Original Research

International Journal of Sports Science

& Coaching
Using passing network measures to 0(0) 1–7
! The Author(s) 2020
determine the performance difference Article reuse guidelines:
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between foreign and domestic outfielder DOI: 10.1177/1747954120905726
journals.sagepub.com/home/spo

players in Chinese Football Super League

Qian Yu1,*, Yang Gai2,*, Bingnan Gong2,3, Miguel-Ángel Gómez2

and Yixiong Cui4

Abstract
The study was aimed to identify the passing performances related to social network between domestic (Chinese) and
foreign (non-Chinese) players that contributed to the offensive play according to playing positions in Chinese Football
Super League. Data were collected from all 16 teams, with 63,446 passes from 8885 player observations of 346 unique
players (258 domestic and 88 foreign) being filtered. Nine network metrics were adopted to assess the passing per-
formance between two player groups. The results revealed that foreign players, especially midfielders and forwards, had
increased values than domestic counterparts in passes in, indegree, stress, partner and betweenness centrality (stand-
ardised Cohen’s d range: 0.18–0.62; inference: possibly to most likely). Moreover, foreign midfielders also demonstrated
higher values in passes out, outdegree and closeness centrality (0.30–0.56; likely to most likely). The analysis of passing
performance allows for a better understanding of team’s attacking properties and highlights the prominent role foreign
players act within the build of attack. It is possible that recruiting high-level overseas players contribute to Chinese
teams’ goal scoring performance, but coaching staffs and team managers should take cautions in terms of the degree of
reliance on them, as the development of domestic midfielders and forwards would be compromised.

Keywords
Football, foreign players, match analysis, passing performance, social network

Introduction
Computer-based approaches have been applied to col-
lect and analyse technical, tactical and physical aspects Reviewers: Cesar Mendez-Dominguez (Universidad Complutense de
in team sport, especially in football, and progressively Madrid, Spain)
Carlos Lago-Pe~nas (University of Vigo, Spain)
increased the information and dimensions related to Diogo Coutinho (Universidade de Trás-os-Montes e Alto
the study of match performance behaviours.1–3 Douro, Portugal)
Recently, the general structure of football teams and
connections among players during match-play were *Co-first author.
investigated using social network analysis (SNA).4–7 1
College of Physical Education, Yangzhou University, Yangzhou, China
2
Contrast to the traditional notational analysis, the Faculty of Physical Activity and Sport Sciences, Technical University of
SNA considers and models passing distribution of Madrid, Madrid, Spain
3
China Football College, Beijing Sport University, Beijing, China
teams and is proved to be useful since its concepts 4
AI Sports Engineering Lab, School of Sports Engineering, Beijing Sport
and methods could not only illuminate player’s or University, Beijing, China
team’s dynamics, but also improve the understanding
of complex relational interactions within the team.8,9 Corresponding author:
Yixiong Cui, AI Sports Engineering Lab, School of Sports Engineering,
Specifically, this promising body of research helped to Beijing Sport University; Information Road No. 48, 100084, Beijing,
understand the most prominent players’ positions that China.
best contributed to the build of attack during football Email: amtf000cui@gmail.com
2 International Journal of Sports Science & Coaching 0(0)

matches,10 analysed the difference in network density players on the field at any given minute. Given this
between two halves in the match11 and investigated the specific policy, one that is more restrictive than most
number of passes per minute and the clustering coeffi- other domestic elite competitions,30 it is expected that
cient of teams during the FIFA World Cup.12 teams within the CSL may exhibit distinctive perfor-
Generally, network analysis is based on the accumu- mance profiles with differences potentially notable
lation of passes in which happen numerously in every between domestic and foreign players according to play-
match irrespective of the quality of the teams.13 ing positions, and determining the prominence of for-
The passing network of a football team consists of eign and domestic players could better inform team’s
the players as vertices and the passes between the play- tactical training and improve inter-player dynamics.
ers as the edges. Prior studies adopting this approach Therefore, based on the above rationale, this study
went beyond simplistic frequency counts of passes tried to provide the knowledge about the match perfor-
made and provide insights into teams’ passing flow, mance of foreign players in Asian professional football
player interaction and prominent player during offen- league and look into their roles within the team’s net-
sive play by using metrics such as degree centrality, work. The specific aim was to investigate the difference
betweenness centrality (BC) and closeness centrality in match performances between foreign and domestic
(CC).14,15 In particular, Grund6 found that high net- outfield players within team’s passing network from
work intensity and low centralisation were related to CSL, regarding different playing positions. It was
better team performance in Premier League. Clemente hypothesised that foreign players of all playing posi-
et al.16 analysed 128 passing adjacency matrices from tions outperformed domestic players in all network-
2014 World Cup and concluded that midfielders played related metrics.
the most important role in building the attack. A more
recent study analysing the last 2018 World Cup showed
that teams adapted their passing microstructures
Materials and methods
according to different match status, and a more direct Sample and variables
play style was preferred when teams were losing.9
However, among available literature, it appears that By undertaking an observational design, this study
relative analysis of foreign transferred players in foot- included all 240 matches from the CSL 2017 season.
ball developing countries, such as in Asia (e.g. China), The inter-player passing statistics of all matches were
was seldom addressed.17 tracked and provided by the Shanghai Champion
The phenomenon of labour migration is a salient fea- Information Technology Co., Ltd. through a validated
ture of modern sports that has been investigated by data collection system (Champdas Master System,
scholars for over a decade covering a wide range of http://www.champdas.com) operated by trained collec-
sports. For instance, the subject of football has also tors, and they have at least two years of experience in
been relatively well researched,18–25 and it is positively operating the system.3 The starting players and substi-
established that migration in sport is a complex and tutes were all included. However, as the study only
multidimensional process and a part of global sport sys- considered the passing performance of outfield players
tems.23,26 The societal multi-dimensionality of migratory when teams were in possession of the ball, after organ-
issues, however, has led to the proliferation of academic ising the data, any inter-player passing count less than
interest with often divergent views on the subject.22,27 five passes was ruled out, and goalkeeper-involved
This migration is influenced by foreign player policies passes were also excluded during the filtering process.
with differences noted between some competitions. For Finally, 63,446 passes were filtered, and they belonged
example, more restrictive policies to sign foreign players to 8885 player observations of 346 unique players (258
are experienced in Asia compared to other competi- domestic and 88 foreign) from all 16 CSL teams.
tions.28 There is a great emphasis on including domestic The players were further divided into three different
players and limited foreign players per team within groups according to their positions based on the tacti-
the Korean, Japanese and Chinese competitions.28 As cal line-up of each CSL team: defender (2442 domestic
the first league of a football developing country, the player observations from 103 unique players and 379
Chinese Super League (CSL) is a representative of foreign player observations from 19 unique players),
such phenomenon and recruits many top foreign players midfielder (3257 from 120 unique players and 1407
each year to improve team quality and obtain success.29 from 28 unique players, respectively) and forward
Under such circumstances, the Chinese Football (481 from 35 unique players and 919 from 41 unique
Association had to establish a foreign player policy players, respectively). All players’ positions were deter-
since 2017 CSL, stating that each team may have a max- mined in the match statistics offered by the data pro-
imum of four foreign outfield players, and during the vider, so that positions such as wingers, forwards and
match, a team should have no more than three foreign strikers were assigned into the category of forward;
Yu et al. 3

attacking midfielders, centre midfielders, left and right player has a high stress if it is traversed by a high
midfielders were classified as midfielders; while left and number of shortest paths
right fullbacks, centre backs and sweepers were as
defenders. The personal characteristics for both domes-
X i; j 2 V
tic and foreign players of these three categories are Stress ðAÞ ¼ g ðAÞ
shown in Table 1. Based on the number of matches i 6¼ j 6¼ A ij
contested, a total of 480 adjacency matrices were estab-
lished, and the corresponded network graphs were gen- where gij(A) is the number of shortest paths between
erated. This matrix represents the connections between player i and player j that pass through player A.
a player and an adjacency teammate.16
The following two passing-related variables and 5. Partner (partner of multi-edged node pairs): the
seven SNA metrics were used to evaluate players’ pass- value indicates if certain node (player) is a partner
ing performance and their importance within team’s of node pairs with multiple edges.
passing network. 6. Betweenness centrality (BC): it tries to measure the
Passing performance variables: extent of the control that each node (player) holds
over the network by considering the shortest paths
1. Passes out: total number of passes that the player between all pairs of nodes (players). In other words,
realised effectively to his teammates during the match. this metric indicates the amount of network that a
2. Passes in: total number of passes that the player particular player ‘controls’ and is one of the most
received effectively from teammates during the match. meaningful measure among other metrics because it
successfully quantifies how often each player lies
Generally, SNA contains various centrality metrics between other players of the passing network,
to evaluate the importance of certain node (player) acting as a mediator or ‘bridge’ for them
inside a given social network. But considering the prac-
tical application of SNA to football analysis, the study !
X rij ðAÞ
just applied the following variables that are suitable for Betweenness ðAÞ ¼
the interpretation of players’ importance within a pass- i6¼A6¼j rij
ing network:14,15,31,32
where i and j are players in the passing network differ-
1. Neighbourhood connectivity (NC): in SNA, the con- ent from player A, rij denotes the number of shortest
nectivity of a node is determined by the number of paths from player i to player j and rij (A) is the number
its neighbours. Therefore, the NC of a player in of shortest paths from i to j that A lies on.
football is defined as the average connectivity of
all teammates of that player. 7. Closeness centrality (CC): This index attempts to
2. In-degree centrality (IDC): the count of inbound quantify actor importance in terms of their total
edges (arcs) from all neighbouring nodes (team- graph theoretic distance in the social network. It
mates) to a certain node (player); indicates how easy it is for a player to be connected
3. Out-degree centrality (ODC): the count of outbound with teammates (by passing relation); therefore, that
edges (arcs) from a certain node (player) to all nodes player is requested by the team as a target to pass
(players) that are connected to it (neighbours); the ball. Thus, it provides a direct measurement on
4. Stress centrality (SC): the metric is the summa of the how easy it is to reach a particular player within a
number of shortest paths passing through n. A team. A high closeness score corresponds to a small

Table 1. Personal characteristics for CSL domestic and foreign players.

Defender Midfielder Forward

Domestic Foreign Domestic Foreign Domestic Foreign

(n ¼ 1026–1272) (n ¼ 160–176) (n ¼ 1272–1357) (n ¼ 397–411) (n ¼ 295–300) (n ¼ 520–555)

SNA variables Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)

Height (cm) 185.3 (4.1 188.1 (4.7) 179.3 (5.1) 181.3 (5.1) 182.7 (4.8) 180.4 (6.8)
Weight (kg) 77.1 (5.1) 82.1 (6.3) 71.8 (5.7) 74.8 (5.4) 72.5 (5.2) 74.2 (6.0)
Age (years) 26.9 (3.8) 29.3 (3.1) 26.9 (3.8) 29.1 (2.5) 26.7 (4.4) 27.8 (3.7)
Time played (%) 96.6 (11.1) 97.9 (8.2) 86.4 (19.3) 97.9 (8.2) 82.0 (20.3) 97.1 (9.2)
4 International Journal of Sports Science & Coaching 0(0)

average distance, indicating a well-connected player in passes in, indegree, SC, partner and BC (standar-
within the team dised Cohen’s d range: 0.18–0.62; inference: possibly
to most likely). Moreover, foreign midfielders also
1 demonstrated higher values in passes out, outdegree
Closeness ðAÞ ¼ X
i2V
LðA; iÞ and closeness centrality (0.30–0.56; likely to most
likely). On the contrary, while both domestic defenders
where L(A,i) is the length of the shortest path between and midfielders had higher values of NC than foreign
two players A and i, the formula then calculates the counterparts (–0.29 to –0.18; possibly to likely), domes-
summa of player A and other teammates and the recip- tic defenders were also shown to possess higher passes
rocal of the value is obtained. Players with low close- in, indegree and partner (–0.25 to –0.19; possibly to
ness score have little proximity to others. likely) (see Figure 1).

Statistical analysis Discussion

After determining the normal distribution of the data The aim of this study identified the most prominent
(via Kolmogorov–Smirnov test), through nine passing players that build the attack of a team within the
network-related variables that measure player’s prom- CSL according to playing positions (defender, midfield-
inence inside a team’s network were calculated and er and forward) and nationality (foreign and Chinese).
were compared between domestic and foreign players The results of this study showed that foreign players
via standardised (Cohen’s d) mean differences, comput- played a prominent role in the offensive play of CSL
ed with pooled variance and respective 90% confidence teams and contributed to the build of attack, especially
intervals (CI). Uncertainty in the true differences of the on midfielders. Moreover, coach must take into consid-
comparisons was assessed using the non-clinical mag- eration the individual contribution of each player for
nitude-based decisions,33 and the magnitudes of clear the overall connection of the team can be important
differences were assessed as follows: <0.20, trivial; indicator that may increase the possibility of optimising
0.20–0.60, small; 0.61–1.20, moderate; 1.21–2.0, large the tactical performance of football players. To the best
and >2.0, very large.34 Differences were deemed clear if of our knowledge, this is the first study to analyse the
the CI for the difference in the means did not include collective behaviour of team via SNA according to
positive and negative values (0.2 times the standard- playing position in the CSL.
isation estimated from between-subject standard devi- For defender, the results showed that all SNA-
ation) simultaneously.35 related variables were similar between domestic and
foreign players except for IDC, which is an important
variable that should be considered in match analysis. In
Results particular, this player’s position receives most of the
Table 2 represents the descriptive statistics of all pass- passes from their teammates. The domestic players
ing network metrics for domestic and foreign players of showed higher values in IDC than their counterparts,
different playing positions. Foreign midfielders and which is in line with the previous study of Clemente
forwards presented higher values than domestic players et al.16 who reported that the high value related to

Table 2. Descriptive statistics of passing and SNA related variables for CSL domestic and foreign players.

Defender Midfielder Forward

Domestic Foreign Domestic Foreign Domestic Foreign

(n ¼ 1026–1272) (n ¼ 160–176) (n ¼ 1272–1357) (n ¼ 397–411) (n ¼ 295–300) (n ¼ 520–555)

SNA variables Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)

Passes out 16.00 (10.82) 15.72 (11.74) 17.50 (12.50) 26.05 (17.51) 12.38 (9.86) 12.90 (9.09)
Passes in 14.88 (10.01) 13.11 (8.96) 17.32 (12.34) 24.2 (16.22) 13.19 (10.46) 16.01 (10.94)
Neighborhood 3.97 (1.61) 3.72 (1.51) 3.93 (1.59) 3.49 (1.48) 4.10 (1.89) 3.86 (1.69)
Out-degree 1.96 (2.56) 1.96 (1.49) 1.94 (2.49) 3.14 (5.14) 1.45 (1.52) 1.54 (1.54)
In-degree 1.69 (2.11) 1.27 (1.18) 2.11 (2.28) 3.03 (3.69) 1.54 (1.66) 1.86 (1.80)
Stress 6.00 (9.41) 5.69 (9.46) 7.36 (11.43) 13.71 (15.73) 2.91 (5.29) 4.08 (7.93)
Partner 0.88 (1.21) 0.66 (0.96) 1.05 (1.37) 1.61 (1.71) 0.55 (0.87) 0.71 (1.05)
Betweenness 0.06 (0.09) 0.06 (0.11) 0.08 (0.12) 0.18 (0.17) 0.04 (0.07) 0.06 (0.10)
Closenness 0.48 (0.27) 0.51 (0.26) 0.45 (0.31) 0.57 (0.30) 0.45 (0.34) 0.47 (0.34)
Yu et al. 5

Figure 1. Effect sizes [90% CI] of differences in the mean counts between domestic and foreign players, when bars of one variable
crossed the negative and positive smallest worthwhile change threshold in the meantime, the effect was unclear. Asterisks indicate the
likelihood for the magnitude of the true differences in mean as follows: *possible; **likely; ***very likely; ****most likely. Asterisks
located in the trivial area denote for trivial differences.

IDC was found in the central defender based on 1-3-5-2 that foreign midfielders not only promoted connectivity
playing formation. At the meantime, it is highly prob- between all teammates, but also played the prominent
able that most CSL teams prefer to contract more for- role in the offensive phase, fostering a global cooperation
eign players that could instantly improve attacking among the team. Although domestic players demonstrat-
quality and make them the priority in the attacking ed comparatively higher value in Neighbourhood, it is
process, given the current league’s policy that limits likely that rather than making key passes to opponent’s
the number of on-pitch foreign players in the match defensive zones, they are generally tending to make
and the outnumbered foreign defender observations more “safe” passes that are less tackled, to defending
throughout the whole 2017 CSL season. Therefore, players, as most of them are still refining their passing
domestic players might have played more important effectiveness, tactical creativity and decision-making effi-
role in the defending process as central defender ciency.17 However, the current study contrasts with the
mainly showed high volume of passes in the first previous study of Gai et al.,17 where the differences of
phase of building the attack for increasing the connec- playing styles could indicate a more collective style of
tion among teammates in the defensive zone.16 play for foreign players in their home countries while
For midfielders, the current results are in line with domestic players reflected a minor tactical focus.
previous studies,11,36 where the midfielders demonstrat- Despite the disparities of playing styles during formative
ed greater values for most of SNA-related variables years between foreign and domestic players,30 domestic
such as IDC, ODC, CC and BC, compared to the players still try to cover the limitations of their less tech-
others playing positions (e.g. defender and forward). nical and tactical skills giving the teammates frequent
The highest volume of passes between such players’ cooperation with each other. Moreover, as team manag-
positions, mainly in the offensive phase, increases the ers of CSL league are prone to design most offensive
connection among teammates in the middle sectors. tactics emphasising foreign players’ major participation,
Likewise, our finding also showed that all SNA- domestic midfielders would not get as many chances as
related variables significantly differentiated domestic their foreign peers to attempt more penetrative passes
and foreign players. More specifically, foreign players to forwards or receive more passes from defenders.
showed high values than their domestic counterparts in This phenomenon is mirrored by that greater values of
most variables except for Neighbourhood, revealing foreign players in CC and BC, which are more
6 International Journal of Sports Science & Coaching 0(0)

meaningful indexes than the other degree centrality met- Declaration of conflicting interests
rics, such as IDC and ODC.37 The author(s) declared no potential conflicts of interest
For forwards, the results showed that all SNA- with respect to the research, authorship, and/or publication
related variables were similar between domestic and of this article.
foreign players except for pass in. The current study
is in line with the previous study,36 which indicated
that forwards can always be identified as those players Funding
having the lowest closeness and betweenness, as they The author(s) disclosed receipt of the following financial sup-
are isolated players waiting to receive passes as well as port for the research, authorship, and/or publication of this
players who get replaced more often. Particularly, the article: This work was supported in part by National Key
current study confirmed that forwards received more R&D Program of P.R. China (2018YFC2000600). The cor-
passes from their teammates, especially for foreign for- responding author was supported by the China Postdoctoral
wards, because of the limits of the number of foreign Science Foundation and the Fundamental Research Funds
player’s policy in the CSL, the teams likely recruit for the Central Universities (2019QD033).
these players to key positions related to goal scoring
(forward), decision-making and passing skills (central
midfielder) and defensive actions (central defender). ORCID iD
Additionally, the imbalanced recruitment of teams Yixiong Cui https://orcid.org/0000-0002-1755-9631
from the CSL is focused on those playing positions
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