We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predictive models for future game outcomes (which team will win?) and sports betting outcomes (which team will win with the point spread? will the total points be over under the line?). We experiment with several feature sets and find that simple features using large volumes of tweets can match or exceed the performance of more traditional features that use game statistics.
Recently, Hong and Skiena @cite_13 used sentiment analysis from news and social media to design a successful NFL betting strategy. However, their main evaluation was on in-sample data, rather than forecasting. Also, they only had Twitter data from one season (2009) and therefore did not use it in their primary experiments. We use large quantities of tweets from the 2010--2012 seasons and do so in a genuine forecasting setting for both winner WTS and over under prediction.
@cite_13
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The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60 of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53 ).