Quantifying Off-Ball Contributions in Football Using Network Analysis: The Off-Ball Impact Score (OBIS)
This might be my best and scariest project up to date. Scary because it can be full of flaws, but also best because I feel this will change something in the way we approach passing networks. Not that I think I will innovate the analytics space, but because I have been trying to find a way to create something meaningful from passing networks away from the aesthetics on social media. Because, I’m a firm believer we can create something meaningful from it and gather valuable information, you just need to know where to look and what the aim is.
In this article, I will show you a way that you can use to create off-ball value from passing networks by creating metrics from it and then going to an analysis that will lead to calculations for an impact store. That sounds very vague, but it will become more clear — I sure hope so at least lol — at the end of this article. It will have some logical steps to ensure it remains transparent at all times.
Why this development in passing network analysis?
I eluded to the fact a little bit, but the reason for this analysis is predominantly selfish. I wanted to see if I could create something meaningful from passing networks and test myself to create an off-ball value from event data. The reason for that is that I believe we have incredible data on how valuable possession/actions are with the ball, but far too few without the ball.
The next step for me is to show that with an out of the box thinking, there can open a world that offers much more metrics and paths for data analysis, beyond the aesthetically pleasing passing networks we have seen on social media — which I’m guilty of as well — which don’t add a whole lot. So, I wanted to challenge myself and see what we can extract from the passing network interconnectivity and calculations to develop new metrics and work with that.
Data collection and representation
The data used for this project comes from Opta/Statsperform and was collected on Saturday 14th of December 2024. All of the data is raw event data and from that XY-data, all the metrics have been developed, plotted, manipulated and calculated.
The data is from the Eredivisie 2024–2025 season and both contains match-level data as well as season-level data. There aren’t any filters used for value, but this is something that can be done in the next implementation of the score, as I will explain further on in this article.
There are different providers out there offering the XY-data, but I am sticking to Opta data for event data as all my research with event data has been done with Opta and therefore the continuity will improve the credibility of this work in line with my earlier work.
Passing networks: what are they?
American Soccer Analysis (ASA) said it quite clearly for me as follows:
“The passing network is simply a graphic that aims to describe how the players on a team were actually positioned during a match. Using event data (a documentation of every pass, shot, defensive action, etc. that took place during a game), the location of each player on the field is found by looking at the average x- and y-coordinates of the passes that person played during the match. Then, lines are drawn between players, where the thickness — and sometimes color — of each line signifies various attributes about the passes that took place between those players.
The most common and basic style of passing network simply shows these average player locations and lines between them, where the thickness of the line denotes the amount of passes completed between each set of players.”
In the image above, I’ve created a passnetwork on a pitch from the game AZ Alkmaar vs Ajax 2–1 (December 8th, 2024) and it shows AZ. Now this shows the combinations and directions of the passing combinations, including the average positions.
This is something we see a lot in articles and social media and data reports, but we want to add value to this. Sometimes this happens by any type of value: Expected Threat (xT), Expected Goal Chain, Goals added (G+) or On-Ball Value (OBV). This gives us more meaning and context about the networks, but in my opinion it’s quite limited in the way it tells us about value away from possession. These are possession-based values.
Methodology Part I: Creating metrics from passing networks
So in the passing network we have calculated the average positon of the 11 starters per team and what the passing combinations are. This is now visual, but we want to take a step back and see a few different things we can create into metrics:
- 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 are metrics that player-based and focus on how a player is in possession and out of possession. This distinction is important to us, to have an idea where to look later as we approach out of possession metrics.
These metrics are calculated in Python by analysis — using code- the relations between players in terms of passing and their average positions.
Next to player-level data, there are so data metrics that are team-based. These are the following I’ve managed to calculate:
- Network density: Measures the overall connectivity of the team, defined as the ratio of actual passes (edges) to the maximum possible connections.
- Network reciprocity: Proportion of passes that are reciprocated (player A passes to B, and B passes back to A).
- Network Assortativity: Measures if high-degree players tend to pass to other high-degree players
Analysis I: Adjust passing networks with player-receiving values
We calculate the newly developed metrics and form them into a CSV/Excel file, whatever your preference is for analysis. It will look like this:
As you can see we have the distinction between passing and receiving in general. We want to focus on betweenness, which is important: A player with high betweenness centrality is a key link in the team, acting as a bridge between other players. This highlights their importance in maintaining the flow of play.
If we look at this specific game, we can see that Clasie is the player who is most important as the key link in the team, followed by Mijnans and Penetra. It’s not weird that the two midfielders are linked so importantly, but the central defenders getting the ball so often, means something for the security and risk-averse style of the play.
We can also use any of the other metrics to illustrate how a player is doing, but you get my drift: value is there to be added.
Of course, this is on a player level, how does this translate to team-level for this specific game?
On their own these metrics mean nothing, they have to be in relation to other games that AZ has played or have to be benchmarked against the whole league. They however do tell us something about how close the average positions of the players are in relation to each other: a high density means that the team has good ball circulation and most players are connected through passes. A low density may indicate a more direct, counterattacking style with fewer connections. And, that’s something that’s valuable in the bigger picture.
Analysis II: Player comparisons
We also want to see what the relation is between passing and receiving for the key players. We will look at Betweenness and Closeness, which give value if you are closest to receive or pass the ball to the closest: are these key players equally good in both or do you find different outcomes?
If we look at the scatterplot above, we don’t see many outliers and we only see the top 10 players. However, an interesting conclusions we can draw from this is that players are more likely to score higher on passing the ball to the closest teammate, than they are to receive it from the closest teammate. Tristan Gooijer (PEC Zwolle) being the exception.
If you look one step further and compare Eigenvector centrality to Clustering Coefficient, we get some different insights. Eigenvector focuses on a key player also linking with other key players, while clustering coefficient focuses on how well a player is connected in passing triangles.
As you can see in the scatterplot above, the relations are quite different here. The most important players are more likely to be included in the passing triangles, confirming that key players will always look for each other.
Methodology Part II: Creating OBIS
Now, I want to take the next step and the last step. We want to see how we can create an off-ball value score from passing networks. We can do that as follows. We will filter the new metrics and choose those we think we will help in calculating that metric:
- In-degree centrality: A player with high in-degree centrality is frequently targeted by teammates and serves as a passing hub or focal point.
- Betweenness: A player with high betweenness centrality is a key link in the team, acting as a bridge between other players. This highlights their importance in maintaining the flow of play.
- Eigenvector: A player with high eigenvector centrality is well-connected to other influential players. They amplify their team’s passing efficiency by linking with key teammates.
To make the score, I have a formula:
We have the three metrics as described above and they all have a weight. Some metrics are more important for the score than others. In this instance, In-degree has a weight of 0.5, Betweenness a weight of 0.3 and Eigenvector a weight of 0.2.
Analysis III: Off-Ball Impact Score (OBIS)
If we look at the total season so far and the OBIS, we can see that these 15 players score highest in this metric. We want to convert this into a score from 0–100 and if we do that, we get the following scores for the top 25:
Obviously,, this is a season score and we can also look at this from a individual level. And, with that I mean the match level. How do we add value to the passing network with using OBIS.
We are looking at a different game and this time it is the SC Heerenveen — PSV game that ended 1–0 for the hosts. We will focus on PSV.
In the pitch above you can see the passing network of PSV in their game against Heerenveen, but with the nodes coloured based on the OBIS they had in this particular game. In other words, which player had the most impact in not passing, but receiving the ball.
Final thoughts
OBIS is a promising metric for evaluating player performance by combining key network-based metrics like in-degree, betweenness, and eigenvector centrality. By weighting these factors and normalizing them, OBIS provides an insightful measure of player influence on the field. However, further refinement could enhance its accuracy and adaptability. Incorporating additional metrics (pass completion, defensive actions) and considering context-dependent factors (game state, opponent strength) would improve OBIS’s ability to reflect a player’s true impact. Additionally, using machine learning to fine-tune weightings and integrate spatial data could offer a more nuanced, dynamic representation of player performance.
