Sports match prediction model for training and exercise using attention-based LSTM network
Qiyun Zhang, Xuyun Zhang, Hongsheng Hu, Caizhong Li, Yinping Lin, Rui Ma
Abstract
Sports matches are very popular all over the world. The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match. It's a challenging effort to predict a sports match. Therefore, a method is proposed to predict the result of the next match by using teams' historical match data. We combined the Long Short-Term Memory (LSTM) model with the attention mechanism and put forward an AS-LSTM model for predicting match results. Furthermore, to ensure the timeliness of the prediction, we add the time sliding window to make the prediction have better timeliness. Taking the football match as an example, we carried out a case study and proposed the feasibility of this method.