Space control and occupation metrics with average positions
Space and zonal control. These words and concepts are used quite often when we talk about football in a tactical sense. How do we control the spaces, zones and areas on the pitch and ensure we dominate in the game? We can capture These very interesting questions on video with telestration programs.
However, how do we make sure that we can capture this with data? The most obvious solution to that is to have a look at tracking data — and believe you me, I should write about tracking data more often — but not everyone has the opportunity to use tracking data. Furthermore, out-of-possession data is not as prevalent as we would like.
In this article, I want to use on-ball event data to create new metrics for space control and space occupation. I will do that by focusing on average positions of players while on the ball during a specific game or season.
Data
The data I’m using for this research comes from event data that is raw. That comes from Opta/StatsPerform, but this kind of data can be used from Hudl/StatsBomb, SkillCorner or any other provider that has event data.
The data was collected on Friday 14 February 2025 and focuses on Ligue 1 2024–2025. While the metrics can be created on an individual player level, I will keep my attention on the team level as it can give us some interesting insights.
Methodology
As we want to look at space control and we want to look at on-ball data, we need to make sure that we have a methodology that works for this. I honestly had to find a way that would work the best, or fail the least. First I started with looking at bins.
Yes, I’m very much aware this bin doesn’t overlay the pitch properly. However, this shows the zone control from Team A (blues) and Team B (reds). This control is based on all x and y variables and handles all on-ball touches, which might not necessarily mean that all variables are relevant.
Then I moved over to visualising average player positions.
Still, I wasn’t very convinced with how this visual looked and how it gave me control or occupation of space. There are two reasons for that:
- Some areas are more purple, but dont’ really show if that’s a mixed-control zone or not
- This plots all average positions for all players featured in a match. All players are needed for the total control, but without making a distinction for substitutions, it could lead to overcrowding and misleading data.
I like the idea of average positions though and I kept going back to a post I earlier wrote about Off-Ball Impact Score (OBIS)
In passing networks, the average positions of the network are calculated at the beginlocation of a pass: where does the pass begin. In terms of that, they calculate average positions where passes are made in that specific match or season. Also it makes sure that it focuses until the first substitution.
It gives us a good idea of where passes were made during that specific game on an average for specific players, as you can see in the image below.
What if you used that logic of passing networks and calculate new things from those networks? Of course, we have done that already a little with OBIS:
- In-degree centrality: The total weight of incoming edges to a player (i.e., the number of passes they received).
- Out-degree centrality: The total weight of outgoing edges from a player (i.e., the number of passes they made).
- Betweenness centrality: Measures how often a player lies on the shortest path between other players in the network.
- Closeness centrality: The average shortest path from a player to all other players in the network.
- Eigenvector centrality: Measures the influence of a player in the network, taking into account not just the number of connections they have but also the importance of the players they are connected to
- Clustering coefficient: Measures the tendency of a player to be part of passing triangles or localized groups (i.e., whether their connections form closed loops).
These measure many things, but I want to focus more on control and occupation than pure off-ball impact.
In addition to the already calculated metrics, I wanted to pose some new metrics which we can calculate with average positions based on the begin location of a pass:
- Team Control (%): The percentage of the field controlled by each team.
- Overlap (%): The percentage of the field that is controlled by both teams.
- Convex Hull Area: The area of the convex hull for the team (shows how compact the team is).
- Vertical Compactness: The range (peak-to-peak) of player positions in the y (vertical) direction.
- Horizontal Compactness: The range (peak-to-peak) of player positions in the x (horizontal) direction.
- Player Density: The average number of players per unit area on the field.
- Centroid X and Y: The average position (center of mass) of the team’s players.
- Horizontal Spread: The maximum distance between players in the horizontal direction.
- Vertical Spread: The maximum distance between players in the vertical direction.
- Circularity: A measure of the team’s shape, with 1 being a perfect circle (indicating high compactness).
With these new metrics, we can create some new insights into space control and occupation in individual games, seasons and for individual players’ analysis.
Analysis
When we continue with our metrics, we can look at it from two ways. The first one is to look at it from an individual game perspective:
I have had a look at the game between PSG and Monaco, after which I calculated these metrics for both teams.
- PSG had 40,06% control of the pitch, while Monaco had 40,85% control of the pitch
- Overlap is the same in percentages
- PSG had Convex Hull Arae of 2261,02 and Monaco of 1814,81. This means that Monaco had a smaller playing field than PSG in the game.
- Vertical and Horizontal compactness, we see that PSV is more compact in a vertical sense, while Monaco is more compact in a horizontal sense
- In terms of player density, there are more players per area for PSG than for Monaco
- In terms of circularity, the high compactness is higher for PSG than for Monaco.
This is for a single game, but we can also look at all teams and focus on a complete season. We can compare them in serval ways, but the first one is via percentile ranks:
Here you can see how PSG scores in terms of all the metrics we just calculated compared to the rest of Ligue 1. what’s interesting is how high they score in player density and horizontal spread.
And this is Monaco’s percentile rank calculated. While PSG has more outliers in the high and low regions, Monaco is much more steady and consistently scores above average for every metric. What’s interesting here is that they score highest for percentages of pitch control during the games they played.
Final thoughts
Integrating average position metrics with passing data offers a deeper understanding of a team’s playing style and tactical approach. By mapping the average positions of players during a match, it becomes easier to identify areas of strength and vulnerability in possession. Teams that focus on short, quick passes tend to have more compact positioning with high-density zones in central areas, promoting controlled build-up play.
However, it does feel unfinished or incomplete. This is because we only look at the locations of passes and there are so many more types of touches to be considered. That’s something to alter for version 2.0.
