[Newbie Question]

Say I wanted use a deep learning neural network to model 100k samples of 3 children playing Monkey in the Middle, a game where two outer children (child 1 and child 2) are attempting to pass a ball back and forth to one another while the inner child (child 3) attempts to intercept the ball. Perhaps there is some environmental effect on all of them, such as low gravity. In this implementation methodology I'm imagining I would be able to pass to the input nodes something like : {child 1 arm strength, child 1 accuracy, child 1 speed, child 1 hand/eye coordination, child 2 arm strength, child 2 accuracy, child 2 speed, child 2 hand/eye coordination, child 3 veritcal leap, child 3 arm length, child 3 ability to invoke empathy in others, environmental gravity, wind speed, wind direction} with the hopes of getting some output (based on a scoring scheme) like: {Child 1 performance, child 2 performance, child 3 performance} I know I could just pass all of these arguments into some sort of neural network and hope for the best, but from what I've read it might not work very well because the inputs are describing 4 separate phenomena (child 1,2,3, environment), the performance of which are all dependent on one another (except environment which simply exerts influence on the other 3), including some variables which are describing exactly the same feature, but in different entities in the system. Is this a problem that is simply not well suited to a deep learning neural network approach, or have I simply missed or misunderstood how one might approach it? Thanks in advance for helping a misguided noob. Edits: formatting