Reverse Engineering using machine learning

First I want to say I'm completely new to the machine learning paradigm and have only discussed it in theory. I have been trying to put it into practice but I'm confused on how to derive the dataset in a way that would allow me to reverse engineer the following problem:

Let's say we have a dataset with a few traits(attributes for a player) and we are trying to reverse engineer the formula for deciding if a player scores a goal:

Let's say the attributes are the following. All numbers are from 0-100.

AGI     AWR    KP    KA     Tec(technique)
Player 1     44      60     90    70      66

Let's say the real formula we are trying to reverse engineer is the following:

.25*AGI + .15*AWR + .30*((KP+KA)/2) + .30*(Tec) + diceRoll(3)

And let's say if the number comes out to be greater than 85 the player scores the goal.

Let's say our data set essentially has a bunch of players kick attempts at the goal, and has a true or false for score like the following:

AGI     AWR    KP    KA     Tec  Score
60      40     70    30      50    1(true)
44      60     90    70      66    0(false)
90      90     60    65      38    0(false)

Is there a way to train a neural network that essentially predict outcomes based on a player's attributes?

Is this the correct use of a neural network? or is there a better tool suited to figure this out?