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{VHaarenILP11,
  author = "Van Haaren, Jan and Van den Broeck, Guy",
  title = "Relational learning for football-related predictions",
  booktitle = "Preliminary Papers ILP",
  year = "2011",
  url="http://starai.cs.ucla.edu/papers/VHaarenILP11.pdf",
  keywords   = {conference}
}