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Fantasy Premier League (FPL) performance predictors tend to base theiralgorithms purely on historical statistical data. The main problems with thisapproach is that external factors such as injuries, managerial decisions andother tournament match statistics can never be factored into the finalpredictions. In this paper, we present a new method for predicting futureplayer performances by automatically incorporating human feedback into ourmodel. Through statistical data analysis such as previous performances,upcoming fixture difficulty ratings, betting market analysis, opinions of thegeneral-public and experts alike via social media and web articles, we canimprove our understanding of who is likely to perform well in upcoming matches.When tested on the English Premier League 2018/19 season, the modeloutperformed regular statistical predictors by over 300 points, an average of11 points per week, ranking within the top 0.5% of players rank 30,000 out ofover 6.5 million players.

Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football