Deep Learning Algorithms for Sports Data Analysis and Prediction in Sports Competitions

Sports data analysis and prediction are essential for gaining a competitive advantage in today’s sports. Artificial Neural Networks (ANNs) have shown promising outcomes in several disciplines, including sports analytics. Sports data is dynamic and complex, making it difficult for standard ANNs to identify minute patterns in it. We introduce a new Puzzle-Optimized Artificial Neural Network (PO-ANN) in this work, which is intended for sports data processing and prediction. The PO-ANN is optimized using a puzzle-inspired method to enhance the network’s ability to identify and comprehend complex patterns in the data. The technique constantly modifies the weights and network topology, enabling the model to better react to the shifting dynamics of sports competitions. The Indian Premier League provided the dataset, which consists of 950 matches and 20 variables (IPL). We implemented our proposed PO-ANN and forecast accuracy in sports data analysis and prediction using Python. We performed a comparison analysis between our suggested PO-ANN approach and other existing methods, using numerous metrics, including MSE, MAE, and MAPE. The suggested POANN technique produced better outcomes than the previous approaches.