  It is not surprise for machine learning models to provide decent prediction
accuracy of soccer games outcomes based on various objective metrics. However,
the performance is not that decent in terms of predicting difficult and
valuable matches. A deep learning model is designed and trained on a real
sequential trading data from the real prediction market, with the assumption
that such trading data contain critical latent information to determine the
game outcomes. A new loss function is proposed which biases the selection
toward matches with high investment return to train our model. Full
investigation of 4669 top soccer league matches showed that our model traded
off prediction accuracy for high value return due to a certain ability to
detect dark horses. A further try is conducted to depict some indicators
discovered by our model for describing key features of big dark horses and
regular hot horses.
