Footballnet: A Deep Learning Network for Football Competition Prediction

With the advancement of technology, artificial intelligence (AI) and big data have made a significant contribution on sports communication, especially in the field of enhancing audience engagement, personalized content recommendations, real-time data analysis, and targeted advertising. Predicting sports outcomes is still a challenging assignment because of some unpredictable factors such as player performance, weather, and tactical decisions. Traditional statistical models and machine learning algorithms, like logistic regression and XGBoost, have had limited success in capturing complex patterns in high-dimensional data. In this study we explore the novel application of convolutional neural networks (CNNs) for predicting the outcome of sports event. Leveraging CNNs' ability to identify intricate patterns, the proposed method shows improved accuracy, precision, recall and F1 score compared to other six traditional models, achieving up to 93%- 97%, 87.8%-92.2%, 91.5%-96.5% and 90.7-92.9 respectively. Besides, while there are some fluctuations influenced by Gaussian noise, the four targets of our model are still much higher than other traditional models, and both of results could show the stability and reliability of the model. The result of these could not only indicate that CNN offers a promising method for enhancing sports analytics, but also permits reliable and accurate predictions of complicated datasets.