What Is Needed To Learn How To Analyze Soccer Statistics?

Those who are interested in soccer always ask questions about the necessary skills. For many, soccer analytics is the ultimate dream. If sports analytics and statistics are impressive, then it is logical that such a question arises.

 You should start with the quality and acceptance of the collection of source data. A decade ago, data coverage was limited by statistics on goals, shots, number of corners, possession of the ball, etc. It is not bad, but this is ordinary statistics that can be watched on TV; in itself, it does not help predict the victory in games.

Not so long ago, the process of collecting information about the behavior of the ball began. The largest provider of this data, Opta, offers (x, y) the coordinates of each ball pass, each defensive action, and each attack. Opta is currently one of the data providers, including Statsbomb, Wyscout, and some bookmakers who collect data. For many soccer clubs, this information was useful, for example, to search for players. For sports statistics, it is possible to measure the immediate expected tasks of players during the match by collecting the expected transfers, passing models, and interception chains.

 To thoroughly analyze statistics, you need to have programming skills, better in Python, and also understand the basics of statistical modeling. A fundamental goal is a logistic regression model. Pass-through models involve either logistic regression or primary neural networks.

Of course, it is essential to know about the history of the ball moving on the soccer field, but the future of soccer analytics may be different. Some experts, for example, Raul Pelaez Blanco, believe that you need to understand how players act in different contexts on the soccer field, that is, to have data on 22 players on the ground in real-time.

To cope with this task, you need to use computer vision and machine learning to determine the positions of players and the ball. But even here, algorithms can make mistakes. But, despite this, the available data is already enough to generate analytics.

Anyone who wants to engage in soccer analytics should think openly and broadly. Data science and statistics play an essential role, but there is a place for those who have a good understanding of physics, computer vision, or parallel computing. It is necessary to develop appropriate skills to achieve the goal.

And the last thing that is important to remember is that you need to be a team player because it is essential to learn and share everything that he learns.

Therefore, regardless of the way the data is analyzed, it should be open to others. You need to communicate with others, learn, and share your knowledge. All this will lay the foundation for soccer analytics of the future.