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Predicting Football Match Results Using Machine Learning Techniques

Predicting Football Match Results Using Machine Learning Techniques

To analyse various aspects of a football match, it is important to make a football match prediction today. It keeps you aware of aspects like records, statistics, team's performance and more. Predicting the results of football matches is challenging because the sport is so prominent. Multiple factors play their role and, therefore, prediction becomes challenging. Machine learning technologies focus on the match's statistics for prediction.

Now let's get into more details on how this prediction is carried out through machine learning technologies:

Metrics

It is best to use accuracy as an evaluation metric. It is measured as:

Accuracy = (TP+TN)/ (TP+FN+TN+FP)

The meaning of these symbols is as follows:

Now the first step is to web-scrape the main page, choosing the football data and then making a Data frame by combining all information. The code is ready to select data from the matches. All the codes would be attached to the Git repository.

Data

It's now time to assess and evaluate the data collected. This makes it possible to prepare a prediction model for the results. To avoid confusion, let's look at the meaning of all the variables involved:

It is wise to check how these variables interrelate so that they help in Attack and Defence approaches. Within the code, a function is given that works on this approach by the team and places played (Home/Away).

When it comes to the attacking approach, four variables, including SoT, Sh, Gls and Torcida, are selected.

Now let's look at the methodology to be followed:

Methodology

For today football match prediction, the methodology involves data pre-processing. You cannot get statistics of the games before the declaration of the match's result. It is vital to create a few new variables that can be used before the game's result. For the same, it is important to create a mean for all the variables. The mean will encompass the results of all the games played before the game you are predicting.

If variables show negative values then the visiting team had noted better performances in the previous games compared to the opponent team.

At this point, the database is ready and now you can execute some models and analyse their performances.

Let's look at the execution of this method for today football match prediction:

Execution

For execution, the initial four variables are dropped and used by MinMaxScalar on every feature variable. On Sklearn, this estimator levels up and translates every feature distinctly in a way that it is within the given range across the training set. The below four models are used:

1. Support Vector Machine

This supervised learning model targets to enlarge the distance amongst the points. So, it becomes simple to categorise between the points and also classification of classes becomes easy. Therefore, Support Vector Machine (SVM) splits data in a way that it can maximise the margin amongst the classes.

2. Random Forest

Implied from the name, random forest prepares multiple decision trees and collects them into a "Forest" of trees. It takes a sample with size m bootstrap of the columns, which depict the descriptive variables when splitting the tree in every node of it. Finally, the majority vote provides the deciding vote for classification problems.

3. KNN

This model carries out classification depending on the class of its k adjacent points depending on a distance metric.

4. Logistic Regression

It is a multiple linear regression whose result is constricted within the interval [0, 1] through the sigmoid function.

The algorithm was played randomly 1,000 times for all the above 4 models. The same makes it possible to supervise the accuracy and standard deviation of all models. After playing the algorithm 1,000 times, Logistic Regression showed the best results. It also incorporated one of the lowest standard deviation rates and the maximum accuracy amongst all four models.

Concluding Note

Football prediction is a difficult task and it demands more variables to ensure effective prediction of the results. With machine learning algorithms, it is easy to determine which team bets, and also it simplifies the task of predicting the football match result. Therefore, with machine learning technologies, today's football match prediction becomes easy.



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