Sports Analytics with Natural Language Processing: Using Crowd Sentiment to Help Pick Winners in Fantasy Football

Abstract

We develop a method for maximizing potential in the sports analytics picking winners problem. We do so by: i) leveraging BERT-based, large-scale sentiment anal- ysis of tweets about players. ii) statistical modeling incorporating sentiment analysis, weather, stadium, and other professional projections to create player projections. iii) an integer programming algorithm to construct player lineups. Our experiments show that this method outperforms similar lineup creation using solely existing professional projections by an order of magnitude, with player projections alone outperforming professional projections by nearly 30%. These results suggest a new approach to sports prediction modeling that relies on natural language processing and state-of- the-art language models to incorporate the wisdom of the crowd.