  This paper presents a groundbreaking model for forecasting English Premier
League (EPL) player performance using convolutional neural networks (CNNs). We
evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming
player FPL score based on historical FPL data over the previous weeks. Our
baseline models, Ridge regression and LightGBM, achieve solid performance and
emphasize the importance of recent FPL points, influence, creativity, threat,
and playtime in predicting EPL player performances. Our optimal CNN
architecture achieves better performance with fewer input features and even
outperforms the best previous EPL player performance forecasting models in the
literature. The optimal CNN architecture also achieves very strong Spearman
correlation with player rankings, indicating its strong implications for
supporting the development of FPL artificial intelligence (AI) Agents and
providing analysis for FPL managers. We additionally perform transfer learning
experiments on soccer news data collected from The Guardian, for the same task
of predicting upcoming player score, but do not identify a strong predictive
signal in natural language news texts, achieving worse performance compared to
both the CNN and baseline models. Overall, our CNN-based approach marks a
significant advancement in EPL player performance forecasting and lays the
foundation for transfer learning to other EPL prediction tasks such as win-loss
odds for sports betting and the development of cutting-edge FPL AI Agents.
