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:足球是一个非常由结果驱动的行业, 其目标比大多数运动中少见, 因此有更远的参数来判断球队和个人的性能是关键。 期望目标( xG) 提供了更深入的洞察力, 而不是一个得分线。 为解决对足球进行进一步分析的需要, 本文使用了机器学习应用程序, 并应用于足球事件数据。 从这个概念, 产生了一个二进制分类问题, 由此产生了一种概率性能估值, 使用后勤递减和加速推力方法进行输出。 模型成功地预测了足球运动员以15, 575 射击为基础的xGs 概率值。 拟议的解决方案使用StatsBomb作为数据提供者和一个行业基准, 以调整模型方向正确方向。 为解决对足球进行进一步分析的需要, 拟议的MLE 解决方案使用了对足球应用的机器学习应用程序应用程序应用程序, 用于解决古老的老老老的老老老的老俗说词: “ 球已经落到错误的人' 。 模型的开发被用来调整并获得比一般模型显示的更现实的预期目标值。 。 。 为了通过实现这个目标, 方向, 定位的论文将定位调整 xG, 将培训数据转换为更接近的轨道 水平, 将培训数据转换为 方向, 水平 以更精确的 方向的 方向 方向 向更精确的数值 方向定位 。