Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review

Abstract

In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases, resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis of tactical behaviour, albeit in - sometimes radically - different ways. Accordingly, we present a multidisciplinary framework where each domain's contributions to feature construction, modelling and interpretation can be situated. We discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which is pivotal in unlocking the potential of position tracking data in sports analytics.

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European Journal of Sport Science
ISSN: 1746-1391 (Print) 1536-7290 (Online) Journal homepage: https://www.tandfonline.com/loi/tejs20
Unlocking the potential of big data to support
tactical performance analysis in professional
soccer: A systematic review
F.R. Goes, L.A. Meerhoff, M.J.O. Bueno, D.M. Rodrigues, F.A. Moura, M.S.
Brink, M.T. Elferink-Gemser, A.J. Knobbe, S.A. Cunha, R.S. Torres & K.A.P.M.
Lemmink
To cite this article: F.R. Goes, L.A. Meerhoff, M.J.O. Bueno, D.M. Rodrigues, F.A. Moura, M.S.
Brink, M.T. Elferink-Gemser, A.J. Knobbe, S.A. Cunha, R.S. Torres & K.A.P.M. Lemmink (2020):
Unlocking the potential of big data to support tactical performance analysis in professional soccer:
A systematic review, European Journal of Sport Science, DOI: 10.1080/17461391.2020.1747552
To link to this article: https://doi.org/10.1080/17461391.2020.1747552
© 2020 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
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REVIEW
Unlocking the potential of big data to support tactical performance
analysis in professional soccer: A systematic review
F.R. GOES
1
, L.A. MEERHOFF
2
, M.J.O. BUENO
5
, D.M. RODRIGUES
3
,
F.A. MOURA
5
, M.S. BRINK
1
, M.T. ELFERINK-GEMSER
1
, A.J. KNOBBE
2
,
S.A. CUNHA
4
, R.S. TORRES
3
, & K.A.P.M. LEMMINK
1
1
Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen,
The Netherlands;
2
Leiden Institute of Advanced Computer Sciences (LIACS), Leiden University, Leiden, The Netherlands;
3
Institute of Computing (IC), University of Campinas, Campinas, Brazil;
4
Sport Sciences Department (DCE), University of
Campinas, Campinas, Brazil &
5
Sport Sciences Department, State University of Londrina, Londrina, Brazil
Abstract
In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical
behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly
employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports
science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the
context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies
employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases,
resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis
of tactical behaviour, albeit in –sometimes radically –different ways. Accordingly, we present a multidisciplinary
framework where each domain’s contributions to feature construction, modelling and interpretation can be situated. We
discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal
aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which
is pivotal in unlocking the potential of position tracking data in sports analytics.
Keywords: Football, big data, tactical analysis, team sport, performance analysis
Highlights
.Over the recent years, there has been a considerable growth in studies on tactical behaviour using position tracking data,
especially in the domains of sports science and computer science. Yet both domains have contributed distinctly different
studies, with the first being more focused on developing theories and practical implications, and the latter more on
developing techniques.
.Considerable opportunities exist for collaboration between sports science and computer science in the study of tactics in
soccer, especially when using position tracking data.
.Collaborations between the domains of sports science and computer science benefit from a stronger dialogue yielding a
cyclical collaboration.
.We have proposed a framework that could serve as the foundation for the combination of sports science and computer
science expertise in tactical analysis in soccer.
1. Introduction
Increasingly large amounts of data are collected in
professional soccer for the purpose of match analysis.
Player positions are tracked continuously during
practice and competition using state-of-the-art track-
ing systems (Rein & Memmert, 2016). Due to recent
technological innovations, there has been a particular
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License
(http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Correspondence: F. Goes, Center for Human Movement Sciences, University of Groningen, UMCG, Antonius Deusinglaan 1, 9713 AV,
Groningen, The Netherlands. E-mail: f.r.goes@umcg.nl
European Journal of Sport Science, 2020
https://doi.org/10.1080/17461391.2020.1747552

increase in systems and devices that collect and
provide position tracking data. These innovations
have been embraced and widely adopted by pro-
fessional sports organizations, and the use of data is
broadly considered as a potential game-changer in
professional sports (Rein & Memmert, 2016).
However, there is still a lot to be gained, as the avail-
ability of data has increased much more rapidly than
the scientific advancements required to valorise data
in the domain of soccer (Rein & Memmert, 2016).
One of the more interesting opportunities provided
by the availability of position tracking data in soccer is
the study and analysis of tactical behaviour. Tactical
behaviour is an important determinant of perform-
ance in team sports like soccer, and refers to how a
team manages its spatial positioning over time to
achieve a shared goal (i.e. scoring), while interacting
with the opponent under constraints of the con-
ditions of play (Gréhaigne, Godbout, & Bouthier,
1999; Rein & Memmert, 2016). In the past, the
analysis of tactical behaviour has mostly been based
on manually annotated data and observation by
experts (Rein & Memmert, 2016). As these assess-
ments mainly describe what happens with the ball,
they only provided insights into the “who and
what”, and –albeit with poor accuracy –the
“where, and when”of on-ball behaviour (Vilar,
Araujo, Davids, & Travassos, 2012). However, as
tactical behaviour is the result of the interaction
between all players on –and off the ball (Gréhaigne
et al., 1999; Rein & Memmert, 2016), truly analysing
the mechanisms behind it requires accurate data on
all 22 players and the ball. Therefore, position track-
ing data provides the opportunity to accurately study
the mechanisms behind tactical behaviour in soccer.
However, despite its potential in the analysis of tacti-
cal behaviour, so far it has mainly been used to deter-
mine player activity profiles to monitor player loading
and subsequently prescribe training loads (Sarmento
et al., 2014).
The large amounts of position tracking data chal-
lenge the data management and analytics methods
native to sports (Gandomi & Haider, 2015), and
unlocking its potential in the study of tactical behav-
iour requires solving these challenges first (Rein &
Memmert, 2016). Although data can be considered
‘big’based on the three V’s (volume, variety, and vel-
ocity Gandomi & Haider, 2015), there are no univer-
sal benchmarks for these dimensions. Whether a
dataset is considered big or not heavily depends on
the interplay between these dimensions, and is gener-
ally considered to be domain specific (Gandomi &
Haider, 2015). One could consider data ‘big’when
it exceeds the ‘three-V tipping point’: the point
where traditional data management and analysis
methods become inadequate (Gandomi & Haider,
2015). The overall process of deriving information
from position tracking data can be divided into two
components: data management and data analytics
(Gandomi & Haider, 2015; Labrinidis & Jagadish,
2012). These components can each be divided
further into various sub processes, each associated
with their own challenges (Gandomi & Haider,
2015; Labrinidis & Jagadish, 2012). Challenges to
the data management component have been
thoroughly addressed in previous reviews. Manafi-
fard, Ebadi, and Moghaddam (2017) for example
provide a detailed review on the strengths and weak-
nesses of optical tracking systems, and what could be
done when it comes to (pre-)processing to improve
data collection with these systems in the future (Man-
afifard et al., 2017). In other examples Stein et al.
(2017) and Rein and Memmert (2016) (both specific
to soccer) and Gandomi and Haider (2015) (in
general) have addressed the various data streams
that need to be brought together in the analysis, and
how this poses a challenge to data management
systems commonly employed in soccer (Gandomi
& Haider, 2015; Rein & Memmert, 2016; Stein
et al., 2017). Challenges to data analytics on the
other hand, and specifically the challenge of aggregat-
ing raw position data into interpretable spatiotem-
poral features that capture the complex dynamics of
tactical behaviour, have received considerably less
attention so far.
Contributions from the domain of sports science
and the domain of computer science are typically
characterized by distinctly different research para-
digms. Research from the domain of sports science
on tactical behaviour is generally characterized by
deductive reasoning in forming a hypothesis and
designing an (experimental) study. Teams are for
example considered as complex dynamical systems
and hypotheses regarding their behaviour are formu-
lated based on expectations rooted in such a theoreti-
cal perspective (Araújo et al., 2015; Balague,
Torrents, Hristovski, Davids, & Araújo, 2013;
Seifert, Araújo, Komar, & Davids, 2017) To study
whether soccer teams behave like dynamical
systems, and to study how manipulating constraints
affects the system’s behaviour, data is typically col-
lected for a specific research purpose, after the
research question has been formulated. In most
sports science contributions, this means data is col-
lected in an experimental setting –most frequently
a set of manipulated small-sided games –which is
designed based on the research question and related
hypotheses. The raw position tracking data is then
usually aggregated into features that operationalize
the hypotheses and represent group level behaviour,
such as team centroids or team surface areas
(Frencken, Lemmink, Delleman, & Visscher, 2011;
2F. Goes et al.

Memmert, Lemmink, & Sampaio, 2017). A feature
like the team centroid reduces the complex behaviour
of a group of players into interpretable behaviour by
aggregating their movements into a single feature,
in the case of the centroid, representing the average
positions at a point in time. These aggregated features
are then used to study the interaction between groups
over time. This can be insightful for the development
of specific theories. However, by reducing the team’s
performance to these aggregated features, relevant
aspects of the complexity of this behaviour may be
overlooked. Aggregating the behaviour of 11 players
into one feature, like the centroid, might for
example, fail to capture the different movements of
sub-units (i.e. defensive line) on the team and
thereby fail to fully capture the complexity of tactical
behaviour.
On the other hand, contributions from the domain
of computer science, as well as the application of its
techniques –also described as ‘data science’–
utilize a distinctly different research paradigm. Com-
puter science concerns the theoretical foundations of
(computationally retrieving) information, typically
yielding advanced analyses and high-level represen-
tations of large and complex data (Gudmundsson &
Horton, 2017). For example, Knowledge Discovery
(also referred to as ‘Data Mining’) is all about identi-
fying the robustness of patterns that are found
without formulating hypotheses about the existence
of these patterns. Although both sports- and compu-
ter science adopt a deductive approach, the type of
empirical evidence for these deductions is radically
different. In sports science, (experimental) research
designs typically aim to confirm or reject a hypothesis
that was formulated based on theory as discussed in
the previous paragraph. In computer science, new
modelling techniques are evaluated by testing the
robustness of the generated model. This quantifi-
cation of robustness can then be used to verify
whether a discovered pattern was ‘significant’:How
likely is it that this pattern was found by chance? In
other words, whether the technique worked success-
fully is deduced based on the empirical evidence to
quantify the robustness. Explorative techniques
such as subgroup discovery (Grosskreutz & Rüping,
2009) have the benefit that patterns can be discovered
based on how ‘interesting’they are, for example
based on how accurate the pattern is (ratio between
true positives and false negatives) or how many
instances it applies to. Typically, computer science
techniques have been developed in the context of
large datasets with many possible patterns to
explore, as it is not always clear which patterns can
be expected a-priori. From position tracking data,
many features can be derived resulting in a multitude
of features. Therefore, the data mining tools from
computer science are well-suited to deal with the
complexity of position tracking data.
One could argue that unlocking the full potential
of big data for sports –science and practice –
requires bringing the two domains, and thus two
distinctly different paradigms, together, as their
contributions can be regarded complimentary.
Doing so however, requires one to understand the
challenges and opportunities of a multidisciplinary
interplay between the domains of sports- and com-
puter- science (Rein & Memmert, 2016). Several
authors have addressed this question in previously
published narrative studies: Rein and Memmert
(2016) have discussed the potential of applying
big data in tactical analysis, but also discussed
how it challenges the methodological approaches
native to sports sciences. Memmert et al. (2017)
have applied techniques from both domains to a
position tracking dataset of one professional match
to illustrate the potential of using contributions
from both domains. Gudmundsson and Horton
(2017) have provided an overview of –mostly –
computer science techniques available in sports for
the study of spatiotemporal behaviour. Stein et al.
(2018)have described the entire process from
data acquisition, to storage, to ultimately analysis
and interpretation, in an attempt to provide an
overview of different segments of the process of uti-
lizing big data for performance analysis. Although
these studies all refer to challenges as well as the
potential of multidisciplinary collaboration, none
of these studies actually put the contributions
from both domains into one framework nor do
they discuss the operationalization of such a
collaboration.
The integration of fundamental computer science
work into applied settings (i.e. data science) has
been discussed in other applied domains, illustrating
the benefits of integrating these techniques in differ-
ent settings. Gandomi and Haider (2015) have dis-
cussed the challenges and opportunities of applying
big data in general, while more specific examples of
integrating computer science techniques in specific
settings outside of sports include forecasting and
pattern mining of financial time-series in economics
(Cao & Tay, 2003), development of individual
video recommendation systems in media and enter-
tainment (Davidson, 2010), and spatiotemporal
analysis of geographical data in geographic and
earth sciences (Peuquet & Duan, 1995). These
examples illustrate that application of techniques
from computer science can support analysis and
innovation in other areas. With the current review,
we aim to outline a framework that integrates contri-
butions from the domains of sports science and com-
puter science in the study and analysis of tactical
Unlocking the potential of big data to support tactical performance analysis 3

behaviour in soccer using position tracking data, and
discuss the additional insights that can be gained
from this integration. We specifically focus on the
identification of challenges and opportunities with
regard to the utilization of expertise from the
domains of sports science and computer science, as
both domains benefit from a conceptual model that
outlines where each domain complements the other
in analysing tactical behaviour in soccer using pos-
itional tracking data.
2. Methods
2.1. Literature search
A systematic review of the available literature was
conducted according to PRISMA (Preferred Report-
ing Items for Systematic reviews and Meta-analyses)
guidelines (Moher et al., 2015). A literature search
was conducted on 14 June 2019 to identify studies
that report the use of position tracking data to
analyse tactical behaviour in soccer (Figure 2).
Specifically, the following electronic databases were
searched: Science Direct, Dimensions, Computer
Science Bibliographies, PubMed, Scopus, ACM
Digital Library, IEEE Xplore.
Titles and/or abstracts of all records in an elec-
tronic database were searched for the combination
of the following search terms: soccer OR football
AND tactic∗OR strateg∗OR formation∗OR inter-
∗player OR inter∗team OR spatio∗temporal NOT
robo∗.
Furthermore, additional studies to consider were
identified by manually searching the reference lists
of included papers.
2.2. Study selection
To be considered for this review, studies had to
concern tactical behaviour and meet the inclusion cri-
teria outlined in Table I. For the purpose of this
review, tactical behaviour was defined as how a
team or individual manages its spatial position over
time to achieve a shared goal (i.e. scoring), while
adapting to, and interacting with the opponent
under constraints of the conditions of play (Gré-
haigne et al., 1999). We operationalized this by
searching for studies that at least included data and
analysis on the interactions in space and time on
the inter-team as well as intra-team level.
The first author conducted the first selection based
on titles and abstracts conducted by the first author.
Any study that clearly not met the inclusion criteria
was excluded at this stage. When a confident decision
based on the title and abstract could not be made, the
study was included for full-text analysis. Next, the eli-
gibility for inclusion was assessed based on analysis of
full-text papers by the first author of this review. The
final selection was then validated by at least one of the
co-authors. Any ambiguities regarding the inclusion
of papers of the review until consensus was reached.
2.3. Data extraction
All included studies were classified as sports science
(1) or computer science (2) based on the journal or
conference they were published in, as well as the
associated keywords. Next, information on data col-
lection was extracted. To review the contributions
of all studies to the components of feature construc-
tion and modelling & analysis (Figure 1), we
extracted data on the spatial aggregation features,
window selection, and techniques applied for analy-
sis. Furthermore, data was extracted on the link
with match performance, the problem definition or
aim of the study, and the inclusion of a theoretical
definition of tactical behaviour to review the inter-
pretability of all included studies. Finally, all findings
were categorized and put into a single framework
(Figure 2), that will serve as the context for the dis-
cussion of our findings, and as a proposed structure
for the utilization of expertise from the domains of
sports science and computer science in the study
and analysis of tactical behaviour. All data extraction
Table I. In- and exclusion criteria for the systematic literature search.
Inclusion criteria Exclusion criteria
.Published in the last 15 years
.Full-text publication English
.Published as a peer-review journal or conference paper
.Tactical analysis based on position tracking data (LPM, GPS or Optical Tracking)
.Data collected in matches or SSGs
.Data collected in soccer
.No full-text available (in English)
.Analysis based only on notational data
.Data collected in futsal
.Data available for only one team
.Data available of less than two players
Notes: LPM, Local Position Measurement system with Radio Frequency Identification (RFID) (Frencken et al., 2010); GPS, Global
Positioning System; SSGs, Small-sided games.
4F. Goes et al.

was based on full-text assessment by the first author
of this review. Data extraction tables (Supplementary
Data) were developed based on consensus between
all authors.
3. Results
The initial database search returned 2290 records to
be considered for inclusion. An additional 48 papers
were identified based on manual inspection of the
reference lists of already included papers (see ‘Identi-
fication’in Figure 1). As a result, a total of 2338
records were screened based on title and abstract,
of which 146 were considered for full-text assessment
(see ‘Screening’in Figure 1). After full-text assess-
ment, 73 records were excluded because they did
not meet our inclusion criteria (see ‘Eligibility in
Figure 1). The remaining 73 records (Aguiar, Gon-
çalves, Botelho, Lemmink, & Sampaio, 2015;
Andrienko et al., 2017; Aquino et al., 2016a,
2016b; Baptista et al., 2018; Batista et al., 2019;
Barnabé, Volossovitch, Duarte, Ferreira, & Davids,
2016; Bartlett, Button, Robins, Dutt-Mazumder, &
Kennedy, 2012; Bialkowski et al., 2014a,2014b,
2014c,2016; Castellano, Fernandez, Echeazarra,
Barreira, & Garganta, 2017; Chawla, Estephan, Gud-
mundsson, & Horton, 2017; Clemente, Couceiro,
Martins, Mendes, & Figueiredo, 2013a,2013b,
2014; Couceiro, Clemente, Martins, & Machado,
2014; Coutinho et al., 2017,2018; Duarte et al.,
2012,2013a,2013b; Fernandez & Bornn, 2018; Fig-
ueira, Gonçalves, Masiulis, & Sampaio, 2018; Filetti,
Ruscello, D’Ottavio, & Fanelli, 2017; Folgado, Gon-
çalves, Abade, & Sampaio, 2014a; Frencken et al.,
2011; Frencken, De Poel, Visscher, & Lemmink,
Figure 1. Flowchart of systematic literature search (conform PRISMA guidelines) where the number of included studies during each of the
stages of the search process is shown. The main reasons for exclusion based on full-text assessment, as well as the number of included studies
are shown at the bottom.
Unlocking the potential of big data to support tactical performance analysis 5

2012; Frencken, van der Plaats, Visscher, &
Lemmink, 2013; Frias & Duarte, 2014; Gonçalves,
Figueira, Maçãs, & Sampaio, 2014; Gonçalves
et al., 2017a,2017b; Gonçalves, Marcelino, Torres-
Ronda, Torrents, & Sampaio, 2016; Grunz,
Memmert, & Perl, 2012; Gudmundsson & Wolle,
2010; Janetzko et al., 2014; Janetzko, Stein, Sacha,
& Schreck, 2016; Knauf, Memmert, & Brefeld,
2016; Link, Lang, & Seidenschwarz, 2016;
Machado et al., 2017; Memmert et al., 2017;
Memmert, Raabe, Schwab, & Rein, 2019; Moura,
Barreto Martins, Anido, De Barros, & Cunha,
2012; Moura et al., 2013,2016; Olthof, Frencken,
& Lemmink, 2015,2018,2019; Power, Ruiz, Wei,
& Lucey, 2017; Ramos, Lopes, Marques, & Araújo,
2017; Rein, Raabe, & Memmert, 2017; Ric et al.,
2017; Sampaio, Lago, Gonçalves, Macas, & Leite,
2014; Sampaio & Macas, 2012; Siegle & Lames,
2013; Silva et al., 2014a,2014b,2015,2016a,
2016b; Spearman, Basye, Dick, Hotovy, & Pop,
2017; Stein et al., 2015,2016; Travassos, Gonçalves,
Marcelino, Monteiro, & Sampaio, 2014; Vilar,
Araujo, Davids, & Bar-Yam, 2013,2014a,2014b;
Wei, Sha, Lucey, Morgan, & Sridharan, 2013; Yue,
Broich, Seifriz, & Mester, 2008a,2008b;Zhang,
Beernaerts, Zhang, & de Weghe, 2016) were
Figure 2. Conceptual framework for the combination of sports science (translucent red bars) and computer science (translucent blue bars)
expertise in the study of tactical behaviour in soccer. Based on the results from the current systematic review. Bars with percentage represent
the relative occurrence of a certain method or feature within a domain. Abbreviations: SSG, Small-Sided Games; LPM, Local Position
Measurement.
6F. Goes et al.

included for analysis in the review. Of the included
papers, 54 (74%) were qualified as sports science
papers and 19 (26%) as computer science papers.
Below, we will describe the results of our systema-
tic analysis of the literature. We examine various cat-
egories, including: Problem Definition, Data
Collection, Spatial Aggregation, Temporal Aggrega-
tion, and Modelling & Interpretation. We analyse
the included studies numerically, by describing how
often various categories occur. Moreover, we sum-
marize the different categories in a visual framework
where we combine the expertise from sports- and
computer-science domains (see Figure 2). This
figure will be used as a guide to explain the body of
literature that encompasses the study of tactical be-
haviour. Full details and data extracted from the
included studies can be found in the supplementary
data.
3.1. Problem definition
In most included sports science studies, research
questions were driven by theoretical or practical
domain expertise from for example, physiology, be-
havioural science or psychology. Studies frequently
aimed for practical implications, and study designs
and data collection result from the research question.
When looking at the problem definitions and aims of
the included sports science papers, 63% studied the
effect of an intervention on tactical behaviour, as is
illustrated by the work of Olthof et al. (2018,2019),
who studied the effect of manipulating pitch sizes
on tactical behaviour in different age groups, and
the work of Gonçalves et al. (2016,2017a,2017b),
who studied the effect of numerical imbalance
between teams on tactical behaviour (Gonçalves
et al., 2017a,2017b; Ric et al., 2017). Twenty
percent studied a variable/method to quantify tactical
behaviour, as is illustrated by the work of (Link et al.,
2016), who conceptualized a new feature called “dan-
gerousity”to quantify offensive impact. Finally, 17%
studied the relationship between variables (see
‘Problem Definition’in Figure 2), as for example
illustrated in the work of Rein et al. (2017) who
studied the relation between pass effectiveness quan-
tified by the change in space control and number of
outplayed defenders and success in 103 Bundesliga
games (Rein et al., 2017).
In most included computer science studies on the
other hand, research questions were driven by theor-
etical and methodological domain expertise from for
example computer sciences, mathematics or data
science. These studies frequently aimed for new
methodological approaches and techniques rather
than practical implications. Furthermore, in many
cases the design could be considered data-driven:
rather than formulating hypotheses based on theory
and collecting data in an experimental set-up to test
these hypotheses, studies used large sets of available
data and generated hypotheses from the data. When
looking at the problem definitions of these studies,
5% studied the effect of an intervention or constraint,
as there is the work by Bialkowski et al. (2014a,
2014b,2014c), studying the impact of home-advan-
tage on the dynamic formation of a team on the
pitch. The majority (84%) of computer science con-
tributions however, studied a new technique or
model (mostly classification or clustering problems),
like the work by Fernandez and Bornn (2018), who
proposed an improved model for measuring space
control, the work by Andrienko et al. (2017), propos-
ing a new feature to quantify pressure on a player, or
the work by Bialkowski et al. (2014a,2014b,2014c)
and the work by Grunz et al. (2012) proposing new
methods to identify patterns and formation in the
data (Bialkowski et al., 2014a,2014b,2014c; Grunz
et al., 2012). Finally, 11% studied prediction or prob-
ability problems, as illustrated in the work by Spear-
man et al. (2017), or Chawla et al. (2017), who
proposed models to predict if a pass would arrive at
a team-mate or not (Chawla et al., 2017; Spearman
et al., 2017) (see ‘Problem Definition’in Figure 2).
3.2. Data collection
The type, quality, and quantity of data strongly
influences the research questions that can be
answered within the study of tactical behaviour, as
well as the approach that can be used (see ‘Data
Collection’in Figure 2). Most studies (64%) used
optical tracking data as this is the system of
choice in many professional competitions. As
opposed to LPM and GPS systems, optical tracking
systems typically allow tracking of the ball.
However, they are also known to have a lower accu-
racy in comparison to wearable tracking devices,
especially LPM (Frencken, Lemmink, & Delleman,
2010). Work by Mara, Morgan, Pumpa, and
Thompson (2017) revealed optical tracking
systems suffer measurement errors in the range of
−2.5 m–2.5 m in measuring covered distance on
20–100 m (change of direction) runs (Mara et al.,
2017). Although these errors could limit the use
of optical tracking data for the analysis of physical
performance, the subsequent errors of 0–0.5 m in
measuring position still allow for accurate assess-
ment of tactical behaviour, as the error margin is
small enough for data to still represent actual pos-
itions. Only a minority (18%) of the studies used
ball tracking, and a much larger part of the
Unlocking the potential of big data to support tactical performance analysis 7

studies (42%) used the more time-consuming nota-
tional event data to study ball events. Sensor
systems (36%) and experimental designs (48%)
like small-sided games (SSGs) were exclusively
used in sports science studies. As sensor systems
do not allow ball tracking, event-based analyses
are impossible without notational event data
(Figure 2).
3.3. Spatial aggregation
Tracking the X and Y position of 22 players and the
ball 1–100 times a second results in sizeable
amounts of data, even for one match, as well as a
high complexity as the 22 degrees of freedom of the
system allow for numerous potential interactions.
Therefore, most studies aggregate raw position data
by reducing the spatial positions of all players into
spatial features. More specifically, spatial aggregation
refers to the process of constructing features that
capture group-level behaviour per timeframe and
allow one to derive contextual meaning, as these fea-
tures reduce the system’s complexity to an interpret-
able level (see ‘Spatial Aggregation’in Figure 2).
These features can be constructed at the macro
level (full team), as for example in work by Frencken
et al. (2012), who aggregated the positions of the
team into one team cerntroid, at the micro-level (sub-
groups of at least two players), like in the work by
Memmert et al. (2017), who aggregated the positions
of a subgroup (e.g. defensive line) into a line centroid,
or even at the level of the individual, as in the work by
Olthof et al. (2015), who measured the average dis-
tance of all players to the team centroid (e.g. stretch
index). Furthermore, combinations of spatial aggre-
gates can be used to construct composite measures
of spatial (sub-)group interactions, as for example
presented in the work by Goes, Kempe, Meerhoff,
& Lemmink, 2019, who constructed a measure of
pass effectiveness by using line centroids, team
spread and team surface areas. Most sports science
studies (84%) used some form of spatial aggregation,
most frequently (57%) centroid related features
(Frencken et al., 2011; Yue et al., 2008a,2008b), fol-
lowed by team surface areas and spread (Moura et al.,
2012) (46%), length and width (Folgado, Lemmink,
Frencken, & Sampaio, 2014b) (30%), and space
control (Rein et al., 2017) (7%). Distribution
amongst computer science studies is somewhat
similar, with 58% of the studies using spatial aggre-
gates, specifically centroid features (32%), length
and width (11%) and space control (11%).
However, as data mining techniques can directly
be applied to the positional data without aggregating
it into features, a small minority of the sports science
studies (16%), and nearly half of the computer
science studies (42%) do not use spatial aggregation.
In these cases, patterns in the raw data can, for
example, be detected using unsupervised machine
learning techniques like clustering, as is illustrated
by the work of Grunz et al. (2012), Knauf et al.
(2016), and Machado et al. (2017), who all mine pat-
terns in the data by clustering the raw positions in
some way (Grunz et al., 2012; Knauf et al., 2016;
Machado et al., 2017). Furthermore, machine learn-
ing techniques also allow for the inclusion of many
features and studying their non-linear relationships,
like there is the work by Power et al. (2017), and
Spearman et al. (2017), who model pass risk and
reward and the probability of a pass arriving and
include a multitude of features (Power et al., 2017;
Spearman et al. 2017). In many of these computer
science contributions, the algorithm does feature
selection automatically. The main benefit of this is
that instead of creating features based on a-priori
assumed relationships between entities, (hidden)
relationships can be uncovered from the data. As fea-
tures are not created and selected based on expec-
tations of the user, but rather based on their
importance in the algorithm, they could prove to be
a better depiction of patterns in the data.
3.4. Temporal aggregation
To extract information, statistically compare, or
model time-series of either raw data or aggregated
spatial features, data needs to be aggregated within
the temporal domain as well (see ‘Temporal Aggrega-
tion’in Figure 2). Temporal aggregation refers to the
summation of data over a given time-window, by for
example computing the mean value of a given feature.
We consider three different methods for temporal
aggregation: first of all, data can be aggregated (e.g.
averaged) over time windows with a fixed size, inde-
pendent of the context of the game (Sampaio &
Macas, 2012). In such methodologies, for example,
data is aggregated over the course of a half or full
match, or another time window with a fixed duration.
Secondly, data can also be aggregated over a window
with a fixed size that is linked to match events. An
example is looking at the 3 s following a pass (Goes
et al., 2019), or the 30 s before a goal (Frencken
et al., 2012). Finally, data can be aggregated over
windows with a flexible size. In these cases,
windows are always linked to events with variable
durations like a sequence of passes or running trajec-
tories (Rein et al., 2017; Spearman et al. 2017). The
majority of sports science studies (60%) utilized fixed
windows in which they often aggregate spatial data
over the course of a full SSG or match, while only a
minority aggregates over fixed (9%) or flexible
8F. Goes et al.

(24%) event-based windows. However, the majority
of computer science studies aggregated over fixed
(26%) or flexible (42%) event-based windows, and
only a minority (32%) aggregated over fixed
windows independent of context.
3.5. Modelling & interpretation
Most included sports science studies utilized statisti-
cal models, and models rooted in the dynamical
systems theory like relative-phase (Palut & Zanone,
2005) and entropy (Pincus, 1991,1995) analyses
that allow for time-series analysis. These models are
generally based on linear relationships and allow
comparison of multiple conditions, the study of
relationships between variables, and testing specific
hypotheses. Furthermore, they are interpretable on
the level of individual features. Most computer
science studies on the other hand used methods
that are in comparison computationally complex
(i.e. require more computations and therefore more
processing power), like various machine learning
approaches. These approaches allow the study of
(non-) linear complex relationships amongst many
different features and the discovery of hidden pat-
terns in the data, but require specific (programming)
skills and often high-performance computing clus-
ters, and can be harder to interpret, especially
without the methodological domain expertise.
To be able to interpret the practical impact of a
study on behaviour, it needs to be clear what (tactical)
behaviour was actually studied, and how changing
this behaviour impacts performance (see ‘Modelling
& Interpretation’in Figure 2). Only 19% explicitly
defined tactical behaviour, of which only one study
Janetzko et al. (2014) was classified as a computer
science study. Analysing the extracted definitions,
three common elements were identified: Tactical
performance/behaviour refers to (1) the dynamic
positioning and organisation in space and time, of a
team and its players on the pitch, in interaction with
and adapting to the movement of the ball, (2) move-
ment of the opponents, and conditions of play, (3)
and constitutes more than just the sum of individual
parts. As according to these criteria tactical behaviour
is emergent, it cannot be studied by breaking down
the behaviour of a team into 11 individual parts and
analysing them separately, as behaviour is the result
of interaction. Furthermore, only 30% used match
performance indicators (e.g. outcome, shots on
goal) in their study of tactical behaviour. Most
(86%) investigated the link between tactical features
and match performance using performance indi-
cators related to shots or goals. Interestingly, there
is little consensus on the relation of most tactical fea-
tures with performance (outcome). On the one hand,
studies that investigated the link between often-used
tactical features like the team-centroid did not find
a clear relationship with offensive events and per-
formance (Bartlett et al., 2012; Frencken et al.,
2012). On the other hand, authors who used more
complex tactical features like the team surface area
or spread (Moura et al., 2012,2016), or composite
features related to passing (Rein et al., 2017; Spear-
man et al. 2017) did report some relationship with
performance. These rather inconsistent reports on
the effect of tactical features on performance, as
well as the large variety of possible tactical features
to analyse, highlight how difficult it is to uncover
and interpret consistent and generalizable patterns
in tactics.
4. Discussion
With this review, we aimed to put the contributions of
sports and computer science to the analysis of tactical
behaviour in soccer using position tracking data into
perspective. Both domains contributed significantly
to the study of tactical behaviour, and provide a set
of unique approaches towards analytics. Our results
show that there are considerable differences in meth-
odology. We propose that both domains benefit from
a cyclical collaboration and embedding each other’s
domain expertise. Therefore, we provide a frame-
work for optimizing this collaboration by linking the
contributions from both domains to different parts
of the analytical process that entails the analysis of tac-
tical behaviour using position tracking data (Figure 2).
Our framework could support the field of sports ana-
lytics and specifically the analysis of tactical behaviour,
and result in a better translation to practice.
We have argued in our introduction that research
from sports science and research from computer
science is characterized by distinctly different, and
to some extent contrasting research paradigms. Our
results have revealed that this was also true for
research specifically concerning the study of tactical
behaviour using position tracking data. The sports
science studies we have included in this review were
predominantly characterized by deductive reasoning
in which hypotheses were formed based on theory,
and tested in mostly experimental settings. This is
clearly illustrated by many of the included sports
science works, like those by Aguiar et al. (2015), Bap-
tista et al. (2018), Coutinho et al. (2017,2018),
Duarte et al. (2012), Frencken et al. (2011,2013),
or Olthof et al. (2015,2018), who all presented a
theoretical framework to study and understand tacti-
cal behaviour that is rooted in the dynamical systems
theory (Aguiar et al., 2015; Baptista et al., 2018,
2019;Coutinhoetal.,2017,2018;Duarteetal.,
2012;Frenckenetal.,2012,2013; Olthof et al., 2015,
Unlocking the potential of big data to support tactical performance analysis 9

2018), and specifically designed experimental set-ups
with small-sided games to analyse behaviour against
the backdrop of this framework. The aims of these
sports science studies are generally focused on advan-
cing our understanding of tactical behaviour, and
applying the findings in practice to for example
improve training design or talent identification and
development. This is illustrated in studies like those
by Gonçalves et al. (2016,2017a,2017b), who
studied the impact of numerical imbalance and
spatial constraints on tactical behaviour in small-
sided games, to optimize training design (Gonçalves
et al., 2016,2017a,2017b). Or the work by Olthof
et al. (2015,2018,2019), who studied the impact
of field size on tactical behaviour in small-sided
games and compared that behaviour to behaviour
seen in a real match, to find out what design would
be the best format to improve match performance.
The included computer science studies on the
other hand, provide a very different perspective.
The studies we included from this domain generally
do not present any theoretical context to explain tac-
tical behaviour, nor do they contain hypotheses about
what this behaviour would look like or how teams or
players would react to certain manipulations or
stimuli. We would like to argue that based on our
findings, this is not necessarily a shortcoming but
rather a matter of a different aim and perspective.
Rather than aiming for an increased understanding
and practical implications in sport, the computer
science studies we included were typically focussed
on advancing methodology and computational tech-
niques for data processing, modelling and extraction
of information by means of inductive designs that
centre on data mining, feature extraction and visual
analysis. This is illustrated by for example the work
of Bialkowski et al. (2014a,2014b,2014c,2016),
and Wei et al. (2013), who presented new methods
to detect formations and identify positional roles
based on data based on large observational dataset
collected in competition. Or the work of Stein et al.
(2015,2016), and Janetzko et al. (2014,2016), who
presented a data visualization and exploration tech-
niques that aim to optimize the workflow of video-
analysts in professional soccer organizations
(Janetzko et al., 2014,2016; Stein et al., 2015,
2016). Or the work of Chawla et al. (2017), who pre-
sented a model to accurately classify successful and
non-successful passes based on data. None of these
works extensively discus practical applications,
explain the findings based on a theoretical under-
standing of tactical behaviour or advance our under-
standing of behaviour, have experimental designs or
result in direct practical implications on the level of
training and performance. However, this is by
design, as these contributions all aimed to propose
new techniques, features and data processing and
visualization routines instead.
The distinct difference in contributions from both
domains to the research on tactical behaviour is also
confirmed by other recent review studies on similar
topics. In systematic reviews characteristic for sports
science like those by Sarmento et al. (2014) and
Ometto et al. (2018), the focus is on how position
tracking data can be used to analyse performance
and monitor loading, or how to manipulate small-
sided games to change behaviour. On the other
hand, in typical computer science survey papers like
the one by Perin et al. (2018), Gudmundsson and
Horton (2017) and Stein et al. (2017), the focus is
more technical, discussing topics from data manage-
ment to visualization and how to develop analytical
tools. Given the fundamental differences in expertise
and methodology, collaboration between both
domains can therefore be regarded a key challenge.
Most studies included in this review fit well into
one end of the sports science –computer science
spectrum, and collaborations between domains are
still relatively sparse. However, we have also included
multiple studies that gravitate towards the middle of
the spectrum and illustrate the added benefit of a
synergy between both domains. The studies by Link
et al. (2016), Rein et al. (2017), and Goes et al.
(2019), are examples of sports science work that uti-
lizes observational designs in which large datasets
were collected in competition and used for the devel-
opment and validation of new features that assess
some aspect of performance (Goes et al., 2019;
Link et al., 2016; Rein et al., 2017). Although in
these studies most involved scientists had a back-
ground in sports science, at least some of them also
had a background computer science helping them
applying computer science techniques for data pro-
cessing, visualization and analytics coming from
domains like mathematics, data mining and
machine learning, and information processing.
Despite their methodology, these studies were still
classified as sports science as their aim was not
necessarily the sole development of a new approach
or technique, but rather the validation of these
approaches by studying their relation to successful
performance and applying the approach for the
purpose of performance analysis. The work by Goes
et al. (2019) for example resulted in a new metric to
quantify the effectiveness of a pass that was con-
structed using clustering techniques and then
applied for player evaluation purposes, while the
work by Rein et al. (2017), was focussed on applying
multiple metrics that assess pass effectiveness by
studying their relation to offensive performance.
As we identified several sports science studies that
utilized techniques from other domains to advance
10 F. Goes et al.

their research, we also identified multiple computer
science studies that did the same. The studies by
Power et al. (2017), Spearman et al. (2017),
Andrienko et al. (2017) and Fernandez and Bornn
(2018) can all be regarded as examples of studies
that predominantly involved expertise from computer
and data science, but who also involved domain
expertise from sports (science) (Andrienko et al.,
2017; Fernandez and Bornn, 2018; Power et al.,
2017; Spearman et al. 2017). These studies focussed
on feature development and modelling, as they con-
structed models for the assessment of pass risk and
reward, pressure, space control and pass probability.
Different to the sports science examples mentioned
before, the scope of these studies was methodologi-
cal, yet they typically validated their approach and
its assumed relation to performance based on
domain expertise, and provided several examples of
practical use cases based on data collected in compe-
tition. These examples from sports science and com-
puter science studies that utilize expertise from other
domains illustrate the additional benefits that can be
gained and can in some ways be regarded as tem-
plates for future collaborations.
The included studies are illustrative of collabor-
ations between the domains of computer science
and sports science suggest contributions from both
domains are compliant rather than concomitant.
We therefore propose that collaboration between
sports science and computer science in the process
of studying tactical behaviour using position tracking
data should be a cyclical rather than a parallel one.
Sports science tests theory and translates practical
problems into research questions. By applying tech-
niques from computer science to sports science
research designs one could come to different
answers to research questions. These answers might
differ in the sense that sports scientists could assess
different aspects of performance, but they could
also differ in the sense that these methods allow for
a more in-depth answer. The other way around
research questions deduced from theory and obser-
vation by sports science, can be used by computer
science to define the scope of their search for, and
development of appropriate technologies to derive
information from position tracking data. Computer
science provides the tools to gain in-depth knowledge
and enables sports science to test increasingly
complex hypotheses and ask new questions. As both
domains bring relevant expertise in relation to con-
ducting and interpreting tactical analyses, we
propose that impactful analytics relies on the combi-
nation of expertise from both domains.
The quality (i.e. accuracy, sampling frequency,
inclusion of ball data) and quantity of available data
have a big impact on most types of research and
cannot be ignored in any discussion of sports ana-
lytics. Due to technological advancements, lowers
costs, and growing interest (Rein & Memmert,
2016), we have seen an increase in the availability
and quality of data in soccer, similar to big data devel-
opments in other areas, providing numerous oppor-
tunities (Gandomi & Haider, 2015), like opponent-
analysis, scouting and performance optimization on
a team and individual level. However, based on our
results, these opportunities only seem to be seized
to a limited extent. Most sports science studies are
characterized by experimental set-ups in which
small samples of data are collected in a specific popu-
lation, to answer a predetermined research question
(Olthof et al., 2015; Travassos et al., 2014). Although
this kind of research has allowed us to draw general
inferences about what drives tactical behaviour of
groups, the small sample sizes and highly specific cir-
cumstances that are often different from a real match
also limit the use of findings from these studies in
real-life tactical analysis. As tactical behaviour is
highly dependent on the context (Gréhaigne et al.,
1999; Rein & Memmert, 2016), larger real-life datasets
collected in actual competitive matches in combination
with methodology that enables capturing complex pat-
terns might allow one to draw conclusions about per-
formance with a stronger ecological validity. Of
course, causation and correlation should not be con-
fused, but with large enough datasets, the discovered
patterns carry some weight and at the very least
provide a good basis for developing new theories that
can be further examined in more controlled settings.
On the other hand, handling and analysing much
larger datasets challenges back-end processes (i.e.
storing, pre-processing andquerying)andanalytics
(i.e. aggregation and feature construction) that are
not typically addressed by sports science research,
and can thus be regarded a key challenge. The
domain of computer science typically focuses on tech-
nological developments within these processes, and
collaboration could advance the ability of sports
science to work with increasingly large datasets.
As illustrated by the results in this review, the
majority of sports science studies utilizes low-level
(simple to compute and high reduction of complex-
ity) spatial features like the team centroid (Folgado
et al., 2014; Yue et al., 2008a,2008b), that aim to
capture group-level behaviour in one feature. The
computation of these features is relatively easy, and
their computational cost is low, yet as illustrated by
the results, they have limited value. Features like
the team centroid have often been developed to
study tactics in small-sided games, but seem incap-
able of fully capturing the complex dynamics of an
11-a-side match (Goes et al., 2019). Combining
computer science expertise on for example data
Unlocking the potential of big data to support tactical performance analysis 11

mining and machine learning, with sports science
theory provides many opportunities to innovate in
this aspect. A potential example could be applying
the work of Bialkowski et al. (2014a,2014b,2014c,
2016), that has resulted in methodology to automati-
cally and dynamically identify formations and pos-
itional roles. Applying this method in sports science
research like that of Memmert et al. (2017), Goes
et al. (2019) or Siegle and Lames (2013), who all
use line centroids in which the lines are based on
manual annotation of fixed positional roles, could
lead to different answers and new insights. The
other way around, applying the theoretical framework
of dynamical systems theory that is presented in for
example the sports science work by Frencken et al.
(2012,2013), to feature construction in computer
science work like that on quantifying pressure by
Andrienko et al. (2017), could lead to advanced
methods that use coupling between features and
movement synchrony of players to quantify pressure,
defensive strategies and off-ball performance of
offensive players. These are typical examples of cycli-
cal collaboration. The outcome of a collaboration like
this would for example allow one to innovate the way
we analyse the performance of a team during the
game, to support decision-making by the coach in
near-time, to analyse the opponent before the
match by studying patterns that characterise their
successful attacks, or to identify specific patterns to
emphasize and train in the own team.
Ultimately, spatial features –no matter their com-
plexity –hold little meaning when aggregated over a
full match, and temporal aggregation is essential to
place spatial behaviour in a temporal context (Gré-
haigne et al., 1999; Rein & Memmert, 2016). Most
included sports science studies aggregated over fixed
windows independent of game-context, like a match
or half (Duarte et al., 2013a,2013b; Gonçalves et al.,
2017a,2017b), which limits interpretability. We argue
that deriving meaning from spatial features requires
the use of event-based time-windows, which is more
common in computer science studies (Andrienko
et al., 2017;Chawlaetal.,2017; Fernandez and
Bornn, 2018), as using event-based time-windows
allows one to draw conclusions about for example a
pass, dribble or set-piece. On such a small timescale,
it is much easier to find structural patterns than on
theleveloftheentiregame.Thisinturnwouldallow
one to answer questions like what defines an effective
attack, or successful dribble. Although this might
seem like another opportunity for sports science to
implement existing computer science expertise, this
one is less straightforward than spatial aggregation,
and adequate temporal aggregation can be regarded
as a key challenge. As time-series analysis is typically
challenging for most machine learning techniques
(Fu, 2011), and sport and behavioural sciences actually
have a lot of expertise in time-series analysis, one could
argue innovation here would definitively be on the brink
of interaction between both domains.
Despite the often underlined potential (Memmert
et al., 2017; Rein & Memmert, 2016; Stein et al.,
2017) of position tracking data to study tactical be-
haviour, in sports, and specifically in soccer, the
application is still relatively limited (Rein &
Memmert, 2016; Folgado et al., 2014). Our results
demonstrated the contributions to this topic have
increased substantially over the recent years, and
already resulted in an in-depth understanding of
tactics in soccer. However, so far, these studies have
had little practical impact, and the potential of pos-
ition tracking data does not seem to be fully utilized
so far. We argue that changing this requires domain
expertise from sports science as well as computer
science embedded within a multidisciplinary
approach, which is a key challenge for sports ana-
lytics. It also requires a clear link between method-
ology, findings and real-life performance (i.e.
answering the question “how does this help me/is
this related to winning the game?”asked by “prac-
tioners”). Understanding behaviour therefore
requires an approach that at least evaluates a certain
aspect within the context of others, as well as
answers the key performance question “how does
(changing) this behaviour impact our performance”.
With this systematic review, we provided an evalu-
ation of contributions from sports science and compu-
ter science to the study of position tracking data for the
purpose of tactical analysis in soccer, and we have
shown how an interplay between both domains could
results in innovative contributions to the field of
sports analytics. One major limitation of the current
review is its narrow scope, as we largely ignored essen-
tial components of the data analytics process like data
acquisition, storage, management, visualization, as
well as ethics and privacy issues (Perin et al., 2018;
Stein et al., 2017). However, doing so allowed us to
discuss the opportunities for position tracking data to
impact tactical behaviour, whereas previous reports
have merely touched upon its potential. This has
resulted in the discussion of a set of challenges con-
cerning the data analytics process, specifically feature
construction, spatial and temporal aggregation that
could be resolved by multidisciplinary collaboration,
which is pivotal in unlocking the potential of position
tracking data in sports analytics.
5. Conclusion
With this review, we have shown the considerable
opportunities for collaboration between sports
12 F. Goes et al.

science and computer science to study tactics in
soccer, particularly when using position tracking
data. Our systematic review highlights that sports-
and computer science research on tactical behaviour
contains distinctly different contributions. We pro-
posed a framework that could serve as the foundation
for the combination of sports science and computer
science expertise in tactical analysis. It has become
clear that the collaborations between both domains
benefit from a stronger dialogue yielding a cyclical
collaboration: sports science identifies problems and
tests theory hypotheses, computer science develops
robust techniques to solve such problems, and
sports science in turn adjusts theories and derives
practical implications from data by implementing
them.
Acknowledgements
This work was supported by grants of the Nether-
lands Organization for Scientific Research and
FAPESP (project title: “The Secret of Playing Foot-
ball: Brazil vs. The Netherlands”).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplemental data
Supplemental data for this article can be accessed here
https://doi.org/10.1080/17461391.2020.1747552.
Funding
This work was supported by grants of the Netherlands Organiz-
ation for Scientific Research (629.004.012-SIA) and FAPESP
(2016/50250-1, 2017/20945-0 and 2018/19007-9).
ORCID
F.R. Goes http://orcid.org/0000-0002-5995-3792
L.A. Meerhoff http://orcid.org/0000-0003-4386-
0919
F.A. Moura http://orcid.org/0000-0002-0108-
7246
S.A. Cunha http://orcid.org/0000-0003-1927-0142
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