  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.
