This may seem like a silly question.
I am running a neural network through some tennis data. The objective of the network is to determine the probability of each player winning the match. There are around 40 inputs, and one output (being the probability of player A winning, (1 – output) for player B).
The inputs are various statistics and performance measures of each player over the last n matches. I’ve written the code that extracts these numbers from my database of tennis match results, which are then fed into the neural network.
The problem I have is as follows:
In the training set, the input values relating to the winner of the match being analysed by the network, will always be fed through the same input neurons. Because of this, the desired output will always be 1, because player A always wins (this is how my database is structured, player A is the winner of the match and player B is the loser).
How can I overcome this issue? Is it simply a case of randomising the player A and player B orders?
Hope this question makes sense.
Many Thanks
I would train every match twice, once with the input Winner – Loser and the output ‘1’, once with the input Loser – Winner and the desired output ‘0’.
(Oh, and I don’t think a neural network output can be interpreted as a probability, in the sense that if the ANN predicts some outcome with output 0.9 it will be right 9 out of 10 times.)