GoKawiilI Simulated 38,612 Countryle Games to Find the Best Strategy
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I have always enjoyed geography games. For a long time I was obsessed with GeoGuessr, and more recently I got pulled into the collection of daily geography puzzles on the web. Games like Globle, Travle, and Countryle all test your geographic intuition in different ways, asking you to use hints and spatial reasoning to work your way toward the correct country.
Your browser does not support the video tag. Today's game really challenged my lacking West-Africa geography knowledge. In Countryle, after entering a country, the game tells you in which cardinal direction the target lies. It also reveals whether you matched the correct continent or hemisphere, and whether the target country is larger or smaller in population and warmer or colder in average temperature. While playing, I kept asking myself the same questions: Is there a best opening guess? And once I got the feedback, what is the best next move? Reverse engineering the game The easiest way to “solve” Countryle is to decrypt the daily target country string the backend provides the frontend with. Since everything else from there on happens in the browser, the decryption key has to be shipped somewhere in the JavaScript bundle. That route is possible, but it is also boring. I wanted to solve the game the same way a player does: only using the feedback that appears after each guess. So instead of reading the answer directly, I extracted the dataset that Countryle uses internally and rebuilt the decision process from scratch. Countryle’s feedback is provided through five channels: Cardinal direction: north, south, east, west, and the diagonal directions. Continent: whether the guessed country shares the target’s continent. Hemisphere: whether the guessed country shares the target’s hemisphere. Population: whether the target is smaller, larger, or close in size. Average temperature: whether the target is colder, warmer, or close in value.
For population and temperature, the game effectively operates with buckets. In my solver these become three states: wrong, close, and correct. wrong, indicated by Countryle in red, means the target lies clearly outside the tolerance range, close, depicted with yellow, means it is in the broader neighborhood, and the green correct means it falls within a narrow band around the true value. These buckets matter because they determine how aggressively the remaining search space can be filtered after each guess.
Not all feedback channels are equally valuable. Hemisphere is useful once, but afterwards it often becomes irrelevant. That's also why I excluded it from any of the following figures. Continent can be highly informative when it isolates a small region, but much less so when the result is something like Africa, which still leaves dozens of possibilities. Direction, temperature, and population tend to carry much more granular information, especially when combined.
Turning Countryle into a solver
I built five modules, one for each feedback type. Each module has two jobs.
Filter: remove countries that are inconsistent with the feedback we just received. If Countryle says the target country is west of Bulgaria, every country that is not west of Bulgaria can be discarded immediately. Score: among the remaining candidates, pick the next country that is expected to reveal the most information.
The filter always runs first. The scorer only decides what to guess next after the candidate list has already been reduced. This separation made the whole pipeline modular: I could enable or disable individual feedback channels, compare their usefulness, and later reuse the same logic in an automated bot.
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