Football transfers

Football transfers

This article is a part of a series intended to show how to use Memgraph on real-world data to retrieve some interesting and useful information.


Football is a word that could mean one of several sports. In this article, we are referring to the best-known type of football, association football. In North America, South Africa, and Australia, to avoid confusion with other types of football, it is called "soccer".

In professional football, a transfer is the action taken whenever a player under contract moves between teams. It refers to the transferring of a player's registration from one association football club to another. In general, the players can only be transferred during a transfer window and according to the rules. The transfer window is a period during the year in which a football team can transfer players. There are two transfer windows per season: winter and summer windows. Winter transfer windows are throughout January while the summer windows are from July till August.

Usually some sort of compensation is paid for the player's rights, which is known as a transfer fee. When a player moves from one team to another, their old contract is terminated and they negotiate a new one with the team they are moving to. In some cases, however, transfers can function similarly to player trades, as teams can offer another player on their team as part of the fee.

As you may presume, there is a lot of money involved in the game of transfers. According to FIFA, in 2018, from January till September, there were 15,626 international transfers with fees totaling US$ 7.5 billion dollars.

Football season is that part of the year during which football matches are held. A typical football season is generally from August/September to May, although in some countries, such as Northern Europe or East Asia, the season starts in the spring and finishes in autumn due to weather conditions encountered during the winter.

Data model

In this article, we will present a graph model of football transfers from season 1992/1993 to season 2019/2020 in following five leagues:

  • English Premier League
  • French Ligue 1
  • German Bundesliga
  • Italian Serie A
  • Spanish Primera Division

The model consists of the following nodes:

  • Team - a football team with a property name (e.g. "FC Barcelona").
  • Player - a professional football player, contains properties name (e.g. "Luka Modric") and position (e.g. "Central Midfield").
  • League - a football league where multiple teams play in, contains one property name (e.g. "Premier League").
  • Transfer - represents football transfer that connects a Player that is transferred from one Team to another Team within a Season. Transfer contains one optional property fee (e.g. 80.50) that represents a transfer fee in millions of euros and one regular property year (e.g. 1995) that represents how old was a player when the transfer occurred.
  • Season - a football season with two properties name (e.g. "2019/2020") and year (e.g. 2019).

Nodes are connected with the following edges:

  • :TRANSFERRED_FROM - connects team node Team to node Transfer representing a team where the player is being transferred from.
  • :TRANSFERRED_TO - connects node Transfer to team node Team where player is being transferred to.
  • :TRANSFERRED_IN - connects player node Player to node Transfer representing a player that was transferred in the connected transfer.
  • :HAPPENED_IN - connects node Transfer to the node Season in which transfer has happened.
  • :PLAYS_IN - connects node Team that plays in league node League.

Football transfers

Exploring the dataset

You have two options for exploring this dataset. If you just want to take a look at the dataset and try out a few queries, open Memgraph Playground (opens in a new tab) and continue with the tutorial there. Note that you will not be able to execute write operations.

On the other hand, if you would like to add changes to the dataset, download the Memgraph Platform (opens in a new tab). Once you have it up and running, open Memgraph Lab web application within the browser on localhost:3000 (opens in a new tab) and navigate to Datasets in the sidebar. From there, choose the dataset Football player's transfers and continue with the tutorial.

Example queries using Cypher

In the queries below, we are using Cypher to query Memgraph via the console.

Now when we have a dataset of football transfers from season 1992/1993 to season 2019/2020 loaded in Memgraph, we are ready to gain some information out of it.

1. Let's say you want to find 20 most expensive transfers. As mentioned before, transfers fees are represented in millions of euros.

MATCH (t:Transfer)<-[:TRANSFERRED_IN]-(p:Player)
RETURN round(t.fee) + 'M €' AS transfer_fee, AS player_name

2. What about finding the most expensive transfer per season?

MATCH (s:Season)<-[:HAPPENED_IN]-(t:Transfer)<-[:TRANSFERRED_IN]-(:Player)
WITH AS season_name, max(t.fee) AS max_fee
RETURN round(max_fee) + 'M €' AS max_transfer_fee, season_name
ORDER BY max_fee DESC;

3. How about finding out which teams your favorite player has played for? If you wish to check the teams for another player, replace "Sime Vrsaljko" with the name of your favorite player.

MATCH (player:Player)-[:TRANSFERRED_IN]->(t:Transfer)-[]-(team:Team)
WHERE = "Sime Vrsaljko"
RETURN AS team_name;

You might wonder why we haven't specified a direction in our Cypher traversal with (:Transfer)-[]-(:Team). As we want to find the teams that player was transferred from ((:Transfer)<-[]-(:Team)) and transferred to ((:Transfer)-[]->(:Team)), we want to collect both inbound and outbound connections. In order to do so, we omit the arrow (>, <) in our Cypher command.

4. Find players that were transferred to and played for FC Barcelona and count them by the player game position.

MATCH (team:Team)<-[:TRANSFERRED_TO]-(t:Transfer)<-[:TRANSFERRED_IN]-(player:Player)
WHERE = "FC Barcelona"
RETURN player.position AS player_position, count(player) AS position_count, collect( AS player_names
ORDER BY position_count DESC;

5. Football has seen a lot of rivalries develop between clubs during its rich and long history. One of the most famous ones is between fierce rivals FC Barcelona and Real Madrid. There is a term, El Clasico, for a match between those two teams. Let's find all the transfers between FC Barcelona and Real Madrid.

MATCH (m:Team)-[:TRANSFERRED_FROM]-(t:Transfer)-[:TRANSFERRED_TO]-(n:Team),
    ( = "FC Barcelona" AND = "Real Madrid") OR
    ( = "Real Madrid" AND = "FC Barcelona")
RETURN AS transferred_from_team, AS player_name, AS transfered_to_team;

6. FC Barcelona is one of the most valuable football clubs in the world. Players often want to play there as long as possible. But what about those players who didn't fit in well? Where do they go?

MATCH (m:Team)-[:TRANSFERRED_FROM]->(t:Transfer)<-[:TRANSFERRED_IN]-(p:Player),
WHERE = "FC Barcelona"
RETURN AS team_name, collect( AS player_names, count(p) AS number_of_players
ORDER BY number_of_players DESC;

7. What are the teams that most players went to in season 2003/2004? The results may surprise you.

MATCH (season:Season)<-[:HAPPENED_IN]-(t:Transfer)<-[:TRANSFERRED_IN]-(player:Player),
WHERE = "2003/2004"
WITH DISTINCT player, team
RETURN AS team_name, count(player) AS number_of_players, collect( AS player_names
ORDER BY number_of_players DESC, team_name

8. In great teams, there are players who seem to be irreplaceable. When they leave, the club board is often struggling to find a proper replacement for them. Let's find out which positions club "FC Barcelona" spent money on in season 2015/2016.

MATCH (:Team)-[:TRANSFERRED_FROM]->(t:Transfer)<-[:TRANSFERRED_IN]-(player:Player),
WHERE t.fee IS NOT NULL AND = "2015/2016" AND = "FC Barcelona"
RETURN collect( AS player_names, player.position AS player_position, round(sum(t.fee)) + 'M €' AS money_spent_per_position
ORDER BY money_spent_per_position DESC;

9. But what was the highest transfer amount per position FC Barcelona spent on in seasons from 1992/1993 till 2019/2020?

MATCH (:Team)-[:TRANSFERRED_FROM]->(t:Transfer)<-[:TRANSFERRED_IN]-(player:Player),
WHERE t.fee IS NOT NULL AND = "FC Barcelona"
RETURN max(t.fee) + 'M €' AS max_money_spent, player.position AS player_position
ORDER BY max_money_spent DESC;

10. Now, let's find who were the most expensive players per position in team FC Barcelona.

MATCH (team:Team)<-[:TRANSFERRED_TO]-(t:Transfer)<-[:TRANSFERRED_IN]-(p:Player),
WHERE t.fee IS NOT NULL AND = "FC Barcelona"
WITH p.position AS player_position, max(t.fee) AS max_fee
MATCH (p:Player)-[:TRANSFERRED_IN]->(t:Transfer)-[:TRANSFERRED_TO]->(team:Team)
WHERE p.position = player_position AND
      t.fee = max_fee AND = "FC Barcelona"
RETURN max_fee, player_position, collect( AS player_names
ORDER BY max_fee DESC;

If we needed to get the maximum transfer fee per position we would only need first MATCH in the above query, making it way shorter. In order to match players with maximum transfer fees per position our query is split into two parts:

  • First MATCH in the query finds the maximum transfer fee per position.
  • Second MATCH in the query is finding all players transferred to "FC Barcelona" with the same position and transfer fee equal to the maximum one from the previous query.

11. If you want to find all player transfers between two clubs you can do that also.

MATCH (t:Transfer)<-[:TRANSFERRED_IN]-
WHERE = "FC Barcelona"
WITH player, collect(t) AS transfers
MATCH player_path = (a:Team)
      -[*bfs..10 (e, n | 'Team' IN labels(n) OR ('Transfer' IN labels(n) AND n IN transfers) )]->(b:Team)
WHERE = "FC Barcelona" AND = "Sevilla FC"
UNWIND nodes(player_path) AS player_path_node
WITH player_path_node, player
WHERE 'Team' IN labels(player_path_node)
WITH collect( AS team_names, player
RETURN AS player_name, team_names;

In the above query, we will find all players that transferred from "FC Barcelona" to "Sevilla FC". It will include direct transfers (from "FC Barcelona" to "Sevilla FC") and indirect transfers (from "FC Barcelona" to one or multiple other clubs and lastly "Sevilla FC"). That is the reason why we started first MATCH with searching for all players and transfers that were transferred from "FC Barcelona". Next up is the player transfer traversal through transfers and teams all the way to the "Sevilla FC".

For this part, we used the breadth-first search (BFS) algorithm with lambda filter (e, v | condition). It's a function that takes an edge symbol e and a vertex symbol v and decides whether this edge and vertex pair should be considered valid in breadth-first expansion by returning true or false (or Null). In the above example, lambda is returning true if a vertex has a label Team or a label Transfer. If a vertex is Transfer there is an additional check where we need to make sure the transfer is one of the transfers of players transferred from "FC Barcelona". It needs to be either Team or Transfer because to get from a team that made the transfer to the team where the player is being transferred to, we need to go through the node Transfer that connects those two teams. So the traversal from "FC Barcelona" to "Sevilla FC" will go through the following nodes: Transfer, Team, Transfer, Team, Transfer, etc.

12. In the previous query, we found all transfers between two clubs. Let's filter out direct ones now. We need to add a small change in the query to only get indirect transfers.

MATCH (player:Player)-[:TRANSFERRED_IN]->(t:Transfer)<-[:TRANSFERRED_FROM]-(barca:Team),
WHERE = "FC Barcelona" AND = "Sevilla FC"
WITH collect(player) AS players_direct_to_sevilla
MATCH (t:Transfer)<-[e:TRANSFERRED_IN]-
WHERE = "FC Barcelona" AND
      NOT player IN players_direct_to_sevilla
WITH player, collect(t) AS transfers
MATCH path_indirect = (a:Team)
      -[*bfs..10 (e, n | 'Team' IN labels(n) OR ('Transfer' IN labels(n) AND n IN transfers) )]->(b:Team)
WHERE = "FC Barcelona" AND = "Sevilla FC"
UNWIND nodes(path_indirect) AS player_path_node
WITH player_path_node, player
WHERE 'Team' IN labels(player_path_node)
WITH collect( AS team_names, player
RETURN AS player_name, team_names;

In this query, the only difference is that we need to find players who had a direct transfer to Sevilla first. In the next MATCH we use that information to check whether players that were transferred from FC Barcelona, didn't have direct transfer to Sevilla FC.

If you are running this in Memgraph Lab (opens in a new tab) you can change the query a bit in order to get all nodes and edges required for a visual graph representation of players transferring through teams.

MATCH (player:Player)-[:TRANSFERRED_IN]->
MATCH (t)-[:TRANSFERRED_TO]->(sevilla:Team)
WHERE"FC Barcelona" AND"Sevilla FC"
WITH collect(player) AS players_direct_to_sevilla
MATCH (t:Transfer)<-[e:TRANSFERRED_IN]-
WHERE = "FC Barcelona" AND
      NOT player IN players_direct_to_sevilla
WITH player, collect(t) AS transfers, collect(e) AS player_to_transfers
MATCH path_indirect = (a:Team)
      -[*bfs..10 (e, n | 'Team' IN labels(n) OR ('Transfer' IN labels(n) AND n IN transfers) )]->(b:Team)
WHERE = "FC Barcelona" AND = "Sevilla FC"
UNWIND player_to_transfers AS player_to_transfer
RETURN player, player_to_transfer, path_indirect;

MemgraphLab graph visual representation draws nodes and edges from query results. If you only have nodes in the results then only nodes will be drawn on the canvas. If you have both nodes and edges present in the results, MemgraphLab is able to draw nodes and connections between them because it has all the information relevant for drawing.

In order to change the query to accommodate that, we need to change the types of results that are returned and collect any missing edge or node information throughout the query. The first part of the query where we check whether the player was transferred from "FC Barcelona" to "Sevilla FC" stays the same. In order to draw all connections from players to transfers, we need to collect edges connecting them. That is the reason why we are collecting edges e through variable player_to_transfers because it contains information on which player is connected to which transfer. With that in mind, our results contain all the information for the graph visual:

  • A path that contains Transfer and Team nodes, and all the edges collected on the Team to Team traversal
  • A list of Player nodes
  • A list of Player - Transfer edges

Here is a picture of how it will look if you run the query in MemgraphLab.