game’s performance.
Starting with a dictionary file, I parse 20,000+ words into a nested object structure. Each level represents a letter position. The implementation includes a search method that returns three values: FOUND, NOT FOUND, or PARTIAL.
This PARTIAL value is crucial. It indicates when the beginning of a dictionary word is found but it is not a complete word. Later on when searching for words on the board, it tells the algorithm when to stop searching, preventing millions of unnecessary operations during gameplay.
2. Modeling Letter Frequencies
https://playbackpress.com/books/wordzearchbook/chapter/1/2
Here, I analyze letter frequencies from the words in the dictionary to create weighted distributions for generating game boards randomly. I start by tracking how often single letters, two-letter combinations, and three-letter combinations appear across all dictionary words. This approach will work for words in any language as long as you have a dictionary file filled with words.
For each word, I extract all possible letter groupings of each size and count their occurrences. After processing the entire dictionary, I sort these groupings by frequency and select the most common ones. To ensure interesting game boards, I create proportional arrays where more frequent letter groups appear multiple times relative to their frequency. I’ll use this data to create a balanced game board that reflects real language usage by picking the most used groups of letters at random.
3. A Simple Web App
https://playbackpress.com/books/wordzearchbook/chapter/1/3
In this playback, I set up the foundation for the web application and create an efficient build process. Rather than rebuilding the Trie and calculating letter frequencies every time someone plays the game, I develop a build system that pre-generates these data structures.
Then I reorganize the project by creating a build folder and a template file. The BuildTrie class will read the dictionary, construct the word map and letter frequencies, and inject this data into a template file to generate a static Trie.js file. This approach significantly improves performance since the computationally expensive dictionary processing happens once during the build phase rather than on every page load.
4. Building the Game Board
https://playbackpress.com/books/wordzearchbook/chapter/1/4
Next, I implement the WordBoard class, which manages the 7×7 game board and contains the core algorithm for finding valid words. The board is configured to hold 49 letter groups with customizable distributions of single, double, and triple letter combinations.
The heart of the implementation is a recursive search algorithm in the solve method that explores all possible word paths. Starting from each position on the board, the algorithm moves in eight directions (horizontally, vertically, and diagonally), building up potential words by concatenating adjacent letter groups. To prevent infinite loops and ensure game rules are followed, I track visited positions so each letter group can only be used once per word.
The Trie integration provides a crucial optimization. When a letter sequence isn’t found in the Trie, the algorithm stops exploring that path, preventing unnecessary searching. Found words are organized by length in a results array, with each result storing the complete path through the board. The playback concludes with testing the WordBoard, demonstrating how it successfully identifies all valid words that can be formed from the randomly generated letter groups.
5. The Front End
https://playbackpress.com/books/wordzearchbook/chapter/1/5
In this final playback, I build the complete user interface for Word Zearch. I start by creating a 7×7 HTML table where each cell displays a letter group from the WordBoard.
I implement click handling that allows players to select adjacent letter groups, visually highlighting them as they build words. The core gameplay logic compares the player’s selected path against the pre-calculated valid word paths from the WordBoard‘s solve method.
When a match is found, I add visual feedback including directional arrows showing the word’s path, color coding for completed words, and preventing reuse of letter groups by marking them as solved. Found words are displayed in a results list with timestamps and links to their definitions. The interface also includes hover effects to highlight previously found words on the board and handles game completion by showing total time and offering a replay option.
The result is a fully interactive word search game with intuitive visual feedback and smooth gameplay.
Start Building
Word games make excellent programming projects. They combine interesting computer science concepts with practical web development skills.
Try playing the game first to understand what I’m building. Then dive into the code playbacks to see how it all comes together. If you get stuck, use the AI assistant like a tutor to help explain what is happening in the code. Afterwards, if you are feeling adventurous, try modifying the code, optimizing it, adding new features, or building your own word game!
Questions and feedback are always welcome here: mark@playbackpress.com
If you’d like to support my work and help keep Playback Press free for all, consider donating using GitHub Sponsors. I use all of the donations for hosting costs. Your support helps me continue creating educational content like this. Thank you!
Source: freeCodeCamp Programming Tutorials: Python, JavaScript, Git & MoreÂ
