使用真实世界的流畅目标优化长期结果：足球应用
Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football
摘要：
在本文中，我们提出了一种新的方法来优化足球（足球）的长期战术和战略决策封装在一个给定的时间框架内的联赛环境中的事件。我们为一个赛季的球队目标建模，并跟踪这些目标如何随着比赛的展开而演变，以提供一个流畅的目标，可以帮助决策游戏。我们开发了基于马尔可夫链蒙特卡罗和深度学习的算法，这些算法利用流畅的目标，以便从环境中的先前游戏和其他游戏中学习，并提高团队的长期表现。使用来自760场比赛的真实数据集对我们的方法进行的模拟表明，通过使用我们流畅的目标和之前的比赛的优化战术，我们可以平均将球队在联赛中的平均预期完成分布提高高达35.6%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
