Soccer Heat Map | How It Is Generated In Football? (Find Out How Soccer Heat Maps Are Generated!)

Last Updated on October 23, 2023 by Alex PT

A soccer heat map is generated using GPS technology, tracking a player’s position up to 10 times per second, producing data like distance covered, sprints, and top speed. This data aids coaches and analysts in strategy and performance assessment.

The Table Below Contains The Steps And Software Used For Generating A Heat Map. 

Software Used For Creating A Heat Map	Steps In Generating A Soccer Heat Map
Python Software	Getting the data
Matplotblib Software	Drawing a football pitch
Seaborn Software	Leveling up your visualization

What Is A Soccer Heat Map?

A soccer heat map is a graphical representation that displays a player’s movement and positioning on the field during a soccer match. It provides in-depth insights into a player’s performance by illustrating where they spent the most time during the game.

Here’s how a soccer heat map is generated and what it can reveal:

1. Data Collection: To create a heat map, advanced tracking technologies like GPS or computer vision systems are used. GPS trackers, worn by the players, record their positions multiple times per second.
2. Position Data: The tracking devices collect data on the player’s coordinates, speed, distance covered, and other relevant metrics. This data is usually collected for the entire duration of the match.
3. Visualization: Software programs process this data to create a visual representation of the player’s movements. Typically, a heat map uses colors to show where a player was most active. Hotter colors like red or orange represent areas where the player spent more time, while cooler colors like blue or green indicate less activity.
4. Analysis: Coaches, analysts, and team strategists can use these heat maps to analyze a player’s positioning, work rate, defensive coverage, attacking patterns, and more. It provides insights into whether a player is adhering to the team’s tactical plan and helps make data-driven decisions.
5. Strategic Adjustments: Heat maps help teams adapt their tactics. For example, if a forward spends too much time in a deep defensive position, the coach might instruct them to stay higher up the field.
6. Player Development: Individual players can also benefit from heat maps. They can assess their performance, identify areas for improvement, and tailor their training accordingly.
7. Team Performance: Heat maps can be used to evaluate the overall performance of a team. By overlaying multiple player heat maps, coaches can assess team shape, spacing, and passing patterns.

What Is The Importance Of Soccer Heat Maps In Football?

The main importance of a soccer heat map is to find out the frequency of occurrences spread in a given particular area. That is, it helps you know the particular areas every particular player can cover in a football match. This helps you know the ability of every player and the best positions for each player to be placed.

What Software Is Used To Create A Heat Map?

Creating a heat map in soccer or any other sport typically involves specialized software that can process and visualize the tracking data effectively. Here are some of the software and tools commonly used to create heat maps:

1. Sports Performance Analysis Software:
   • Catapult Sports: Catapult is a leading provider of GPS and wearable technology for sports. Their software platform, such as OpenField, allows teams to analyze player data, including heat maps, and integrate it with video analysis.
   • Stats Perform: Stats Perform offers a range of sports analytics solutions, including Sportscode and SportsVU, which can generate heat maps and other performance visualizations.
   • Hudl: Hudl’s sports analysis software enables teams to create heat maps by combining tracking data with video analysis, making it a popular choice for coaches and analysts.
2. GPS Tracking Providers:
   • Many teams and organizations work directly with GPS tracking device providers like STATSports, Polar Team Pro, or GPSPORTS to access their software and analytics platforms, which often include heat map functionality.
3. Video Analysis Software:
   • Sportscode by Hudl: Hudl’s Sportscode is often used for video analysis, and analysts can manually input tracking data to generate heat maps alongside video footage.
   • NacSport: NacSport is a video analysis software that allows users to create heat maps by tagging and analyzing video clips.
4. Data Visualization Tools:
   • Data visualization software like Tableau, QlikView, or Microsoft Power BI can be used to create custom heat maps from tracking data. These tools are highly customizable but may require more technical expertise.
5. Specialized Sports Analytics Software:
   • Some sports-specific analytics software like FieldWiz, which specializes in soccer, offers features for creating heat maps and other performance visualizations.
6. Open-Source Tools:
   • For those with coding and data analysis skills, open-source tools like Python (with libraries like Matplotlib or Seaborn) and R can be used to create custom heat maps from tracking data.
7. Web-Based Platforms:
   • There are web-based platforms like PlayerTek that offer cloud-based solutions for generating heat maps. These are often user-friendly and accessible via web browsers.

What Are The Steps Used In Generating A Soccer Heat Map?

1. Getting the data

2. Drawing a football pitch

3. Leveling up your visualization with a pass map and then a heat map.

These are the three steps for creating a pass map. It’s not just enough to list out the steps. The steps will be explained below and while explaining, there would be football references to mar it interesting.

1. Getting The Data

This is normally the hardest part. Collecting data summary of a sporting event like the goals in a football match or the throws in a basketball game aren’t much trouble. It is a lot harder to get the detailed data set of a football match, or any other sports event. Getting detailed play-by-play data in a football game involves tracking the players on the field, mostly for high tempo. 

In every football game, there are always three people who are using a live video feed on a pitch visual. One of them watches the home team, the other one watches the away team, and the third person is the data checker. 

Lately, statsbomb, our unsung hero made a public release of the play-by-play second accurate datasets of every game across the three recent football leagues: the national women soccer league (US)- (United States), The FA- (Football Association) women’s super league (England), and the 2018 FIFA- (Federation Internationale de Football Association) World Cup.

The statsbomb data sets are all in JSON format. You will need to parse the raw dataset to a relational setup that can effortlessly be recouped and altered. The JSON is an open standard file configuration and data exchangeable format that uses human-readable content to store and communicate data objects comprising value items and array data classifications.

JSON normalize is an effective tool, as it can automatically normalize JSON  format into a relational structure. 

2. Draw A Football Pitch

Now, let’s use matplotlib to draw a straightforward football pitch. It does seem like a lot but we have to unload the draw-pitch function line by line. The function takes in an ax argument which is the product of the add-subplot function in matplotlib. It then puts in various items with pre-defined dimension to duplicate an image of a football pitch. Including the center circle, penalty areas, the 6-yard boxes, and the arcs in the pitch. 

3. Level Up Your Visualization With A Pass Map And A Heat Map

You have to start by creating a pass map. After completing the first two steps, creating a pass map won’t be any trouble. Firstly, we have to load the JSON file and do some fundamental data cleaning in panda that only consists of the passing events of the particular player. 

The next step is to track active zones with a heat map. You have to plot the heat map using seaborn on top of matplotlib to picture the involvement of the player during the game. 

The syntax of the code here is extremely simple. You use a kdeplot which will draw a core density estimation of the dispersion points of the player’s location. After creating your heat map and you feel you can do better. A good way to do better is by joining the pitch, the pass map, and the heat map that you created in order to have a more detailed view of the player’s performance during the game.

Closing Thoughts – Rounding It Up! 

Generating heat maps is extremely difficult, but it gets easier every year as new technology comes out. Following these steps carefully, you can get your heat map without many complications. And thanks to Python for the software they have created to make heat maps less complicated.

References:

https://blog.tryoliver.com/en/heat-maps-oliver
https://ibaworldtour.com/how-football-heat-maps-work/