Predicting Wins and Spread in the Premier League Using a Sentiment Analysis of Twitter

Speaker:  Robert P. Schumaker – New Britain, CT, United States
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Can the sentiment contained in tweets serve as a meaningful proxy to predict match outcomes and if so, can the magnitude of outcomes be predicted based on a degree of sentiment?  To answer these questions we constructed the CentralSport system to gather tweets related to the twenty clubs of the English Premier League and analyze their sentiment content, not only to predict match outcomes, but also to use as a wagering decision system.  From our analysis, tweet sentiment outperformed wagering on odds-favorites, with higher payout returns (best $2,704.63 versus odds-only $1,887.88) but lower accuracy, a trade-off from non-favorite wagering.  This result may suggest a performance degradation that arises from conservatism in the odds-setting process, especially when three match results are possible outcomes.  We found that leveraging a positive tweet sentiment surge over club average could net a payout of $3,011.20.  Lastly, we found that as the magnitude of positive sentiment between two clubs increased, so too did the point spread; 0.42 goal difference for clubs with a slight positive edge versus 0.90 goal difference for an overwhelming difference in positive sentiment.  In both these cases, the cultural expectancy of positive tweet dominance within the twitter-base may be realistic.  These outcomes may suggest that professional odds-making excessively predicts non-positive match outcomes and tighter goal spreads.  These results demonstrate the power of hidden information contained within tweet sentiment and has predictive implications on the design of automated wagering systems.

About this Lecture

Number of Slides:  42
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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