论文标题
通过从连续交易数据中深入学习来发现足球比赛的黑马指标
Discovering indicators of dark horse of soccer games by deep learning from sequential trading data
论文作者
论文摘要
对于机器学习模型提供了基于各种客观指标的足球比赛结果的体面预测准确性并不奇怪。但是，在预测困难和有价值的匹配方面，性能并不是那么体面。深度学习模型的设计和培训是对实际预测市场的真实顺序交易数据进行培训，并假设此类交易数据包含关键的潜在信息以确定游戏成果。提出了一种新的损失函数，该功能将选择偏向于与高投资回报率一起培训我们的模型的匹配。对4669场顶级足球联赛比赛的全面调查表明，由于一定能够检测到黑马的能力，我们的模型将高价值回报的预测准确性交换为高价值。进行了进一步的尝试来描述我们模型发现的一些指标，以描述大黑马和普通热马的关键特征。
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.
