We start from the principle that natural language is too complex for computer programs, that's why it is difficult to have simple programs that can mine natural language in an effective way. Therefore, if we could find a way to simplify the language, it would make it easier to design computer-assisted knowledge management programs. The proposed approach is to simplify the natural language into sequences of predefined keywords. The translation from the natural language to the list of keywords is done by systematic application of a set of regular expressions: when a pattern is matched, it translates into a key word. The keywords obtained are put in sequence to re-express the main ideas in the natural language, but using only the pre-defined keywords.

In this paper, we present two approaches to analyzing pass event data to uncover sometimes-nonobvious insights into the game of soccer. We illustrate the utility of our methods by applying them to data from the 2012–2013 La Liga season. We first show that teams are characterized by where on the pitch they attempt passes, and can be identified by their passing styles. Using heatmaps of pass locations as features, we achieved a mean accuracy of 87% in a 20-team classification task. We also investigated using pass locations over the course of a possession to predict shots.