Football is a very result-driven industry, with goals being rarer than in most sports, so having further parameters to judge the performance of teams and individuals is key. Expected Goals (xG) allow further insight than just a scoreline. To tackle the need for further analysis in football, this paper uses machine learning applications that are developed and applied to Football Event data. From the concept, a Binary Classification problem is created whereby a probabilistic valuation is outputted using Logistic Regression and Gradient Boosting based approaches. The model successfully predicts xGs probability values for football players based on 15,575 shots. The proposed solution utilises StatsBomb as the data provider and an industry benchmark to tune the models in the right direction. The proposed ML solution for xG is further used to tackle the age-old cliche of: 'the ball has fallen to the wrong guy there'. The development of the model is used to adjust and gain more realistic values of expected goals than the general models show. To achieve this, this paper tackles Positional Adjusted xG, splitting the training data into Forward, Midfield, and Defence with the aim of providing insight into player qualities based on their positional sub-group. Positional Adjusted xG successfully predicts and proves that more attacking players are better at accumulating xG. The highest value belonged to Forwards followed by Midfielders and Defenders. Finally, this study has further developments into Player Adjusted xG with the aim of proving that Messi is statistically at a higher efficiency level than the average footballer. This is achieved by using Messi subset samples to quantify his qualities in comparison to the average xG models finding that Messi xG performs 347 xG higher than the general model outcome.

翻译：足球是一项结果导向极强的运动，进球比大多数体育项目更为罕见，因此需要更多参数来评估球队和个人的表现。预期进球（xG）能够提供比单纯比分更深入的分析视角。为满足足球领域进一步分析的需求，本文采用机器学习方法处理足球事件数据。基于此概念，构建了一个二元分类问题，通过逻辑回归和梯度提升方法输出概率估值。模型基于15,575次射门成功预测了足球运动员的xG概率值。该方案以StatsBomb为数据源，并以行业基准为导向调整模型。提出的xG机器学习解决方案进一步用于解决"球落到了错误球员脚下"这一经典迷思。通过模型开发，本文调整并获得了比常规模型更真实的预期进球值。为此，本文提出位置调整xG（Positional Adjusted xG），将训练数据按前锋、中场和后卫拆分，旨在根据球员位置子群揭示其能力特征。位置调整xG成功预测并证实：进攻型球员更擅长积累xG，其数值由高到低依次为前锋、中场和后卫。最后，本研究进一步发展为球员调整xG（Player Adjusted xG），旨在证明梅西在统计层面上具有高于普通足球运动员的效率水平。通过使用梅西子集样本量化其能力，并与普通xG模型对比，发现梅西的xG值比常规模型结果高出347个xG。