soccer_passing — Soccer passing networks (Wyscout, 2016-2018)

Description

Passing networks of soccer (association football) players, built from the match event data ("soccer-logs") collected by Wyscout and published by Pappalardo et al.: all the 1,941 matches of seven competitions, one network per competition:

england: English Premier League, season 2017/2018 (380 matches);
france: French Ligue 1, season 2017/2018 (380 matches);
germany: German Bundesliga, season 2017/2018 (306 matches);
italy: Italian Serie A, season 2017/2018 (380 matches);
spain: Spanish La Liga, season 2017/2018 (380 matches);
euro_2016: UEFA European Championship 2016 (51 matches);
world_cup_2018: FIFA World Cup 2018 (64 matches).

Each node is a player, and each directed edge goes from a player to a teammate who received at least one of his completed passes in a given match, weighted by the number of such passes; the network of a competition is thus a multigraph, with one edge per pair of players and match, and the passing network of a single team in a single match is the subgraph of the edges with the corresponding match_id and team. Passes include the free kicks that are passes (corners, free kicks, free kick crosses, goal kicks and throw-ins), but not shots and penalties.

Wyscout records who made each pass and whether it was accurate (i.e. reached a teammate), but not who received it. As usual for these data, the recipient of an accurate pass is taken to be the player of the next event of the same team in the same period of the match (events of the opposing team in between, e.g. duels, are skipped). Of the 1,855,914 passes in the data, 1,478,167 are included: left out are the 341,770 passes that were not accurate, and the accurate passes for which no recipient can be determined in this way (7,416 for which the next event of the team is by the same player, 28,547 for which it has no player, and 14 that are the last events of the team in a period). Only players who made or received at least one included pass appear in the networks.

The networks have the following properties:

(Node) player_id: the Wyscout id of the player.
(Node) name: his short name.
(Node) first_name and last_name.
(Node) role: his role (goalkeeper, defender, midfielder or forward).
(Node) team: the team(s) for which he made or received passes in the competition (several, separated by semicolons, for players transferred during the season).
(Node) nationality: the country of his passport.
(Node) birth_date.
(Node) height: in centimeters, and weight_kg, his body weight in kilograms (0 if unknown).
(Node) foot: his preferred foot (empty if unknown).
(Edge) weight: the number of completed passes.
(Edge) match_id: the Wyscout id of the match.
(Edge) time: the starting time of the match (Unix time).
(Edge) match: the label of the match (teams and final score).
(Edge) team: the team of the two players.

Tags

Social Offline Sports Weighted Multigraph Timestamps Metadata

Added

2026-10-27

Citation

L. Pappalardo, P. Cintia, A. Rossi, E. Massucco, P. Ferragina, D. Pedreschi, F. Giannotti, "A public data set of spatio-temporal match events in soccer competitions." Scientific Data 6, 236 (2019) 10.1038/s41597-019-0247-7 Sci-Hub

Upstream URL 403

https://doi.org/10.6084/m9.figshare.c.4415000

Upstream license

CC BY 4.0, https://creativecommons.org/licenses/by/4.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
england	514	79,497	154.66	208.34	24.01	8172.89	0.01	0.91	10	0.49	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	167 KiB	517 KiB	437 KiB	404 KiB
france	541	81,300	150.28	207.76	23.22	13581.72	-0.02	0.91	12	0.49	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	171 KiB	470 KiB	437 KiB	401 KiB
germany	473	66,631	140.87	188.87	23.20	8290.35	0.03	0.91	13	0.60	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	141 KiB	432 KiB	364 KiB	331 KiB
italy	533	82,279	154.37	209.90	26.20	1935.62	0.08	0.91	16	0.77	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	173 KiB	468 KiB	438 KiB	400 KiB
spain	557	81,526	146.37	204.50	26.40	437.55	0.04	0.91	9	0.56	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	176 KiB	493 KiB	444 KiB	408 KiB
euro_2016	449	10,823	24.10	30.60	15.79	1.03	0.43	0.86	2	0.05	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	42 KiB	83 KiB	77 KiB	68 KiB
world_cup_2018	602	13,796	22.92	29.07	15.23	0.91	0.47	0.86	2	0.04	Directed	Unipartite	player_id name first_name last_name role team nationality birth_date height weight_kg foot	weight match_id time match team	55 KiB	106 KiB	99 KiB	88 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.