Multivariate Analysis of the Offensive Phase in High-Performance Women's Soccer: A Mixed Methods Study

Abstract

Currently, there are still few studies on the tactical and contextual criteria that determine
offensive success in women’s soccer. The objectives of this study were to discover contextual and
tactical criteria that show an association with success in elite women’s soccer and to establish a
multivariate predictive model for the offensive phase. For this, 2323 ball possessions were analyzed
in FIFAWomen’sWorld Cup 2019 via direct observation. In addition, eight semi-structured interviews
were conducted with women’s soccer coaches and players. For direct observation, a bivariate analysis
between the analysed criteria and possession’s outcome was suggested. Additionally, a multivariate
predictive analysis was proposed via a decision tree method. To analyze the interviews, a sequential
analysis of delays and polar coordinates analysis were carried out. It was established a multi-variant
model of offensive success based on possession zone (middle offensive), initial offensive intention
(progress) and start zone (preoffensive or offensive) criteria. The probability of offensive success
was 75.2% interactively between those criteria. In addition, the analysis of the interviews allowed
us to conclude that criteria related to technical–tactical performance, decision-making and physical
capacities of the players will be decisive in increasing the probabilities of success.

Full text

sustainability
Article
Multivariate Analysis of the Offensive Phase in
High-Performance Women’s Soccer: A Mixed Methods Study
Iyán Iván-Baragaño 1, * , Rubén Maneiro 2, * , JoséL. Losada 3and Antonio Ardá1


Citation: Iván-Baragaño, I.; Maneiro,
R.; Losada, J.L.; Ardá, A. Multivariate
Analysis of the Offensive Phase in
High-Performance Women’s Soccer:
A Mixed Methods Study.
Sustainability 2021,13, 6379.
https://doi.org/10.3390/su13116379
Academic Editors: Antonio
Hernández-Mendo, Coral Falco,
Verónica Morales-Sánchez,
Cristina Menescardi and
Tomas Herrera-Valenzuela
Received: 1 May 2021
Accepted: 1 June 2021
Published: 4 June 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
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4.0/).
1
Department of Physical and Sport Education, University of A Coruña, 15179 A Coruña, Spain; ardasd@udc.es
2Department of Science of Physical Activity and Sport, Pontifical University of Salamanca,
37002 Salamanca, Spain
3Department of Social Psychology and Quantitative Psychology, University of Barcelona,
08035 Barcelona, Spain; antonio.arda@udc.es
*Correspondence: iyan.ivan@udc.es (I.I.-B.); rubenmaneirodios@gmail.com (R.M.)
Abstract:
Currently, there are still few studies on the tactical and contextual criteria that determine
offensive success in women’s soccer. The objectives of this study were to discover contextual and
tactical criteria that show an association with success in elite women’s soccer and to establish a
multivariate predictive model for the offensive phase. For this, 2323 ball possessions were analyzed
in FIFA Women’s World Cup 2019 via direct observation. In addition, eight semi-structured interviews
were conducted with women’s soccer coaches and players. For direct observation, a bivariate analysis
between the analysed criteria and possession’s outcome was suggested. Additionally, a multivariate
predictive analysis was proposed via a decision tree method. To analyze the interviews, a sequential
analysis of delays and polar coordinates analysis were carried out. It was established a multi-variant
model of offensive success based on possession zone (middle offensive), initial offensive intention
(progress) and start zone (preoffensive or offensive) criteria. The probability of offensive success
was 75.2% interactively between those criteria. In addition, the analysis of the interviews allowed
us to conclude that criteria related to technical–tactical performance, decision-making and physical
capacities of the players will be decisive in increasing the probabilities of success.
Keywords:
technical–tactical performance; women’s soccer; opposition sports; observation; mixed
methods; ball possession
1. Introduction
Soccer is the most played sport in the world. For this reason, it is also the sport that
has awakened the most interest in the field of research [
1
]. Its popularity has meant that,
over the last few decades, studies of different aspects of the game have created a theoretical
and scientific framework of great value for professionals in this sport. On the other hand,
it is only since the beginning of this century that scientific publications about women’s
soccer have begun to increase [
2
]. This lack of publication has conditioned the sporting
development of this modality, forcing trainers and researchers to resort to using the results
taken from the male equivalent, despite the differences in the development of the game
between the two sexes [2,3].
In the last few years, several authors have developed scientific knowledge about soccer
played by women and the variables of the game that shape performance in this sport [
3
].
Research is mainly related to the parameters concerning the physiology of female soccer
players [
2
], although these elements do not represent an approach to the tactical reality of
the game. Aspects related to time-motion during matches were studied by Hewitt, Norton
and Lyons [
4
], observing a decrease of approximately 5% in the total distance covered by
the players in the second halves. Bradley et al. [
5
] came to the same conclusion, along
with the observation that this represented a significant difference from men’s elite soccer.
Other research has focussed on the study of technical variables with the aim of establishing
Sustainability 2021,13, 6379. https://doi.org/10.3390/su13116379 https://www.mdpi.com/journal/sustainability

Sustainability 2021,13, 6379 2 of 16
differences between the two sexes [
5
,
6
], together with associating the appearance of these
technical elements of the game with success. Along the same lines as De Jong et al. [
3
],
a recent ambitious study found that the most important element in achieving success is
scoring the first goal. These authors also observed that success is highly linked to indicators
of aggressive and physical play such as duels and aerial duels won [
3
] between women
soccer players. Kubayi and Larkin [
7
] analysed the FIFA Women’s World Cup France 2019
and observed that the winning teams had more ball possession time and made more passes,
which resulted in more goal opportunities.
For some time, most of the studies on performance indicators in soccer were carried
out from a quantitative perspective. More recently, some authors have tried to find out
the criteria that determine success in soccer based on qualitative methodology. Due
to the high complexity in soccer analysis, in recent years different studies have been
published using the integration of both methodologies as a basis, under the paradigm of
Mixed Methods [8–10].
Despite the growth in interest over the last few years, bibliography in relation to
achieving offensive success in women’s soccer remains scarce. In this sense, the main
success indicator in soccer is the goal [
11
,
12
] and, due to the little casuistry during the
development of a soccer match, it means that achieving this and creating goal opportunities
has fundamental value for technicians and players [13].
In men’s soccer, the association between contextual and tactical criteria and success
during the development of the game in the offensive phase has been studied over numerous
decades up to the present day. The contextual match status variable [
12
,
14
,
15
] proved to be
an important factor that modified aspects such as the possession zone. James, Mellalieu
and Holley [
16
] observed that teams moved the possession zone to more defensive areas
when they were winning, with the subsequent negative link to offensive success, as shown
by Casal et al. [
17
] later. In women’s soccer, it was also seen that this was a variable that
modified ball possession following analysis of the FIFA Women’s World Cup Canada
2015 [
18
]. The kind of start of possession and the zone in which it was produced proved to
be tactical indicators significantly linked to success in men’s soccer [
19
,
20
] and in women’s
soccer [
21
]. It was also shown that once possession of the ball was recovered, the offensive
tactical intention [
22
] significantly modified the development of offensive actions in men’s
soccer: the teams that showed a tactical intention to move quickly towards the rival goal
had a greater probability of finishing their offensive actions successfully. On the other
hand, the spatial context of interaction [
23
,
24
] proved to be a variable that explained the
achieving of high-value offensive situations when contexts were produced in which the
team initiating the offensive action did so with only the rival backfield between the ball
and the rival goal [24].
Other studies have attempted to quantify different variables related to the develop-
ment of offensive actions such as the time spent in offensive and defensive phase [
25
–
27
]
observing that the best teams were able to recover possession of the ball more quickly than
their opponents [
27
] independently of the match status. It was also shown that successful
offensive actions in soccer do not last long and involve a low number of passes [
28
]. On
the other hand, in terms of the dynamic of the game itself, there has been an attempt to
discover the influence of the rival team’s tactical defensive behaviour in achieving offen-
sive success, by analysing variables such as the defensive positioning of the opponent at
the beginning of the offensive action [
28
,
29
], the defensive intention [
25
,
30
] and the rival
defensive organisation [22].
Despite the existing evidence concerning the behaviour of the tactical and contextual
criteria and success in men’s soccer, women’s soccer is currently devoid of a high number
of references that would allow clear conclusions to be reached about this aspect [
31
]. In
spite of the increase over the last few years in publications that have allowed conclusions
to be made regarding technical [
3
,
5
–
7
] or tactical parameters [
18
,
21
,
32
], a more in-depth
analysis is needed of this soccer that has awakened so much interest in the last decade [
33
].

Sustainability 2021,13, 6379 3 of 16
Mixed Methods are currently presented as one of the most appropriate methodologies
to knowing the criteria that are associated with success in women’s soccer [
9
]. This
methodology was applied in this study in search of integration between qualitative and
quantitative perspectives [
10
], in the third paradigm of research [
34
] in a complementary
way to achieve the goals of this study. The integration of qualitative and quantitative
data from the mixed methods methodology allowed us to achieve the objective of this
study in a comprehensive way. Quantification of the data by itself is not enough to explain
the complex behavior in high-level soccer. Likewise, sole qualitative analysis can lead to
subjectivity. For these reasons, the application of mixed methods, with a robust quality of
the data supported by the experience of the observers [
35
], makes it possible to approach
the problem of soccer from a comprehensive perspective. Therefore, its use in the analysis
of soccer performance should be developed in the coming years.
For all this, the aims of this study are twofold: on the one hand, (i) to discover the
contextual and tactical criteria that show an association with success in elite women’s
soccer, and on the other hand, (ii) to establish a multivariate predictive model for the
offensive phase from tactical and contextual criteria. If these goals were met, coaches
and players will obtain relevant information on how to develop ball possessions in elite
women’s soccer.
2. Materials and Methods
2.1. Design
A Mixed-Method design was applied for this study from direct observation and
indirect observation [
35
]. From direct observation, ball possessions in the FIFA Women’s
World Cup France 2019 were analyzed. From indirect observation, eight semi-structured
interviews with female soccer coaches and players were analyzed.
Observational methodology was used in this study due to its suitability for observation
and analysis of sports performance developed in a natural and spontaneous context [36].
It has a nomothetic observational design—various study units analysed; punctual—
different sessions analysed over time; and multidimensional—plurality and concurrence of
various response levels are reflected in the observation instrument. In terms of this classifi-
cation, this work is framed within quadrant III of those proposed by Anguera et al. [37].
2.2. Participants and Sample
Direct observation. The study units were ball possessions in the FIFA Women’s World
Cup France 2019 [
38
,
39
]. A total of 2323 were analysed from the 16 matches of the final
phase of the tournament. Both teams were analysed in each match. The fact that the
analysed matches were from the knockout stage—in which a win is needed to progress
in the tournament—eliminates any result speculation on the part of the observed teams.
Additionally, all allowed matches were close games. In the matches analyzed, there were no
major differences between teams. This fact did occur in group stage matches (e.g., EE.UU.
13-0 Thailand). With a view to obtaining the utmost rigour in the results, any actions taking
place during extra time were excluded from the recording and analysis. The inclusion
criteria used for the recording of offensive actions was adapted from Garganta [
40
]. Actions
in which the attacking team fulfilled any of the following requirements were coded:
(i) three
consecutive contacts with the ball or (ii) a finished pass—as long as it lasted more than
three seconds, or (iii) a shot taken. The offensive actions lasted from the first contact with
the ball up to (i) possession changing to the rival team or (ii) there being a regulatory
interruption in the game.
Indirect observation. A total of eight semi-structured interviews [
8
,
9
] with players
and coaches were analyzed (Table 1). The coaches and players were selected by the authors
of the study. All the coaches were UEFA PRO coaches and had experience as head coach in
the Spanish women’s first division. In the case of the players, at the time of the interviews,
all of them were part of one of the teams in the Spanish women’s first division. Those
interviewed included the second coach of the Spanish National team, a player participating

Sustainability 2021,13, 6379 4 of 16
in the FIFA Women’s World Cup Canada 2015 and a player participating in the FIFA
Women’s World Cup France 2019. The semi-structured interviews were prepared by a
group of three experts in the field, two of them Ph.D. in Sports Sciences and a predoctoral
researcher in Sport Sciences. A first pilot interview with five players was conducted. This
interview served as the basis for the elaboration of the final interview, after discussion
among the group of experts. The definitive interview consisted of 15 questions. According
to Anguera [
41
], it was a semi-structured, one to one and non-directed interview. All of
them were requested and carried out by the study authors. The interviews lasted between
36 and 75 min. The transcription of interviews was carried out ad verbatim and the
segmentation was carried out based on the orthographic and syntactic criteria [
42
]. In total,
2410 text units were analyzed.
Table 1. Characteristics of the interviewees.
Int. Sex Role Team Age [Years]
1 M C Real Sociedad 50 [27]
2 F P Spanish Women’s National Team 22 [7]
3 F P Spanish Women’s National Team 25 [7]
4 M C R.C. Deportivo de la Coruña 38 [9]
5 M C U.D. Levante 41 [12]
6 M C Madrid C.F.F. 28 [8]
7 F C Spanish Women’s National Team 39 [21]
8 F P Spanish Women’s National Team 33 [13]
Note. M = Male; F = Female; C = Coach; P = Player; Int = Interview; Years = Years of experience as player
and/or coach.
2.3. Observation Instruments
The direct observation instrument (Table 2) used in this study to analyse ball posses-
sions in FIFA Women’s World Cup was created ad hoc and was a combination of field
formats and category systems [
37
] which gives it the necessary rigour and flexibility for
studying the analysed actions.
The observation instrument used by Maneiro et al. [
18
] was taken as a reference and
various criteria were added with the aim of testing out others that proved to be significant
in previous literature. The categories and definitions of the sub-criteria Start Zone (length)
and Interaction Context were taken from Castellano [
23
] and Castellano and Hernández
Mendo [
24
]. The Start Zone (width) was coded by dividing the field of play into two
lateral corridors and one central; the lateral corridors went from the touchline up to the
projections of the lateral line of the penalty area. The sub-criteria Defensive Organization
can be consulted in the publication by Casal et al. [
43
] Finally, the sub-criteria Defensive
Positioning and Defensive Intention were taken from the instrument “REOFUT” [
28
]. In
both sub-criteria, the definitions of the categories were adapted and classification of the
actions allowed according to the collectivity and social interaction of the players in the
analysed actions [
44
]. For the Possession outcome criteria, the following categories were
defined: (i) Goal: ball possession ended in a goal for the observed team; (ii) Shot: ball
possession ended with a shot for the observed team; (iii) Sent to Area: ball possession
ended with a pass or a cross to the penalty area with the intention that the ball was shot by
a player from the observed team; and (iv) No Success: ball possession was unsuccessful.
The indirect observation instrument used in this study was created ad hoc and was a
combination of field formats and category systems. An indirect observation instrument
was structured combining the ascending and descending path from the transcription of the
interviews. According to Izquierdo and Anguera [
45
] in this type of observation, one of the
main issues is in the construction of the indirect observation instrument, which can be built
ad hoc according to the research problem. The instrument consisted of two dimensions and
28 criteria, which were displayed at a first and second level creating the entire instrument.

Sustainability 2021,13, 6379 5 of 16
Table 2. Direct observation instrument. Field format and category systems.
Dimensions Criteria Categories
Dimension 1. Identification
of action
Observed Team
Match Outcome
Win
Lose
Draw
Dimension 2. Start of
possession
Time
1Q
2Q
3Q
4Q
5Q
6Q
Match Status
Winning
Drawing
Losing
Start Form Set Play
Transition
Start Zone (length)
Defensive
Predefensive
Middle
Preoffensive
Offensive
Start Zone (width)
Left
Central
Right
Defensive Organization Organized
Circumstantial
Defensive Positioning
Low
Medium
Advanced
Interaction Context
MM
A0
AA
AM
AR
MA
MR
RA
RM
PA
Dimension 3. Possession
development
Offensive Intention Keep
Progress
Defensive Intention No pressure
Pressure
MD (seconds)
MO (seconds)
Possession Time
Passes
Possession Zone MD
MO
Dimension 4. Possession
outcome Possession Outcome
Goal
Shot
Sent to Area
No Success
2.4. Recording Instrument
For direct observation, the matches were recorded from public television and anal-
ysed post-event. As they are public images it was not necessary to obtain the personal
authorisation of the players who took part in the analysed matches.

Sustainability 2021,13, 6379 6 of 16
For indirect observation, the interviews conducted were recorded by the interviewer
and transcribed ad verbatim. All participants received informed consent before conducting
the interviews.
Furthermore, this study was approved by the Research and Teaching Ethics Committee
of the Universidade da Coruña (approval number: CEID-UDC-2019-0024)
The recording instrument used, both for the direct and indirect observation was
LINCE PLUS v 1.1.1 [
46
]. This tool allows the visualisation, analysis and recording of
actions via a single screen, by various observers simultaneously.
2.5. Procedure
Three observers (soccer trainers, two of them with PhDs and considerable observa-
tional methodology experience) were trained in and familiarised with the observation
instruments over four sessions [47].
For direct observation, quality control of the data was carried via the calculation of
Cohen’s [
48
] inter-observer kappa coefficient for each of the criteria of the observation
instrument. This calculation produced an average value of 0.869 which shows excellent
quality according to the Landis and Koch scale [49].
For indirect observation, a qualitative data quality control was carried out via con-
sensual agreement and quantitative data quality control via the calculation of Cohen’s
intra-observer kappa coefficient. This calculation produced an average value of 0.708. This
value was considered substantial [49] for this study as it was an indirect observation.
2.6. Data Analysis
2.6.1. Ball Possessions Analysis
Bivariate analysis was suggested between the analysed criteria and the dichotomous
possession outcome criteria, through the Chi-squared statistic and Fisher’s exact test
when necessary. For this analysis, the possession outcome criteria were transformed into
dichotomous (0 = No Success; 1 = Success). The Goal, Shot and Sent to Area categories
were considered Success. All other actions were considered as No Success. Normality
of the continuous type criteria was checked via the Shapiro–Wilk Test (p< 0.05) and was
rejected. The U Mann–Whitney Test was used to check whether there were differences
between both groups for this type of criteria.
Finally, a multivariate predictive analysis was carried out via a decision tree. This
technique, which is rarely applied in the field of physical activity in sport, has proved to
possess good applicability and results interpretation in this field. For this reason, over
the last few years, different studies have been developed via this type of analysis in
soccer [18,32,49–51], five-a-side soccer [52] and Australian soccer [53].
The dependent variable input into the decision tree was that of possession outcome in
its dichotomous recoding. All the criteria included in the observational instrument formed
part of the model as predictive. The tree growth method was Chi-Squared Automatic
Interaction Detection (CHAID). This model chooses, in each decision node, the independent
or predictive variable that shows the strongest association with the variable being predicted,
joining together the different categories of each one when there are no significant differences.
Taking into account the total sample size and to avoid pruning the tree, it was decided to
establish a minimum of 100 cases for each node. The established significance level was
p< 0.05 for the inclusion of new nodes on the tree. Lastly, the tree decision model was
validated via the sample division method (50% of the total training sample, 50% of the total
validation sample). The tree decision model correctly classified 79% of cases and the risk
estimation of the model was 0.210.
The statistical analysis was performed using SPSS 25.0 (IBM Corp. Released 2017. IBM
SPSS Statistics for Windows, Version 25, IBM Corp., Armonk, NY, USA).

Sustainability 2021,13, 6379 7 of 16
2.6.2. Interview’s Analysis
“Quantitizing” of text units was carried out from the narrative transcription of in-
terviews to a matrix of codes [
44
]. Two types of analysis were carried out: sequential
analysis of delays and analysis of polar coordinates using HOISAN free program [
54
].
Those categories and behaviors that presented results higher than Z > 1.96 were considered
statistically significant (p< 0.05) for both the sequential analysis of delays and the polar
coordinate analysis.
3. Results
3.1. Direct Observation
A total of 2323 offensive actions were recorded in 16 analysed matches. These data
gave an average value of 145.2 offensive actions per match. An initial descriptive analysis
of the Possession Outcome criteria leads to the observation that, of the total number of
possessions, (n = 1744; 75.1%) ended in No Success. In terms of success: 14.8% of the actions
ended with Sent to Area, 9.0% ended in Shot and just 1.1% ended with Goal. We consider
that one in every four actions (n = 579; 24.9%) ended in relative success for the attacking
team (Goal, Shot, Sent to Area) while the rest of the actions ended with No Success.
On a bivariate level, twelve of the sixteen observational instrument criteria show
significant differences based on the result of the action (Table 3). The criteria that showed
significant differences in the chi-squared test were Time (p < 0.001), Start Form (p < 0.05),
Start Zone (length) (p< 0.001), Defensive Organization (p< 0.001), Defensive Positioning
(p< 0.001), Interaction Context (p<0.001), Offensive Intention (p< 0.001), Defensive
Intention (p< 0.001) and Possession Zone (p< 0.001). In terms of continuous type criteria,
the significant differences via the Mann–Whitney test appeared in the following criteria:
Possession Time in Own Half (MD) (p < 0.001), Possession Time in Opponent’s Half (MO)
(p< 0.001) and Passes (p< 0.005).
On a multivariate level, a twelve-node decision tree was produced (Figure 1), seven
of which are terminal. The model shows a total correct classification percentage of 79%.
A descending reading of the results was carried out, in accordance with the structure of
the tree.
The first node 0 is based on Possession Outcome with 1.159 observations and a majority
of No Success (n = 861, 74.3%) against Success (n = 298, 25.7%). The first criteria taken into
consideration by the algorithm was Possession Zone (
χ2
= 215.447; p< 0.001), branching off
into two, and nodes corresponding to the categories MD and MO. In this branching off, we
can observe that the possibilities of success increase in the MO category, against the MD.
In Node 1 (MD) we can see a total of 578 observations (95.3% No Success; 4.7% Success).
In Node 2 (MO) there is a notable increase in the percentage of success (n = 581; 53.4%
No Success; 46.6% Success). Nodes 3, 4 and 5 of the decision tree are terminal and were
input as the following predictive variable (from the MD onwards), the Possession Time
in Opponent’s Half (MO) (
χ2
= 125.810; p< 0.001). We can observe in these three nodes
how the probability of success increases as the value of the Possession Time in Opponent’s
Half increases. We can see a 0.5% probability of success out of a total of 429 observations in
Node 3 when possession time in the opponent’s half is less than or equal to four seconds.
Node 4 shows 84 observations, and we see that the probability of success rises to 14.3%
when possession time in the opponent’s half is 5, 6 or 7 s. However, when that time is over
seven seconds the possibility of success rises to 20% (Node 5, 65 observations).

Sustainability 2021,13, 6379 8 of 16
Table 3. Bivariate analysis based on possession outcome.
Criteria Categories No Success n = 1744 Success n = 579 pOverall
Match Outcome
Win 665 (38.1%) 238 (41.1%)
0.207
Lose 754 (43.2%) 226 (39.0%)
Draw 325 (18.6%) 115 (19.9%)
Time
1Q 321 (18.4%) 89 (15.4%)
<0.001
2Q 314 (18.0%) 79 (13.6%)
3Q 311 (17.8%) 92 (15.9%)
4Q 277 (15.9%) 90 (15.5 %)
5Q 263 (15.1%) 102 (17.6%)
6Q 258 (14.8%) 127 (21.9%)
Match Status
Winning 409 (23.5%) 131 (22.6%)
0.519
Drawing 742 (42.5%) 236 (40.8%)
Losing 593 (34.0%) 212 (36.6%
Start Form Set Play 573 (32.9%) 161 (27.9%) 0.026
Transition 1171 (67.1%) 417 (72.1%)
Start Zone (length)
Defensive 322 (18.5%) 44 (7.6%)
<0.001
Predefensive 666 (38.2%) 105 (18.1%)
Middle 464 (26.6%) 161 (27.8%)
Preoffensive 267 (15.3%) 218 (37.7%)
Offensive 25 (1.4%) 51 (8.8%)
Start Zone (width)
Left 385 (22.1%) 141 (24.4%)
0.17
Central 946 (54.1%) 288 (49.7%)
Right 413 (23.7%) 150 (25.9%)
Defensive
Organization
Organized 1711 (98.3%) 545 (94.1%) <0.001
Circumstantial 29 (1.7%) 34 (5.9%)
Defensive
Positioning
Low 674 (38.7%) 376 (65.1%)
<0.001
Medium 335 (10.2%) 81 (14%)
Advanced 733 (42.1%) 121 (20.9%)
Interaction
Context
MM 676 (38.8%) 267 (46.1%)
<0.001
A0 0 (0%) 10 (1.7%)
AA 39 (2.2%) 0 (0%)
AM 10 (0.6%) 5 (0.9%)
AR 86 (3.7%) 107 (18.5%)
MA 25 (1.4%) 4 (0.7%)
MR 17 (1%) 20 (3.5%)
RA 619 (35.5%) 120 (20.7%)
RM 64 (3.7%) 14 (2.4%)
PA 206 (11.8%) 32 (5.5%)
Offensive
Intention
Keep 1133 (65.0%) 214 (37.0%) <0.001
Progress 611 (35.0%) 365 (63.0%)
Defensive
Intention
No pressure 1026 (58.9%) 422 (72.9%) <0.001
Pressure 715 (41.1%) 157 (27.1%)
MD (Seconds) 17.0 [3.0–12.0] 0.0 [0.0–4.0] <0.001
MO (Seconds) 14.0 [1.0–8.0] 9.0 [6.0–13.0] <0.001
Possession Time 112.0 [7.0–18.0] 11.0 [6.0–19.0] 0.265
Passes 13.0 [2.0–5.0] 3.0 [2.0–5.00] 0.003
Possession Zone MD 1086 (62.4%) 57 (9.8%) <0.001
MO 655 (37.6%) 522 (90.2%)
Note. n = Frequency; 1Values are presented as medians and interquartile range.

Sustainability 2021,13, 6379 9 of 16
Sustainability 2021, 13, x FOR PEER REVIEW 9 of 16
Figure 1. Predictive analysis of possessions outcome via decision tree. Note: n = frequency; MD = middle defensive; MO =
middle offensive.
Figure 1.
Predictive analysis of possessions outcome via decision tree. Note: n = frequency; MD = middle defensive;
MO = middle offensive.
On the other hand, nodes 6 and 7 of the decision tree input as predictive criteria the
Offensive Intention (
χ2
= 21.381; p< 0.001). Node 6 shows 269 observations and gives
a possibility of success of 35.4% with the Keep category. However, when the Offensive
Intention was progressed (node 7, n = 313), the probability of obtaining Success in the

Sustainability 2021,13, 6379 10 of 16
offensive action was 56.2%. Node 7 branches off into two, taking as predictive variable
the Start Zone (length) (
χ2
= 22.574; p< 0.001) in two nodes (8 and 9), the second of which
is terminal. Node 9, with the Pre-offensive and Offensive categories, shows a total of
109 observations
classified as Success, 75.2% of them, with this node being that which
shows the highest percentage of cases classified as Success. On the other hand, node 8
shows a total of 204 observations, classifying as Success 46.1% of cases when the Start Zone
(length) was defensive, pre-defensive or middle.
The last criteria input by the algorithm from node 8 onwards was Possession Time in
the Opponent’s Half (MO) (
χ2
= 9.728; p< 0.007). Two terminal nodes are produced for
which are taken as a value of possession time in the opponent’s half all those equal to or
less than 7 s in node 10, obtaining probabilities of: 75.3% No Success and 24.7% Success out
of a total of 73 observations. Lastly, node 11 obtains probabilities of 42% No Success and
58% Success for a total of 131 observations, taking all the values of possession time in the
opponent’s half (MO) greater than 7 s.
3.2. Indirect Observation
The tactical or contextual criteria that demonstrated a significant association with
offensive success in women’s football from polar coordinates analysis and sequential
analysis of delays were: (i) physical, technical, tactical and cognitive aspects of players,
(ii) offensive
transitions, (iii) initial defensive intention, (iv) start form, (v) number of passes
and attack duration and (vi) combinative attack and collective offensive technical aspects.
4. Discussion
The aims of this research were twofold: (i) to discover which are the contextual and
tactical criteria that show association with success in elite women’s soccer, and (ii) to
establish a multivariate predictive model for the possessions from the analysed criteria.
The sample used for the study was the FIFA Women’s World Cup France 2019 [
39
]. In
addition, eight semi-structured interviews with women’s soccer coaches and players were
transcribed ad verbatim and analysed by indirect observation.
A total of 2323 actions were analysed in 16 matches. This produced an average value
of 145.2 actions per match, very close to the 147.66 actions observed by Jones et al. [
12
] in
the English Premier League or the 135 observed by Barreira et al. [
20
] when they analysed
the matches of the four semi-finalists in the FIFA World Cup 2010. Of the total of analysed
actions, approximately 25% ended with relative success for the attacking team (Goal, Shot
or Sent to Area) and just 1.1% ended in Goal.
Neither of the contextual criteria analysed—Match Status nor Match Outcome—
showed significant association with success in the possessions in this championship. The
match status criteria are striking in that they proved to significantly influence ball pos-
session in men’s soccer [
12
,
14
,
25
,
26
], modifying the possession zone and meaning that
the teams had the ball closer to the rival goal when they were losing [
14
]. It was later
observed that possession in offensive zones was favourable associated with the success of
ball possessions Casal et al. [
17
]. On the other hand, Taylor et al. [
15
] showed that match
status did not alter the technical behaviour of men’s elite teams in domestic leagues, which
is in line with the results obtained. In terms of the Time criteria, the results allow us to
witness the existence of a greater probability of success during the last half hour of the
match in comparison with the rest of the 15-min periods analysed. While these results may
agree with those obtained by Sarmento et al. [26] this should be treated with caution. It is
necessary to take into account the possible influence of the temporary result and the teams’
need to score a goal when they are losing. These criteria can undoubtedly modify a teams’
offensive behaviour when they are losing in the final minutes of the game, trying to send
balls to the area quickly and ineffectively, thus modifying the statistical results obtained
and leading us to draw erroneous conclusions.
The Start Zone (length) proved to be statistically significant by modifying the proba-
bility of obtaining success in offensive actions. Approximately one in every two actions

Sustainability 2021,13, 6379 11 of 16
analysed started off in the Defensive or Pre-defensive zone, corroborating previous studies
that affirm a stable tendency in this sport towards ball possessions beginning in rear zones
of the field of play [
19
,
20
,
22
]. On the other hand, the nearer to the rival goal is the start of
the offensive action, the greater the probability of success in ball possessions. This was
the very conclusion that Scanlan et al. [
21
] came to after analysing the creation of goal
opportunities in the FIFA Women’s World Cup Canada 2015. These results agree with the
existing bibliography on men’s elite soccer in which there is great consensus concerning
the influence of these criteria on the success of possessions [
19
,
20
,
22
,
25
,
26
]. In relation to
the above, we were able to note an important difference between the sexes in terms of
soccer development that lies in the added difficulty in women’s soccer of carrying out a
pressurised release in the defensive zone through long passes. This technical deficiency
could allow the opposing team to adopt advanced defensive positions that impede the
release of the ball in technically inferior teams. In this sense, it is logical to think that those
female soccer teams that are physically stronger would have technical–tactical advantages
at this point of the game [3]. These results were argued by some of the interviewees:
Coach 1: “Long ball possessions allow other things, but goals always come from quick actions.
[
. . .
] I believe that stealing the ball in opposite’s half is the moment when the rival team will be
disorganized and that is when you will have most probabilities of success”
Player 1: “I have lived it and I have seen it, the more advanced the recovery of the ball, the
greater probability of success”
Coach 5: “The best strategy is the pressure after loss in opposite’s half. For us, stealing the
ball in the opponent’s half as soon as possible is important because of the way we play”
It is therefore logical that the results obtained from analysing the Interaction Context
would show the presence of more offensive interaction contexts in women’s compared to
men’s elite soccer. In the sample analysed, around 10% of the actions began in a context
with a high offensive value [
24
] AR or MR. These values are higher than those observed
when analysing the men’s EURO2008 and EURO2016 [
19
] in which the percentages were
3.77% and 8.74% respectively. These results appear logical for two reasons: firstly—as
Kirkendall [
2
] states after interviewing different trainers of women’s elite soccer—there
might be lower technical quality in the specific post of defender in women’s soccer; and
secondly, the increased difficulty in covering long distances with the ball makes the players
appear more lost on the ball in a sector of the field of play in which the opposing team
shows more offensive interaction contexts. This justification was corroborated in the FIFA
Women’s World Cup France 2019 where it was observed that successful teams obtained
a greater number of ball recoveries in the rival team’s half [
7
]. In relation to this, the
interviews considered that the technical-tactical aspects were a significant criterion in the
proper development of ball possession in women’s soccer:
Coach 1: “To unbalance a team that is defensively organized you have to be able to pass the
ball effectively, decisively, to know when it is a short pass, when it should be a trough ball. If you
don’t do it right, it’s impossible”.
Coach 3: “Ball possession has two objectives: occupy more advanced areas and overcome
rivals. The moment you advance, you are going to cause a player to have to advance and must
become disorganized. During this process, the spaces you are going to occupy will appear, increasing
the probabilities of success”.
Player 3: “What makes the difference is the technical-tactical performance of the players. If
you have players with greater passing efficacy, your ball possession will be safer. So, if you decide,
your attack can be longer and will be more effective”.
The second spatial criteria analysed—Start Zone (width)—did not present significant
differences in terms of success. These results contradict those obtained by Scanlan et al. [
18
]
who observed a higher probability of achieving goal opportunities in the women’s world
cup of 2015 when the start of the offensive phase was produced in the centre lane of the
field of play.
Another criterion included in the observational instrument was Start Form. This
criterion showed a significant influence on possession outcome (p< 0.05) in the same way

Sustainability 2021,13, 6379 12 of 16
that previous studies have demonstrated [
19
,
20
]. This factor appears to indicate that in
women’s soccer a dynamic start to the offensive action is a factor to be considered in the
development of offensive actions, even more so taking into account the higher number of
ball losses produced in women’s soccer compared to men’s [
5
,
6
]. This element seems logical
if we consider that the team that recovers is in a position to exploit a certain space–time
advantage due to the defensive disorganisation of the rival team [
20
]. In relation to all
of the above, the Defensive Organization of the rival team was also a significant variable
(
p< 0.001
): when the opposing team was defensively disorganised at the point of the start
of the offensive action, the probabilities of success rose threefold.
The Defensive Positioning and Defensive Intention criteria presented significant
differences with success (p< 0.001). The two criteria may be related—while it would be
necessary to carry out different investigations into these aspects—with Start Zone (Length)
and Defensive Organization. In this sense, it would appear logical to think that when
teams recover the ball in zones near to the rival goal, the defending team is positioned in its
own half and therefore presents deeper defensive positioning. Similarly, when a team loses
possession of the ball and is not defensively organised there will be a collective defensive
intention directed at carrying out a rapid defence of their own goal by trying to slow
down the attacking team’s advance. In relation to Vogelbein et al. [
27
] observations in the
Bundesliga, which demonstrated that the best teams recovered the ball more quickly and
via more pressurised intentions, the results of this study could suggest a similar tendency
in women’s elite soccer, even though the limitations of the study concerning this variable
do not allow us to form clear conclusions.
Player 4: “The best teams are teams that dominate their matches. For example, France and the
USA are teams that do “gegenpressing” for their great physical performance and because they have
more possession than their rivals”.
Another of the analysed criteria that showed significant statistical differences with
success was the Offensive Intention (p< 0.001). According to the results obtained, we
can affirm that there was approximately twice the probability of success when the team
attempted to Progress rapidly towards the rival goal, compared with possessions in which
it tried to keep possession of the ball. The results obtained by Maneiro et al. [
22
] in men’s
elite soccer are also along these lines.
Finally, the results obtained from analysing the influence of the continuous criteria on
the success of offensive actions were interpreted. Of the four criteria included in the ad hoc
observation instrument, three of them showed significant differences depending on the
result of the possession: Possession Time in Own Half (MD) (p< 0.001), Possession Time
in Opponent’s Half (MO) (p< 0.001) and Passes (p< 0.005), although the total possession
time did not influence the result of the possessions (p= 0.265). Scanlan et al. [
18
] obtained
similar results in the FIFA Women’s World Cup 2015; in this championship, there were
no significant differences observed in the duration of the offensive actions that ended
in a goal compared with those that ended unsuccessfully. In terms of number of passes,
despite there being significant differences, it is thought necessary to enlarge the study of
this variable in women’s soccer according to the results obtained, with a view to seeing
whether its influence on offensive success is similar to that observed in other studies
among men [24,25].
In relation to the previous criteria, we can affirm that the Possession Zone (p< 0.001)
was a variable that modified the success of the possessions in the Women’s World Cup
France 2019. In agreement with Casal et al. [
17
] the results obtained appear to suggest that
the development of possession of the ball in the opponent’s half is an indicator of success
in the possessions in women’s soccer, although we should be cautious when extracting
general conclusions about women’s soccer due to the specific nature of the sample analysed.
A possible justification for this may be the greater technical–tactical performance of the
players of the best teams when developing attacks in areas with higher player density:

Sustainability 2021,13, 6379 13 of 16
Coach 4: “The longer you have the ball in the opponent’s half, the greater the probability of
success you will have. I think that ball possession in own’s half does not contribute anything to the
offensive process”.
Player 3: “I think that talented players make it possible to win matches because they don’t
need space and time: [
. . .
] Their decision-making is faster than other players and their technical
performance allows them not to need space to generate scoring opportunities”.
Coach 1: “Soccer is about perception and constant decision making. [
. . .
] Taking that into
account, our players must be thinking before receiving the ball. This is how we manage to speed up
the offensive process”.
Finally, analysis of the decision tree carried out in this study allows us to corroborate
the influence of the aforementioned criteria. From the criteria introduced by the tree’s
algorithm, we can see that the Possession Zone was the criteria that most influenced the
process of possessions in this edition of the FIFA Women’s World Cup France 2019. When
possession of the ball was developed largely in MD, the probability of success was barely
5%, against the 46.6% observed when the possession zone was MO. Following the straight
branches of the decision tree, the algorithm introduced the Offensive Intention criteria
which increased the probability of success in possession to 56.2% when the initial intention
was Progress. The highest probability of Success (75.2%) was observed when the Start Zone
(Length) was Pre-offensive or Offensive. In this sense, we can see a clear similitude with
different studies of men’s soccer in observing that the criteria that show association with
success in possessions are the Possession Zone [
14
,
17
], Offensive Intention [
22
,
25
,
26
] and
the Start Zone (Length) [19,20,22].
5. Conclusions
According to the results obtained in the current research, it can be concluded that
the greatest probability of finishing ball possession successfully in women’s soccer occurs
when: (i) most of the possession time takes place in the opponent’s half, (ii) there is an
initial offensive intention to progress quickly towards the rival goal once possession of the
ball has been recovered and (iii) the start zone of the possession is pre-offensive or offensive.
In addition, based on the analysis carried out on the transcripts of the interviews with
coaches and players, criteria related to technical–tactical performance, decision-making and
physical capacities of the players will be decisive in increasing the probabilities of success.
Based on the results obtained in both analyzes, it is possible to conclude the need to start
ball possessions in areas close to the rival goal and to develop an intention to progress
quickly. In this situation, in which the center of the game will be in a high density of players
area, offensive success will be conditioned to the technical–tactical performance of the
attacking players, the speed and efficiency in their decision making, and their physical
abilities to take advantage of the situation of defensive disorder that transition moments
represents in elite soccer.
Whilst these results could be useful for trainers and players in training and competi-
tion, we consider it necessary to further develop knowledge of ball possessions in women’s
elite soccer in future research.
6. Limitations and Future Lines of Research
The results obtained in this study suppose an approach to the behaviors to be devel-
oped in the achievement of offensive success in elite women’s soccer. Despite this, one of
the limitations of this study is the fact that the analysis of a single championship does not
allow to draw conclusions about the complex reality of the offensive phase in women’s
soccer. Likewise, it is necessary to carry out more studies in which coaches and players
participating in the tournaments analyzed allow obtaining significant results about the cri-
teria that determine offensive success. Under this premise, mixed methods are postulated
as an optimal paradigm in carrying out future related research.

Sustainability 2021,13, 6379 14 of 16
Author Contributions:
Conceptualization, I.I.-B. and A.A. methodology I.I.-B., R.M. and J.L.L. formal
analysis, I.I.-B. and J.L.L. reviewers, R.M., J.L.L. and A.A. writing-original draft preparation, I.I.-B.
writing—review and editing, R.M., J.L.L. and A.A. visualization, I.I.-B. supervision, R.M., J.L.L. and
A.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
This study was approved by the Research and Teaching
Ethics Committee of the Universidade da Coruña (approval number: CEID-UDC-2019-0024).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in
the study.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors gratefully acknowledge the support of a Spanish government
subproject Mixed method approach on performance analysis (in training and competition) in elite
and academy sport [PGC2018-098742-B-C33] (2019–2021) [del Ministerio de Ciencia, Innovación y
Universidades (MCIU), la Agencia Estatal de Investigación (AEI) y el Fondo Europeo de Desarrollo
Regional (FEDER)], that is part of the coordinated project New approach of research in physical
activity and sport from mixed methods perspective (NARPAS_MM) [SPGC201800X098742CV0].
Also, the authors thank the coaches and players participating in the research.
Conflicts of Interest: The authors declare no conflict of interest.
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