  Real-world images used for training machine learning algorithms are often
unstructured and inconsistent. The process of analysing and tagging these
images can be costly and error prone (also availability, gaps and legal
conundrums). However, as we demonstrate in this article, the potential to
generate accurate graphical images that are indistinguishable from real-world
sources has a multitude of benefits in machine learning paradigms. One such
example of this is football data from broadcast services (television and other
streaming media sources). The football games are usually recorded from multiple
sources (cameras and phones) and resolutions, not to mention, occlusion of
visual details and other artefacts (like blurring, weathering and lighting
conditions) which make it difficult to accurately identify features. We
demonstrate an approach which is able to overcome these limitations using
generated tagged and structured images. The generated images are able to
simulate a variety views and conditions (including noise and blurring) which
may only occur sporadically in real-world data and make it difficult for
machine learning algorithm to 'cope' with these unforeseen problems in
real-data. This approach enables us to rapidly train and prepare a robust
solution that accurately extracts features (e.g., spacial locations, markers on
the pitch, player positions, ball location and camera FOV) from real-world
football match sources for analytical purposes.
