Critical Play: Puzzles – Factory Balls – Mateus

In this critical play, I will examine the game Factory Balls, created by Belgian independent developer Bart Bonte and hosted on Poki.com. Based on my experience playing the game, I would say it targets an audience of at least teenage age, as it requires logical reasoning and a certain degree of cognitive maturity.

The core objective of the game is to paint a plain ball to match a given reference presented at each level. To achieve this, the player uses everyday objects such as construction helmets, belts, bowls, flower seeds, grass seeds, and watering cans. While the game might initially appear simplistic or even childish, its difficulty increases steadily, and later levels present a significant logical challenge.

From an aesthetic perspective, Factory Balls evokes three dominant forms of player engagement: Challenge, Discovery, and Submission. As with many puzzle games, challenge is central; each level introduces a new obstacle, increasing in complexity and requiring more intricate sequencing of actions. Discovery emerges as new tools are introduced in later levels. For example, level 19 introduces sideways bowls for the first time, expanding the player’s toolkit and prompting experimentation (see Figure 1). Lastly, Submission manifests as players dedicate increasing amounts of time to solving each level. The repetitive nature of trial-and-error becomes absorbing, often without the player realizing how much time they have spent.

Figure 1 – Example of a level in Factory Balls. Notice that in this level, four different paint colors and five distinct objects are available to achieve the target ball design

 

The gameplay can be broken down into two core demands: (1) object placement and (2) color application. Players place objects on the ball to restrict specific areas from being painted. Once masked, the player selects a color and paints the exposed areas. This sequence continues until the player reproduces the target design. For instance, in the example illustrated in Gif 1, the player places a diagonal belt on the ball and then applies blue dye. The result is a blue ball with a grey diagonal stripe where the belt had masked the surface. Each level is solved by logically sequencing object placement and color use to replicate the reference image.

 

Gif 1 – Example of a game mechanic in Level 9. Note how placing a belt on the ball and painting it causes the area covered by the belt to retain its previous color – via GIPHY

This mechanic results in engaging puzzle dynamics. The game becomes enjoyable because players must identify the correct combinations of tools and colors, using pattern recognition, memory, and reasoning to reach the goal. The outcome of each action is immediately visible, supporting a natural trial-and-error loop that encourages iteration and learning.

From an ethical standpoint, the game is fair. It assumes minimal prior knowledge—primarily basic logical thinking and some common-sense associations. For example, if seeds are planted and then watered, flowers or grass grow. These assumptions make the game accessible to a wide audience.

However, there are notable accessibility limitations. The game heavily relies on color differentiation, which can be problematic for players with color vision deficiencies. It lacks patterns, icons, or textures to supplement color cues. Additionally, the game is mouse-only, offering no keyboard shortcuts or alternative controls, limiting access for players with motor impairments. Lastly, the absence of a hint system may lead to frustration when players get stuck on a level with no clear support or feedback mechanisms.

About the author

Hello! My name is Mateus Gheorghe de Castro Ribeiro, I am a PhD student in the Stanford Sustainable Systems Lab (S3L) at Stanford University 🌲.

I am passionate about leveraging artificial intelligence and engineering to drive research that contributes to a better world 🌎👨‍💻💡

Throughout my career, I have explored diverse topics, including fuzzy logic systems ✨, ultrasonic waves 🔉, signal processing 🖥️, and Structural Health Monitoring 📌

Currently, my research focuses on AI-driven solutions for sustainable energy systems, particularly machine learning applications to optimize the integration of renewables 🌤, storage 🔋, electric vehicles 🚙, and charging infrastructure ⚡

In my free time, I love both playing and watching soccer ⚽. When it comes to games, I’m especially into video games 🎮. Although I don’t play as much nowadays, my favorites are RPGs like Skyrim, open-world games like Grand Theft Auto, and soccer games like FIFA.

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