Please Need help with Prediction Neural Network

Hi, for my senior design we have are using the YOLOv3 network for objection detection of the game of casino game of Roulette (specifically identifying the 0 pocket and Roulette ball). We already have annotated those images (~2,000ish) data set at this point and our getting decent object detection results. We also have coded functions that compute speed (pixels/second), radius/distance (pixels), angular velocity (degrees/second), theta (degrees), acceleration (degrees/second) per frame and output that data to a txt file ( can be CSV if needed). I was figuring that I could use a predictor neural net for like predicting Weather, Stock Market or House prices, since the general concepts seem the same.

Here’s a sample :
Beating Roulette

I thought outputting the parameters of each frame, like the initial speed/velocity of the ball, and where it finally lands (in what pocket) in a python data frame and run some Regression on it could produce “decent” results, i.e.

speed_initial    velocity_initial    acceleration_initial    theta_initial   pocket_final
   500,454              400                  433                  308             17
.....                .........            ......                 ......          .....

Obviously I would need a lot of data, but I thought the neural network would be able to build a model of what entry speeds and angles result in landing in some specific pocket.

Are there any better approaches? My teammate was planning on using a binary classifier to divide the wheel in 2-halves and the output parameters (speed, accel, theta, radius , etc…) into a binary classifier as a tensor…

If anyone has any suggestions please me know! I know there’s experts on these forums that may have done similar prediction models that maybe i can tweak/twist to fit this roulette prediction!