Abstract. Association football has recently seen some radical changes, leading to higher financial stakes, further professionalization and tech-nical advances. This gave rise to large amounts of data becoming avail-able for analysis. Therefore, we propose football-related predictions as an interesting application for relational learning. We argue that football data is highly structured and most naturally represented in a relational way. Furthermore, we identify interesting learning tasks which require a relational approach, such as link prediction or structured output learn-ing. Early experiments show that this relational approach is competitive with a propositionalized approach for the prediction of individual foot-ball matches ’ goal difference.

@inproceedings{VHaarenBenelearn12,
  author = "Van Haaren, Jan and Van den Broeck, Guy",
  title = "Relational learning for football-related predictions",
  location = "Gent, Belgium",
  booktitle = "Proceedings of the 21st Belgian-Dutch Conference on Machine Learning",
  pages = "85",
  year = "2012",
  url="http://starai.cs.ucla.edu/papers/VHaarenBenelearn12.pdf",
  keywords   = {abstract}
}