Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional Networks

The most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). The data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while The Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. The rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%.