  In this work, a machine learning approach is developed for predicting the
outcomes of football matches. The novelty of this research lies in the
utilisation of the Kelly Index to first classify matches into categories where
each one denotes the different levels of predictive difficulty. Classification
models using a wide suite of algorithms were developed for each category of
matches in order to determine the efficacy of the approach. In conjunction to
this, a set of previously unexplored features were engineering including
Elo-based variables.
  The dataset originated from the Premier League match data covering the
2019-2021 seasons. The findings indicate that the process of decomposing the
predictive problem into sub-tasks was effective and produced competitive
results with prior works, while the ensemble-based methods were the most
effective.
  The paper also devised an investment strategy in order to evaluate its
effectiveness by benchmarking against bookmaker odds. An approach was developed
that minimises risk by combining the Kelly Index with the predefined confidence
thresholds of the predictive models. The experiments found that the proposed
strategy can return a profit when following a conservative approach that
focuses primarily on easy-to-predict matches where the predictive models
display a high confidence level.
