This paper presents a software and hardware framework for using slot cars as a competitive learning and developmental platform in control engineering. A novel mathematical model of the slot car is presented, facilitating experimentation and development of control algorithms and tracking systems in a simulated environment. Additionally, we present a minimally invasive modification to a Carrera slot car track, which seamlessly transitions the algorithms from simulation to a real world setup. The slot car’s in-track states are estimated using an extended Kalman filter and a web camera, with flexibility for integration with other sensors. The Kalman filter provides real-time tracking of the slot car states, allowing for control feedback during the race. A comparison between physical trials and simulator runs is shown to correspond well.