The average position displayed on a pass map is not where a player stood. It is where they passed from, and that distinction changes how analysts read the entire network.
By David Findlay, Founder of KiqIQ.
Quick Answer: A football pass map is a data visualisation that plots each player as a node at their average passing location on the pitch, with connecting lines showing the volume of successful passes between player pairs during a defined period of play.
Definition: In football analytics, a pass map (also called a passing network) applies network theory to event data: nodes represent players, node size reflects the number of successful passes completed, and edge thickness reflects the total successful passes between each specific pair.
Key point: The node position is calculated from the average x and y coordinates of every pass made by that player during the time window analysed, not their general playing position.
How a Pass Map Is Constructed
Pass maps are built from event data. Data providers such as Opta and StatsBomb capture precise pitch coordinates for every pass, recording where the ball was played from and where it was received.
Once the raw event data is collected, analysts calculate the average pass origin for each player across a match or specific phase of play. That average location becomes the node position on the pitch graphic. The number of successful passes completed by each player determines the node size: a larger node signals higher passing involvement.
The lines connecting nodes, called edges, show the passing relationships between player pairs. A thicker edge means more successful passes between those two players during the period analysed.
In academic research, Peña and Touchette formalised this approach in 2012, applying network theory to passing data from the FIFA 2010 World Cup and demonstrating that passing structures could be compared mathematically across teams.
6 Visual Elements Every Analyst Reads
The six elements analysts extract from a standard pass map are:
- Node position: the area of the pitch from which each player drives passing play during possession phases.
- Node size: passing volume, showing who circulates the ball most within the team structure.
- Edge thickness: the strength of the passing relationship between two specific players, based on completed pass totals.
- Edge direction: which player initiates the pass and which receives it, useful for identifying distribution preferences.
- Positional gaps: areas of the pitch with no node activity, indicating zones the team does not occupy or use in possession.
- Cluster density: groups of tightly connected nodes indicate the team’s primary ball-circulation axis, whether through a defensive line, midfield pivot, or wide channel.
The xGChain Layer
StatsBomb introduced an additional dimension to passing networks through their xGChain metric, which assigns value to every player involved in a possession that ends in a shot.
Rather than measuring passing volume alone, xGChain colours the nodes and edges according to their contribution to dangerous possession phases. A player who completes modest passing volumes but is consistently involved in high-threat moves will display differently from a player who circulates the ball frequently across low-risk areas of the pitch.
This distinction matters in pre-match preparation. A defensive midfielder who shows a small node on a standard pass map but a high xGChain value is contributing threat that volume metrics alone would not reveal.
How Analysts Use Pass Maps Before a Match
StatsBomb’s analysis framework outlines several practical applications of passing networks in pre-match preparation. Analysts review pass maps from recent opposition matches to identify which players drive attacking intent from their average positions, to assess how fullbacks position in possession, to locate pressing triggers based on where specific players consistently receive the ball, and to study how centre-backs distribute under pressure.
Pass maps are also used to detect lopsided attacking patterns. If the opposition’s edge weight consistently favours one flank, defensive setups can be adjusted to overload that channel before the match begins.
Key Limitations Analysts Account For
Pass maps carry structural limitations that experienced analysts recognise before drawing conclusions.
Average position distorts reality. A player who starts in one half of the pitch and finishes in another due to tactical switches will show a central average position that does not accurately reflect either phase.
The visualisation is on-ball only. Off-ball movement, pressing intensity, and defensive positioning are invisible in a pass map. It records where passes were made from, not where runs were made to.
Substitutions create visual clutter. Most pass maps stop at the first substitution. Adding a twelfth node to an eleven-player network makes relationships difficult to isolate, so many analysts produce separate maps for each phase.
Formation complexity is compressed. Inverted wingers, overlapping centre-backs, and rotational midfields all generate average positions that can appear static when the actual tactical structure was fluid.
Free Tools and Data Sources Analysts Use
FBref, powered by Opta data from Stats Perform, provides free passing statistics for hundreds of leagues, including progressive passes, completion rates, and advanced passing metrics at player level.
StatsBomb offers free open-data event files via GitHub for select competitions, including detailed pass coordinates recorded at the exact pitch location of each action. These files are the primary data source for analysts building pass maps in Python.
mplsoccer is a Python library built by Andrew Rowlinson that allows analysts to plot pass maps onto scaled pitch graphics using Matplotlib. It includes built-in support for StatsBomb open data formats and is the most widely used open-source library for this type of visualisation.
Frequently Asked Questions
What is a football pass map?
A football pass map is a visualisation that plots each player as a node at their average passing location, with connecting lines showing the volume and direction of successful passes between player pairs during a match or phase of play.
What does node size mean on a pass map?
Node size reflects the number of successful passes completed by that player during the period analysed. A larger node signals higher passing involvement within the team’s structure.
How do analysts use pass maps before a match?
Analysts review recent opposition pass maps to identify which players drive possession, how fullbacks position in build-up, where pressing can be triggered, and which passing combinations generate the most attacking threat.
What is a passing network in football?
A passing network is another term for a pass map. Both refer to the same visualisation: a directed graph in which players are nodes and passing relationships are edges, built from event-level pitch coordinate data.
What tool do analysts use to build pass maps?
The most widely used open-source tool is mplsoccer, a Python library by Andrew Rowlinson. Analysts use it with event data from StatsBomb’s open dataset or Opta-sourced data from FBref.
Can pass maps be used for tactical analysis?
Yes. Pass maps identify a team’s primary ball-circulation axes, key connectors in possession, structural weaknesses, and zones where passing volume is concentrated. They are used in both pre-match preparation and post-match review.
Sources
- StatsBomb Blog Archive: Explaining xGChain Passing Networks
- Peña, J.L. and Touchette, H. (2012): A Network Theory Analysis of Football Strategies
- American Soccer Analysis: How to Use Passing Networks in Soccer
- FBref: Football Statistics and History, powered by Opta / Stats Perform
- mplsoccer Documentation: mplsoccer Python Library by Andrew Rowlinson