Computing Science and Mathematics, University of Stirling, Stirling, United Kindom.
PLoS One. 2023 Apr 5;18(4):e0282295. doi: 10.1371/journal.pone.0282295. eCollection 2023.
Recently, football has seen the creation of various novel, ubiquitous metrics used throughout clubs' analytics departments. These can influence many of their day-to-day operations ranging from financial decisions on player transfers, to evaluation of team performance. At the forefront of this scientific movement is the metric expected goals, a measure which allows analysts to quantify how likely a given shot is to result in a goal however, xG models have not until this point considered using important features, e.g., player/team ability and psychological effects, and is not widely trusted by everyone in the wider football community. This study aims to solve both these issues through the implementation of machine learning techniques by, modelling expected goals values using previously untested features and comparing the predictive ability of traditional statistics against this newly developed metric. Error values from the expected goals models built in this work were shown to be competitive with optimal values from other papers, and some of the features added in this study were revealed to have a significant impact on expected goals model outputs. Secondly, not only was expected goals found to be a superior predictor of a football team's future success when compared to traditional statistics, but also our results outperformed those collected from an industry leader in the same area.
最近，足球界出现了各种新颖的、无处不在的指标，这些指标在俱乐部的分析部门中得到了广泛应用。这些指标可以影响到俱乐部的日常运营，包括球员转会的财务决策，以及球队表现的评估。在这一科学运动的前沿，预期进球是一种衡量标准，它可以让分析师量化给定射门进球的可能性。然而，xG 模型直到现在才考虑到球员/球队能力和心理效应等重要特征，并且在更广泛的足球界并不被所有人所信任。本研究旨在通过机器学习技术的实施来解决这两个问题，方法是使用以前未经过测试的特征来建立预期进球模型，并将传统统计数据与新开发的指标进行预测能力比较。本研究中构建的预期进球模型的误差值与其他论文中的最优值具有竞争力，并且研究中添加的一些特征对预期进球模型的输出有显著影响。其次，与传统统计数据相比，预期进球不仅被发现是预测一支足球队未来成功的更好指标，而且我们的结果也优于同一领域的行业领导者所收集的数据。
