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It is not surprise for machine learning models to provide decent predictionaccuracy of soccer games outcomes based on various objective metrics. However,the performance is not that decent in terms of predicting difficult andvaluable matches. A deep learning model is designed and trained on a realsequential trading data from the real prediction market, with the assumptionthat such trading data contain critical latent information to determine thegame outcomes. A new loss function is proposed which biases the selectiontoward matches with high investment return to train our model. Fullinvestigation of 4669 top soccer league matches showed that our model tradedoff prediction accuracy for high value return due to a certain ability todetect dark horses. A further try is conducted to depict some indicatorsdiscovered by our model for describing key features of big dark horses andregular hot horses.

Discovering indicators of dark horse of soccer games by deep learning from sequential trading data