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

Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football