  Sports data has become widely available in the recent past. With the
improvement of machine learning techniques, there have been attempts to use
sports data to analyze not only the outcome of individual games but also to
improve insights and strategies. The outbreak of COVID-19 has interrupted
sports leagues globally, giving rise to increasing questions and speculations
about the outcome of this season's leagues. What if the season was not
interrupted and concluded normally? Which teams would end up winning trophies?
Which players would perform the best? Which team would end their season on a
high and which teams would fail to keep up with the pressure? We aim to tackle
this problem and develop a solution. In this paper, we proposeUCLData, which is
a dataset containing detailed information of UEFA Champions League games played
over the past six years. We also propose a novel autoencoder based machine
learning pipeline that can come up with a story on how the rest of the season
will pan out.
