Neural Network Predict Soccer Results
I did this for the 2017 UK Premier league season. However I accumulated the first 19 (out of 38 Games), in order to try and help with my predictions.
I would attack this in the following manner

Get Data on the Teams (What you consider data I will leave up to you)
Get the History of previous matches (Personally I think this is not going to help, as teams change so much)
Python


Pandas


Create New Features

Keras
Model away


When I was playing with the UK Premier league, got a prediction accuracy of aprox 62%. So how to make it better ?

Distance travelled


Improved it by 1.2%. Some teams do not like traveling it seems.

Weather


I got the weather forecast per ground @ Kick-Off-Time (What a pain)
Accuracy improved by 0.5%


What I did not do - was get a list of Players who were playing per game, per day. And attribute things like distance run, passing percentage, goals, fouls, yellow cards etc
I've going on a little competition with friends: We're writing models for the upcoming worldcup to see whoms model gets the most points out of a tip game.
So my approach would be to write a neural network and train it with previous worldcup results regarding to the anticipated wining rates (back then), to maximize the score of the tip game (e.g. 6 points for the exact score, 4 correct goal difference, 3 correct winner).
Scrap rates from various sites (bwin etc.) and let the network tip for me.
I'm familiar with linear algebra, probability calculus etc., but have never programmed a neural network yet.
Since I have not much time left, could someone help me by picking the best approach (like which concept/algorithm I should use) or link me a tutorial to a similar problem or the approach that would fit?
Best,
  Hannes