I was hoping that this article would go into deeper techniques than just interpolation and splines.
I would be cool to see an example of how a Kalman filter approach would compare in terms of precision and latency. My expectation is that it would be the best of both worlds.
Or even a physics simulation—if the mouse is moving with a given velocity, that velocity won't change super fast. So even if your data is a bit behind, you can use physics to estimate where they probably are now. And if you get new data, and your estimation is too far off, then move the mouse to the right spot. If it's mostly correct, just base your future simulation off of the new information, so that it moves smoothly towards the correct value.
I would be cool to see an example of how a Kalman filter approach would compare in terms of precision and latency. My expectation is that it would be the best of both worlds.