Well i'm assuming that most people aren't using two cameras so your acceleration/velocity estimates are based on the projection of the object onto the image plane -- using this to predict trajectory is noisy at best.
Given that you have the object centroid in an image plane you can localize the object to a single "ray" in space (look up how perspective projection works
http://en.wikipedia.org/wiki/Perspec...ive_projection)
If are interested in AI (you mentioned markov models) perhaps you could build a theoretical model for ball behavior once projected to the image plane-- would be an interesting project involving math (mostly geometry and maybe statistics if you model noisy measurements probabilistically)
The bounding box size of the "ball" in the image plane gives you a pretty good estimate on depth along that ray. This gives you an estimate of 3d position. Perhaps you can fit a curve through the 3-4 estimated previous 3d positions of the ball and use that curve to project the balls motion forward.
The next part is extracting 3d velocity/acceleration measurements from a 2d projection -- this will involve doing the inverse perspective transform on the 2d measurements (which is possible given your depth estimate for the ball). Alternatively a much easier way is just to work in 3d ... i.e use the 3d estimates to find 3d velocities -- I think this is maybe slightly less error prone
Note that this idea is probably not practical in terms of time/ performance benefit but is a great project from a theoretical /research perspective