Author: Lennon Au-Yeung
This project was created in fulfillment of the blog post requirements for DSCI 542 (Communication and Argumentation), a course in the Master of Data Science program at the University of British Columbia. This project has been further modeified and extended from the original blog post. The link of the blog post can be found here
Expected goals(xG) have been introduced in recent years in a variety of sports such as soccer and hockey for sports analysts to evaluate how ‘dangerous’ the shot is using data. The main goal of expected goals is to be a statistic that allows us to evaluate the quality of the scoring opportunity. Using the expected goals model, we aim to assign a decimal value to each of the shots between 0 to 1 which represents the likelihood of that shot resulting in a goal, with 0 representing that there is no chance the shot will result in a goal and 1 being the shot will definitely result in a goal.
Prelimary EDA has been done before modelling, the results are shown below.
This is the distribution in shot location, we can see the most shots are from near the net.
This shows the shot angle distribution for goals and no goals, we can see that a large portion of goals come from perpendicular to the net.
This shows the difference in distribution for goals and no goals of different factors. From the shot distance graph, we can see that goals are more concentratd in being close to the net and at a perpendicular angle.
Three popular expected goals model have been used in this project, including: Logistics Regresison, Gradient Boosting Classifier and Random Forest.
Using the function from HarryShomer/xG-Model, the results was generated below as an ROC curve.
As we can see, after hyperparamter optimization, three models performed quite similar, all outperforming the model of a random choice.
The feature importance is extracted for the logistic regression model, the top 15 features in terms of magnitued is displayed below.
