What Is Football Analytics and How Can You Use It?

Football analytics is all about using data and statistics to better understand the game. It helps staff, players, and even fans make smarter decisions by analyzing player performance, team strategies, and game trends. Let’s break it down into simple terms and show how you can use it.

What Is Football Analytics?

Football analytics is much easier than what it sounds - it means looking at numbers and patterns in the game to figure out what works and what doesn’t. Instead of just guessing.

It is the process of collecting, analyzing, and interpreting data to improve decision-making

Which involves using statistical methods, machine learning algorithms, and data visualization techniques to study various aspects of the game. The ultimate goal is to enhance performance, optimize strategies, and gain a competitive edge. = Winning more games.

Our process at PlaymakerAI.

Football analytics, machine learning, algorithms, statisctical methods?

Does it sound complicated? Scary perhaps?

There’s no need to worry. At PlaymakerAI, we’ve already done all the hard work for you. Our tools are designed to make analytics easy, simple, and actionable. For everyone who works in the football industry.

No Need to Be a Tech Expert or engineer

You don’t have to understand coding, algorithms, or complex math. We transform raw data into easy-to-understand visualizations.

You should focus on the Game, Not the Data

We use advanced machine learning and algorithms in the background to analyze massive amounts of data. But you don’t need to worry about how it works. With PlaymakerAI, you only see the results that matter the most to you, so you can focus on improving your game or strategy.

Instead of overwhelming spreadsheets or pre-determined KPI´s (that many other providers offer), you’ll get intuitive dashboards that show exactly what YOU want to look at. Totally customizable.
Here’s the short answer:

Every club is different and has different philosophies and strategies.

Our mission is to make football analytics accessible and practical for everyone.

The Basics 

To get started, it’s important to understand key concepts like Expected Points (xP) and Goals (xG), event data, and aggregated data.
Here’s a breakdown:

You may have came across with the KPI Expected Points (xP). But what is it? And how is it calculated?

The basic idea is to give an estimation of the probability to win a game if you would play it a lot of times, removing the randomness a single game introduces. You could win a game scoring one goal from one single opportunity while the opponent has 10 opportunities, scoring no goals. If you played that game 100 times, the opponent most likely would have won most times. This is what Expected Points tries to describe. We can call it the underlying performance of the teams.

Now, most models calculating xP adresses the chances and nothing else. So basically xP will be calculated based on xG of a game. The higher xG you have in relation to the opponent, the higher xP you'll have. And there is nothing wrong with this. xG is a very good descriptor of game dominance.

What is xG?

Expected Goals (xG) is a metric that estimates the likelihood of a shot resulting in a goal.

For example, a close-range shot will have a higher xG (chance of scoring), while a long-range attempt will have a lower xG.

Well, one of the most common objections to xG in general is that it focuses only on shots, so if you dominate the game possession wise or by generating more threat (xT) - that wont be considered in xG.

Is there any real reason for not adding more underlying numbers to an xP model?

Well the simple answer to this would be to look at the correlation between points and possession and xT:

The chart shows different types of football events (e.g., passes, carries, interceptions, etc.) and their contribution to the overall Expected Threat (xT) value in a game.

• Shots account for just 2% of all events in football data.

• Events like passes (76%) and carries make up the majority of meaningful actions leading to goal-scoring opportunities (xT).

If we focus only on shots (as xG does), we ignore 98% of other critical actions that create or prevent scoring chances.

Value of Non-Shot Actions: Metrics like xT quantify how likely a specific action (e.g., a pass or dribble) is to increase the chance of scoring. By including these, we can better assess a team’s true performance.

What is Event Data?

Event data tracks every action that occurs during a match, such as: Information about who performed the action, where it happened, and its outcome.

How Event Data is Used:

• Visualizing how players connect and where the ball moves.

• Understanding how and where a team disrupts the opponent’s play.

• Tracking which players successfully beat their opponents.

Event data gives a detailed, moment-by-moment picture of the game.

In our platform you can choose only to look at event data or aggregated data.

What is Aggregated Data?

Aggregated data combines multiple events into summary statistics or patterns. Instead of looking at individual passes or tackles, aggregated data shows trends over time.

Why Aggregated Data is Important:

• Provides a big-picture view of performance over multiple games.

• Identifies long-term trends and patterns in a team’s style of play.

• Tracks a player’s consistency across different matches.

Keep it simple - Win more games

At PlaymakerAI, we process all this data for you, turning it into simple visualizations and actionable insights. You don’t need to dive into the details of xG formulas or track every event manually – we’ve done the hard work so you can focus on improving performance.

By turning complex data into actionable insights, we make it easier to see the full story behind a game or a player.