Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data

This paper explores the predictive power of big social data in regards to football fans' off-line and on-line behaviours. We address the research question to what extent can big social data from Facebook predict the number of spectators and TV ratings in the case of Danish National Football Association (DBU). The predictive model was built from Facebook, match attendance, and TV ratings data sets from 2014-2016. The best fit was a linear regression model with GLM coding. Ultimately, the model did best when predicting the number of spectators based on the Facebook activity during a match as well as the activity from the last two weeks leading up to the match. Furthermore, the data reveals that photos generates the most activity on the national team's page and with videos running at higher production costs there might be some unexploited potential for DBU to improve its social media marketing strategy. Although data limitations are present, this research concludes that predictive models based on big social data can indeed offer important insights for companies to understand their customer base and how to improve marketing strategies.