Players’ Performance Prediction for Fantasy Premier League, Using Transformer-based Sentiment Analysis on News and Statistical Data

Abstract

Fantasy sports have become increasingly popular, with millions of players engaging in strategic team management and competition. In the realm of Fantasy Premier League (FPL), effective player analysis and performance prediction are crucial for success in each game. This paper presents an innovative approach to enhance FPL analysis and performance prediction by integrating news sentiment and players’ injury with statistical data sources. A dataset of weekly news articles was enriched through pretrained transformer-based sentiment analysis toolkit and combined with different boosting and neural network algorithms for prediction tasks. Our findings demonstrate that integrating these features enhances model performance, with the CNN architecture achieving a reduction in MSE from 6.27 to 5.63 outperforming the state of the art model. These results highlight the potential of leveraging diverse data sources for more accurate predictions and informed decision-making in FPL.

Keywords

• fantasy premier league (fpl)
• machine learning
• neural networks
• sentiment analysis
• transformers