论文标题
基于自动编码器的模拟运动游戏的方法
An Autoencoder Based Approach to Simulate Sports Games
论文作者
论文摘要
在最近，体育数据已广泛使用。随着机器学习技术的改进，已经尝试使用体育数据不仅分析单个游戏的结果，还可以改善洞察力和策略。 Covid-19的爆发在全球范围内打断了体育联赛，引起了人们对本赛季联赛结果的疑问和猜测。如果本赛季没有被打断并正常结论怎么办？哪些球队最终会赢得奖杯？哪些球员会表现最好？哪支球队会以高位结束赛季，哪些球队将无法跟上压力？我们旨在解决这个问题并开发解决方案。在本文中，我们提出了一个数据集，该数据集包含过去六年中欧洲冠军联赛比赛的详细信息。我们还提出了一个基于自动编码器的新型机器学习管道，该管道可以提出一个有关本赛季其余时间如何播放的故事。
Sports data has become widely available in the recent past. With the improvement of machine learning techniques, there have been attempts to use sports data to analyze not only the outcome of individual games but also to improve insights and strategies. The outbreak of COVID-19 has interrupted sports leagues globally, giving rise to increasing questions and speculations about the outcome of this season's leagues. What if the season was not interrupted and concluded normally? Which teams would end up winning trophies? Which players would perform the best? Which team would end their season on a high and which teams would fail to keep up with the pressure? We aim to tackle this problem and develop a solution. In this paper, we proposeUCLData, which is a dataset containing detailed information of UEFA Champions League games played over the past six years. We also propose a novel autoencoder based machine learning pipeline that can come up with a story on how the rest of the season will pan out.
