Use qualified teams performances during the group stage and round of 16 to predict the probability of winning during the next stage.
I collected the data from https://www.foxsports.com/soccer/fifa-world-cup/stats After careful consideration of which features to use I decided on the features in the table below to build my machine learning model (Some of my past soccer experience played a role on deciding which data is more important) In my prior prediction model that I used to predict the round-of-16 match winners I used decision tree regression model to calculate each team chance of winning. My model predicted 7 winners out of 8 right! For the quarterfinals I experimented with few algorithms in Microsoft Azure Machine Learning Studio and I decided on using Two-Class Neural Network model to predict the chance of winning for each team. Classification using neural networks is a supervised learning method, and therefore requires a tagged dataset, which includes a label column. For example, you could use this neural network model to predict binary outcomes such as whether or not a patient has a certain disease, or whether a machine is likely to fail within a specified window of time.