Neural networks are often used in the prediction of soccer results. The methods used are many and varied but they usually depend directly on previous scores to assess the team strengths. The models produced often do just a little better than a random choice. The prediction of home and away wins using a neural network has proved easier than draw prediction but the soccer expert, almost always, does better than a neural network. Harry uses the ratings that are produced by an expert.
Harry has constructed a neural network that uses some of the RATEFORM values from
The RATEFORM home away values and the difference between them are used as inputs. The match result is used for output. The original network uses three different columns for home, draw and away. This can produce misleading validating results because the network has no knowledge that the three outputs are exclusive. So far as the network knows the three outputs have eight different possible combinations - not three. A forth output has been added with three values: 1 - home win, 2 - draw and 3 away win. The three original output columns are excluded. The network produces 53% correct results on the validating examples. A random choice would be only 33% correct.
This shows that Harry's network produces a significant advantage when picking matches.