论文标题
转移门户：准确预测球员转移在足球中的影响
Transfer Portal: Accurately Forecasting the Impact of a Player Transfer in Soccer
论文作者
论文摘要
足球中最重要，最具挑战性的问题之一是预测未来的球员在不同联盟内部和之间的另一个俱乐部时的表现。除了成为团队做出的最有价值的预测外，它也是需要考虑的最复杂的分析任务：a）球员目前的团队和目标团队之间的比赛风格差异，b）每个团队其他球员的风格和能力差异，c）c）联赛质量和风格的差异，以及d）d）球员的角色是球员的作用。在本文中，我们提出了一种解决这些问题的方法，并使我们能够对未来的绩效进行准确的预测。我们的转移门户模型利用个性化的神经网络会计，为球员，团队和联赛的风格和能力水平输入表示形式，以模拟任何所选俱乐部的未来球员绩效。此外，我们使用贝叶斯更新框架随着时间的推移动态修改玩家和团队表示，这使我们能够为带有少量数据的后起之秀生成预测。
One of the most important and challenging problems in football is predicting future player performance when transferred to another club within and between different leagues. In addition to being the most valuable prediction a team makes, it is also the most complex analytics task to perform as it needs to take into consideration: a) differences in playing style between the player's current team and target team, b) differences in style and ability of other players on each team, c) differences in league quality and style, and d) the role the player is desired to play. In this paper, we present a method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club. Furthermore, we use a Bayesian updating framework to dynamically modify player and team representations over time which enables us to generate predictions for rising stars with small amounts of data.
