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：足球是一项高度以结果为导向的运动，其进球频率低于多数体育项目，因此需要更多参数来评估球队和个人的表现。预期进球（xG）能提供比单纯比分更深入的洞察。为满足足球领域进一步分析的需求，本文开发并应用了基于机器学习的方法处理足球赛事数据。基于该概念，构建了一个二分类问题，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。所提方案采用StatsBomb作为数据提供方和行业基准，以正确方向调整模型。本文进一步将xG的机器学习解决方案用于解决老生常谈的"球落到了错误的人脚下"问题，通过模型开发调整并获取比通用模型更真实的预期进球值。为此，本文提出位置调整xG，将训练数据分为前锋、中场和后卫，旨在根据球员的位置亚组提供其能力洞察。位置调整xG成功预测并证明更具进攻性的球员能积累更高xG，其中前锋数值最高，中场和后卫次之。最后，本研究进一步开发了球员调整xG，旨在证明梅西在统计上比普通足球运动员具有更高效率。通过使用梅西子集样本量化其能力，与平均xG模型对比发现，梅西xG比通用模型结果高出347个xG。