Bigfoot

Inspiration: We found a foosball table abandoned on the street in Boston, likely on its way to a landfill. Since we all enjoy playing foosball during lunch breaks or at bars on weekends, we decided to repair it and give it a second life. From there, we wanted to go beyond simple restoration and add improvements to enhance gameplay and the overall experience.

What it does: Our main idea was to redesign the traditional foosball experience by adding more strategy and complexity. In a standard foosball table, the players are fixed in place. We invented a system that allows players to change their formation by moving the foosball figures into different positions, depending on the players’ preferences.

We also added aesthetic and gameplay improvements, including automatic goal detection and LEDs embedded in the score markers that light up when a goal is scored.

How we built it: We rebuilt the foosball table using a mix of restored and newly made parts. Some original parts were still in good condition, so we kept and reused them. Other damaged components had to be rebuilt from scratch.

The movable players were handmade from wood: we cut and shaped them manually, drilled holes, and inserted magnetic components to enable repositioning on the rods/playing system.

Challenges: We initially wanted to automate score detection using an IR emitter and receiver system, but it did not work reliably in our setup. We then switched to a button sensor to detect the mechanical impact/movement associated with a goal, which was a more practical solution for our prototype.

Accomplishments: Some team members were not familiar with electronics or hardware, so building the project was a real challenge. We’re especially proud that our core idea — player mobility and customizable formations — works in practice, and that we were able to play real games with the modified table.

What's next : Next, we want to improve the locking mechanism so that the players cannot be easily removed or stolen, especially if this system is used in bars or other public spaces. We’d also like to make the goal detection more robust and refine the overall finish of the table.