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

TL;DR

This paper introduces a deep learning approach trained on trading data to identify dark horse soccer teams, emphasizing high-value match predictions and uncovering key indicators of underdog success.

Contribution

The study proposes a novel loss function and demonstrates the model's ability to detect dark horses by trading prediction accuracy for high return, revealing new indicators of underdog teams.

Findings

Model detects dark horses with high investment return.

Trade-off between prediction accuracy and high-value detection.

Indicators of key features of dark horses identified.

Abstract

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.