  In this paper, we present a new application-focused benchmark dataset and
results from a set of baseline Natural Language Processing and Machine Learning
models for prediction of match outcomes for games of football (soccer). By
doing so we give a baseline for the prediction accuracy that can be achieved
exploiting both statistical match data and contextual articles from human
sports journalists. Our dataset is focuses on a representative time-period over
6 seasons of the English Premier League, and includes newspaper match previews
from The Guardian. The models presented in this paper achieve an accuracy of
63.18% showing a 6.9% boost on the traditional statistical methods.
