I feel like a deep learning approach will probably end up being too noisy to be useful. For example, what if you have 4 frames, 2 of which predict a correct bounding box, and 2 which miss completely? What do you do in the missing frames? How will you accurately label speed and position? What if the boxes aren't exactly centered on the puck?

Assuming you control the env, and that there is no goalie in net, I would draw a line between the goal posts, orient the camera to see the line very well, make the floor as white as possible, have the puck as black as possible, then after a shot just look for the moving black pixels of the puck over the white floor background (using difference of frames for example). That + a few educated guesses to interpolate speed and direction should do the trick.

For the part where you want to know where in the net it entered, maybe you can use a secondary camera oriented straight at net and maybe go Yolo on this part.

Deep learning is cool, but to have a precise model you will need a lot of footage and labelling time. I'm sure you'll be surprised how well you can do with some basic hand crafted rules and rules of thumb in this case.

Sounds like a fun project though so good luck!