What if Wikipedia’s link-based rabbit hole had a competition mode—turning random article clicking into a full-blown mind sport? That’s Wikispeedia: an online game where players race from one Wikipedia article to another, hopping through hyperlinks, with the goal of reaching the target article. According to Google Trends, Wikispeedia peaked around 2007, but has gradually declined in popularity since.
But what if we could reignite that spark?
Think of chess. Despite having originated thousands of years ago, it’s still at the very forefront of competitive gaming. Sure, a popular Netflix series helped, but the real driving force behind chess’s lasting appeal is its dedicated community and the thriving ecosystem built around it. From Elo ratings to move-by-move analysis, the entire experience incentivizes both novice players and grandmasters to improve, compete, and ultimately have fun.
That synergy between community engagement and robust, well-engineered mechanics is exactly what we want to replicate for Wikispeedia.
Enter the “chessification” of Wikispeedia.
We asked ourselves: “What if Wikispeedia had its own rating system, match-ups based on difficulty, and game-quality analysis—just like chess?” With a robust ecosystem, players could measure their progress, watch themselves climb the ranks, and share replayable “moves” (or clicks) that led them to victory. We want to leverage all the mechanics that make chess so engaging and transplant them into Wikispeedia, thus crafting a platform that both excites new players and incentivizes veterans to keep playing.
In a Nutshell:
- Rate the difficulty of a Wikispeedia puzzle.
- Match players with challenges at their skill level.
- Develop an Elo-like system that encourages improvement and promotes competition.
- Analyze “moves” to find good links and blunders, just as a chess engine might.
With these core ideas, we see the potential for a blossoming Wikispeedia community—maybe one day it’ll even have its own Emmy- or Golden Globe–winning series (hey, stranger things have happened!).
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Continue reading to learn how we engineered this system. We’ll dive deep into how we measure game difficulty, develop a skill rating, and predict which links players might click next. This data story will walk you through our entire approach—move by move—until we, hopefully, checkmate the problem.
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"Before anything else, preparation is the key to success." — Alexander Graham Bell
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In this section, we’ll lay the groundwork—establishing the rules, pieces, and playing conditions that will guide our entire analysis. Just like a chess player studies the board and the position before making the first move, we need to thoroughly understand Wikispeedia’s underlying data and current frameworks. Here, we’ll explore the dataset driving our “chessification”, identify our virtual opponents, reason about player identity, and recognize the flaws in current difficulty ratings before we propose a more objective one. Our goal is simple: set the stage so that every subsequent move we make builds on solid, well-analyzed ground.
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