Predicting the outcome of a football match is a huge phenomenon. Many fans and analysts always predict who will win the game before the game starts. Predictions are made by calculating many variables such as home field advantage, current team score, team strength etc. Managers and club directors can use forecasts to decide what is needed to win a game. This becomes very important as football related funding increases every year.

One method that can be used to solve forecasting problems is logistic regression. Logistic regression is a classification method where the dependent variable has only 2 possible values, such as whether or not a student has graduated or someone has been diagnosed with a disease. Logistic regression models are used to predict the probability of a binary response based on one or more variables. The advantage of this method is that it is very suitable for explaining the relationship between the output variables and the input variables, and it can solve problems that cannot be solved by ordinary least squares regression, that is, the plot of the data will form an S-shaped curve that cannot be explained with linear equations. Logistic regression is an easy to implement and easy to understand method to apply to prediction problem, moreover it gives us additional insight through the estimated coefficients, but it needs a set of variables that can improve performance of this work.