  In this paper, we present a novel approach for optimising long-term tactical
and strategic decision-making in football (soccer) by encapsulating events in a
league environment across a given time frame. We model the teams' objectives
for a season and track how these evolve as games unfold to give a fluent
objective that can aid in decision-making games. We develop Markov chain Monte
Carlo and deep learning-based algorithms that make use of the fluent objectives
in order to learn from prior games and other games in the environment and
increase the teams' long-term performance. Simulations of our approach using
real-world datasets from 760 matches shows that by using optimised tactics with
our fluent objective and prior games, we can on average increase teams mean
expected finishing distribution in the league by up to 35.6%.
