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%.

中文翻译：

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使用实际流利目标优化长期结果：在足球中的应用

在本文中，我们提出了一种新颖的方法，通过封装给定时间范围内的联赛环境中的事件来优化足球（足球）的长期战术和战略决策。我们对球队的一个赛季目标进行建模，并随着比赛的进行跟踪这些目标的演变过程，从而为制定决策提供流畅的目标。我们开发了马尔可夫链蒙特卡洛和基于深度学习的算法，这些算法利用了流畅的目标，以便从先前的比赛和环境中的其他比赛中学习并提高球队的长期表现。使用来自760场比赛的真实世界数据集对我们的方法进行的模拟显示，通过对我们流畅的目标和先前的比赛使用优化的策略，我们平均可以增加球队的平均预期整理联盟分布，最高可达35.6％。