Classic sport match prediction
When making decisions about which data to use in a model you have to be aware of several pitfalls. One of them is information leakage i.e. including data that contains information that you shouldn't have at the time of prediction.
Both Duration and Goals are data points that you do not have at the time of prediction (that is before a match) and therefore should not be used in a prediction model.
This leaves us with only one information point: the teams involved. You could still make a ML model but it would only tell you something trivial: which of these teams won more head 2 head games in the past because that is the only information it would use to determine the likely winner (therefore the model would also always predict the same outcome because the input does not change).
This means you need much more data to actually make a model. Modern models for predicting the outcomes of sport events use data such as recent performance of the teams, time (e.g. time of day, weekday, season, etc.), recent performance of all involved players, context (e.g. weather, etc.).
It does not seem like your dataset contains this information so I am afraid you will be unable to create a model.
I would recommend to start with a different problem that has a better dataset available and is easier to crack for a beginner. The IRIS dataset is a wellknown beginner ML problem and teaches you how to model a classification algorithm.