Lets formulate the problem first, so that it can be addresses in an easier manner.
- Lets say we discretise match-time, by cutting it into windows of (say) 10 minutes each. Our goal is to be able to predict whether the next 10-minute window would contain a goal by our team or not.
Now that the target is defined, lets generate some features to predict the event.
- So, now we have some events happening some number(or zero) of times, in every of the 9 windows of time, events like attempt, foul, penalty, goal (for both teams). Count of such events in time-window would be their "frequency".
- Time passed since last time goal, foul, penalty etc happened.
Apart from frequency and recency, one can also get some continuous features like how much % of time, for this window, did "our" team keep the ball in their control? We can also create cumulative versions of frequency and continuous variables. Some other features like how much time to half-time, how much till full-time, can also be created.
Now using all these features, we can try to predict our stated goal.
Note that NNs require a lot of data to work with large number of variables, and they often overfit. This problem can easily be solved by simple classification algorithms (given it is solvable).