  We designed a multilayer perceptron neural network to predict the price of a
football (soccer) player using data on more than 15,000 players from the
football simulation video game FIFA 2017. The network was optimized by
experimenting with different activation functions, number of neurons and
layers, learning rate and its decay, Nesterov momentum based stochastic
gradient descent, L2 regularization, and early stopping. Simultaneous
exploration of various aspects of neural network training is performed and
their trade-offs are investigated. Our final model achieves a top-5 accuracy of
87.2% among 119 pricing categories, and places any footballer within 6.32% of
his actual price on average.
