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A systematic review of match-play characteristics in women’s soccer

  • Alice Harkness-Armstrong ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

    a.harkness-armstrong@essex.ac.uk

    Affiliations School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, United Kingdom, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom

  • Kevin Till,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom

  • Naomi Datson,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation Institute of Sport, University of Chichester, Chichester, United Kingdom

  • Naomi Myhill,

    Roles Investigation, Validation, Writing – review & editing

    Affiliations Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom, The Football Association, Burton Upon Trent, United Kingdom

  • Stacey Emmonds

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom

Abstract

This review aimed to (1) systematically review the scientific literature evaluating the match-play characteristics of women’s soccer, (2) determine the methods adopted to quantify match-play characteristics of women’s soccer, and (3) present the physical, technical and tactical characteristics of women’s soccer match-play across age-groups, playing standards and playing positions. A systematic search of electronic databases was conducted in May 2021; keywords relating to the population, soccer and match-play characteristics were used. Studies which quantified physical, technical or tactical performance of women’s soccer players during match-play were included. Excluded studies included adapted match-play formats and training studies. Sixty-nine studies met the eligibility criteria. Studies predominantly quantified match-play characteristics of senior international (n = 27) and domestic (n = 30) women’s soccer match-play, with only seven studies reporting youth match-play characteristics. Physical (n = 47), technical (n = 26) and tactical characteristics (n = 2) were reported as whole-match (n = 65), half-match (n = 21), segmental (n = 17) or peak (n = 8) characteristics. Beyond age-groups, playing standard, and playing position, fourteen studies quantified the impact of contextual factors, such as environment or match outcome, on match-play characteristics. Distance was the most commonly reported variable (n = 43), as outfield women’s soccer players covered a total distance of 5480–11160 m during match-play. This systematic review highlights that physical match-performance increases between age-groups and playing standards, and differs between playing positions. However, further research is warranted to understand potential differences in technical and tactical match-performance. Coaches and practitioners can use the evidence presented within this review to inform population-specific practices, however, they should be mindful of important methodological limitations within the literature (e.g. inconsistent velocity and acceleration/deceleration thresholds). Future research should attempt to integrate physical, technical and tactical characteristics as opposed to quantifying characteristics in isolation, to gain a deeper and more holistic insight into match-performance.

1 Introduction

There has been substantial global growth and development of women’s soccer within recent years. Global, continental and national governing bodies have implemented specific women’s soccer strategies and increased investment, to support the development of the sport from grassroots to elite playing standards [15]. There has been an increase in participation rates [3], increased provision and support for developing talented youth players (e.g. the English Football Association’s regional talent centres and Women’s Super League academies programme), increased professionalisation of elite playing standards [6], and subsequently increased audiences for elite senior competitions (e.g. FIFA Women’s World Cup, UEFA Women’s European Championships, UEFA Champions League) [3, 5, 6]. Furthermore, recent research has suggested that observed increases in physical match-play performances of elite senior players are consequential of the sport’s growth and development, and increased professionalisation of the game [7, 8].

Additionally, there has been a notable increase in the volume of literature focusing on women’s soccer [9], which is likely reflective of the sport’s growth and development. The focus of the literature to date has predominantly surrounded injury and strength and conditioning of women’s soccer players, with limited research quantifying the match-characteristics of women’s soccer [9]. This is problematic, as knowledge and understanding of the demands which players may experience during match-play is important for informing population-specific practices for match-play and beyond. For example, coaching practice design and training programme design in preparation for the demands of match-play within respective playing standards, preparing players transitioning across playing standards, long-term athletic player development practices, talent identification, or injury monitoring and rehabilitation processes.

Despite a relatively limited body of literature, there have previously been six narrative reviews summarising match-play characteristics of women’s soccer [1015]. However, there are several important limitations associated with these reviews. Firstly, without a comprehensive literature search and pre-defined, objective study selection criteria, narrative reviews may involve subjective study selection bias [16]. Additionally, the depth of information or choice of data extracted from respective studies may be limited or subjective. Consequentially, narrative reviews may result in biased or subjective author interpretation and conclusions [16]. Therefore, there is a need for a systematic review, to provide a comprehensive, objective and scientifically rigorous summary of the evidence-base on match-play characteristics of women’s soccer. Secondly, all narrative reviews to date have exclusively summarised the physical characteristics of match-play, neglecting the important technical and tactical characteristics. This is problematic, as soccer performance is the combination of physical, technical and tactical characteristics, and thus aspects of performance should not be considered in isolation [17, 18]. Therefore, there is a need to review and summarise physical, technical and tactical characteristics, to provide a holistic understanding of women’s soccer match-play. Thirdly, narrative reviews have highlighted methodological inconsistencies within the literature (e.g. methods of data collection, and velocity or acceleration thresholds). However, no review has attempted to evaluate the methodologies adopted to quantify match-play characteristics. Methods of data collection within recent research likely differ compared to earlier studies, due to FIFA law changes permitting wearable technology (e.g. global positioning system (GPS) units) within competitive match-play. Therefore, it is important that researchers and practitioners have an awareness and understanding of the different methodologies utilised within the literature when interpreting match-play characteristics and informing research or practice. Lastly, existing reviews neglected to summarise the peak periods of women’s soccer match-play characteristics [19, 20], which provide insight into the worst-case scenarios players may face during matches. Understanding the peak periods of match-play players may experience is important for informing coaching practice and training prescription for players, to ensure players are optimally prepared for the most demanding periods of match-play.

Therefore, the aims of this review were to: (1) systematically review the scientific literature evaluating the match-play characteristics of women’s soccer, (2) determine the methods adopted to quantify match-play characteristics of women’s soccer, and (3) present the physical, technical and tactical characteristics of women’s soccer match-play across age-groups, playing standards and playing positions. This will be the first systematic review of match-analysis within women’s soccer, providing researchers and practitioners with a comprehensive, critical and objective resource of the physical, technical, and tactical match-play research across women’s soccer populations, which can be used to inform respective population-specific practice.

2 Methods

2.1 Design and search strategy

The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21]. A systematic search of electronic databases (CINAHL, Medline, PubMed, Scopus and SPORTDiscus) was completed on the 18th May 2021, with no date restrictions applied. The search strategy included the terms for the population (‘female’ OR ‘women’s’ OR ‘girls’), AND sport (‘soccer’ OR ‘football’ OR ‘association football’), AND match-play characteristics (‘match characteristics’ OR ‘match demands’ OR ‘match performance’ OR ‘match play’ OR ‘match-play’ OR ‘match activities’ OR ‘activity profile’ OR ‘physical characteristics’ OR ‘physical performance’ OR ‘running characteristics’ OR ‘running demands’ OR running performance’ OR ‘peak demands’ OR ‘movement characteristics’ OR ‘movement profiles’ OR ‘technical characteristics’ OR ‘technical demands’ OR ‘technical performance’ OR ‘tactical characteristics’ OR ‘tactical demands’). Additionally, the search strategy included NOT (‘American football’ OR ‘Australian football’ OR ‘Australian rules football’ OR ‘Gaelic football’). Additional manual searches of selected study’s reference lists were conducted for potentially eligible studies. A review protocol was not prepared/registered prior to literature search.

2.2 Study selection

Duplicate studies were identified and eliminated prior to initial screening. Initial screening involved, two researchers independently (AHA, NM) screening the title, abstract, and keywords against the eligibility criteria. Selected studies’ reference lists were manually searched for other potentially eligible papers and included for further screening. Following initial screening, selected studies underwent full-text screening against the eligibility criteria, with the selected studies following this further screening included within this review. Disagreements by the two researchers following initial or full-text screening, were resolved through discussion.

Studies were included if they involved women’s soccer players, participants could be of any age, standard or playing position, and studies were included if they involved a physical, technical or tactical performance aspect of friendly or competitive match-play. Only peer-reviewed studies were included, with abstracts, book chapters, systematic reviews and theses excluded. Studies which only included; men, match-play characteristics of other football codes (i.e. American football, Australian rules football, futsal, Gaelic football, rugby league, rugby union, rugby sevens), quantification of training characteristics (i.e. did not include match-play), adapted match-play formats (i.e. match-play not in accordance with official rules for the respective age-group, e.g. reduced match duration or dimensions, small-sided games), or studies unavailable in English were also excluded.

2.3 Methodological quality

The methodological quality of the selected studies were assessed in line with previous systematic reviews involving match performance of soccer players [22, 23]. The methodological quality criteria are shown in Table 1. A maximum score of 10 out of 9 criteria questions could be obtained. Where ‘clearly’ is included within criteria, this required the relevant information to be explicitly detailed within the study. Methodological quality was included for descriptive purposes as opposed to criteria for inclusion/exclusion within this review.

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Table 1. Methodological quality criteria for selected studies.

https://doi.org/10.1371/journal.pone.0268334.t001

2.4 Data extraction

Data were extracted by one author (AHA), and checked by a second (NM), with any disagreements resolved through discussion. Data relating to participant and study characteristics (e.g. age, height, body mass, standard of competition, number of teams, number of matches), methods of data collection and analysis (e.g. equipment specification, adopted velocity thresholds, variable definitions), and match-play characteristics (e.g. physical, technical or tactical variables, and match contextual information such as match outcome) were extracted. Where data were presented as figures, WebPlotDigitizer v4.4 [24] was utilised to extract data. Where studies included other data in addition to the relevant data, only the eligible data relating to match-play characteristics of women’s soccer players were extracted. For example, sex-differences [2530], training and adapted match-formats [29, 3133], matches against men’s soccer teams [34], or assessments of fitness or physiological characteristics [35, 36]. Lastly, to facilitate comparisons between studies, metrics were converted to standard units, including; player height (cm), distance covered (m), and relative distance covered (m·min-1).

2.5 Statistical analysis

A meta-analysis was precluded within this systematic review due to the variation in methods of data collection and analysis. Data are presented as mean ± SD. Where possible, any data extracted as mean ± SE or confidence intervals were converted to SD [19, 3742], however, where this was not possible due to insufficient methodological information provided within studies, SE or confidence intervals were reported and noted [8, 26, 33, 4346].

3 Results

3.1 Overview

Fig 1 presents a flow diagram of the study selection process. The electronic database search identified 1562 articles, with an additional 29 articles identified through other sources. A total of 69 articles remained for analysis following removal of duplicates, initial and full-text screening [8, 19, 20, 2590].

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Fig 1. Flow diagram of study selection process for qualitative synthesis.

https://doi.org/10.1371/journal.pone.0268334.g001

3.2 Study quality

The results for the methodological quality can be seen in Table 2. The mean score was 7.3 ± 1.4, and scores ranged between 4–10. The majority of studies lacked information regarding contextual variables (Q8 n = 28) of matches, whilst only 33 of the 69 studies differentiated match-play characteristics by playing position (Q6).

3.3 Participant and study characteristics

Table 3 presents the participant and study characteristics of the 69 studies. The earliest study was published in 2005 [64, 65]. There has been a notable increase in publications since 2015 (70%). Only 39 studies reported the year(s)/season(s) data was collected, of which 13 and 21 studies’ data were collected prior to- and since 2015, respectively, whilst 5 studies involved data collected both prior to- and since 2015. Nationalities of participants/locations of match-play included; Australia (n = 8; 12%), Brazil (n = 5; 7%), USA (n = 21; 30%), Canada (n = 1; 1%), and various Asian countries (n = 1; 1%), European countries (n = 24; 35%), or countries competing in the FIFA Women’s World Cup Finals (n = 9; 13%), whilst 3 studies did not report this information [20, 72, 84]. Studies predominantly quantified match-play characteristics of senior players (n = 63; 91%), and included international (n = 27; 39%), top tier domestic (n = 28; 41%), lower tiers domestic (n = 3; 4%), and college/university (n = 13; 19%) playing standards. Only seven studies involved youth players, including; U20 [78, 79], U17 [40, 75, 79], U16 [19, 37, 40, 75], U15 [40, 58, 75], U14 [19, 37, 58, 75] and U13 age-groups [58, 75]. Of the 53 studies which reported the number of teams, over half only involved a single team (n = 30; 57%). The mean number of reported participants was 52 (6–518), with 7 studies involving more than 100 participants (107–518) [8, 19, 37, 41, 51, 52, 55]. Of the 57 studies which reported number of matches, the mean number of matches observed was 38 (1–695). However, when excluding the largest number of matches observed within a single study (n = 695) [53], the mean reduced to 27 (1–230) matches. The majority of studies involved competitive match-play only, with two studies involving both competitive and friendly match-play [40, 57], three studies involving only non-competitive match-play [31, 39, 72], and two studies not stating whether match-play was competitive or friendly [20, 61]. Nineteen studies did not report the number of match files. The mean number of reported match files was 200 (4–3268), however when discarding the study with the largest number of match files (n = 3268) [8], the mean was reduced to 138 (4–695) match files.

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Table 3. Participant and study characteristics of studies quantifying match-play characteristics of women’s soccer.

https://doi.org/10.1371/journal.pone.0268334.t003

3.4 Physical characteristics

Studies predominantly quantified physical characteristics of women’s soccer match-play (n = 47; 68%). The majority (n = 35; 74%) quantified whole-match absolute characteristics, whilst 21 studies (45%) quantified half-match absolute characteristics, 15 studies (32%) quantified segmental absolute values (e.g. 5-minutes, 15-minutes), 16 studies (34%) quantified whole-match relative values, and 8 studies (17%) quantified peak values. Distance was the most commonly quantified variable (n = 43; 91%). Details of data collection and analysis methods are presented in Table 4. Data collection methods for quantifying external load variables included; 5 Hz (n = 9; 19%), 10 Hz (n = 22; 47%) or 15 Hz (n = 1; 2%) GPS units, time-motion analyses (n = 5; 11%), 25 Hz multi-camera match analysis system (n = 3; 6%), 25 Hz optical player tracking system (n = 2; 4%), and 20 Hz radio-frequency tracking (n = 2; 4%). Heart rate monitors were used in 11 studies (23%), and the respective physical characteristics reported are presented in S1 Table.

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Table 4. Methods used to quantify physical characteristics of women’s soccer match-play.

https://doi.org/10.1371/journal.pone.0268334.t004

The majority of studies involved comparative groups (n = 34; 72%); playing position (n = 25; 53%), playing standard (n = 5, 11%), and age-group (n = 3; 6%). Whilst, 9 studies (19%) quantified the impact of contextual variables on physical characteristics; environmental factors (e.g. altitude, temperature [36, 49, 84]), quality of opposition [38, 84], match outcome [46, 84], type of competition [50], match location [46], congestion of fixtures [71, 84], playing surface [46], stage of season [89], and stage of menstrual cycle [63].

Of the 26 studies which categorised players by playing position; 9 studies utilised high-level categorisation (i.e. defenders vs midfielders vs forwards) [38, 40, 41, 43, 44, 57, 77, 81, 86]; 7 studies differentiated central and wide defenders and midfielders (i.e. central defenders vs wide defenders vs central midfielders vs wide midfielders vs forwards) [19, 26, 33, 51, 52, 74, 80]; 5 studies differentiated central and wide defenders only (i.e. central defenders vs wide defenders vs midfielders vs forwards) [20, 45, 73, 78, 79]; 2 studies differentiated central and wide defenders and forwards/attackers (i.e. central defenders vs wide defenders vs midfielders vs central attackers vs wide attackers) [69, 70]; 1 study categorised wide players together (i.e. central defenders vs central midfielders vs wide players vs forwards) [62]; and 1 study differentiated central midfielders (i.e. central defenders vs wide defenders vs central attacking midfielders vs central defensive midfielders vs wide midfielders vs forwards) [8]. Three studies included goalkeepers within analysis [8, 62, 74].

A variety of velocity thresholds have been adopted within the 40 studies which categorised movement into velocity zones. The quantitative velocity thresholds are presented in Table 5. Four studies also quantified backwards running (>10 km·h-1) [35, 43, 44, 65]. The methods for establishing or adopting velocity thresholds included; arbitrary velocity thresholds which have previously been utilised in men’s soccer literature [26, 35, 43, 44, 51, 52, 65, 81, 89], sample-mean or individualised velocity thresholds derived from physical performance characteristics (e.g. sprint speed and maximal aerobic speed [20, 63, 72, 73, 84], velocity thresholds based on physical performance characteristics of women’s soccer players from existing literature [29, 33, 40, 41, 46, 57, 78, 79], derived velocity thresholds from match-play data of senior women’s soccer players [8, 38, 39, 70, 82], or a justification for velocity thresholds adopted was not provided [31, 49, 40, 56, 61, 62, 71, 73, 74, 77, 80, 86]. Additionally, 2 studies [34, 54], established velocity zones based on qualitative movement descriptors which had previously been utilised in men’s sports outside of soccer (e.g. hockey, rugby).

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Table 5. Velocity thresholds (km∙h-1) adopted by selected studies utilising quantitative velocity zones to quantify physical characteristics of women’s soccer match-play.

https://doi.org/10.1371/journal.pone.0268334.t005

Fourteen studies quantified acceleration and/or deceleration, however, studies predominantly provided no justification for the thresholds adopted (>1 m·s-2 [33, 79]; >2 m·s-2 [31, 77, 78]) [54, 64, 70]. Where a rationale was provided, thresholds were either; derived from physical performance characteristics of the sample (e.g. acceleration during a maximal sprint; >2.26 m·s-2) [20, 72, 84] or aligned to previous men’s soccer literature (>2 m·s-2) [57, 69]. Five of these studies presented accelerations and/or decelerations within acceleration/deceleration zones, however all studies adopted different thresholds (<1, >1 m·s-2 [80]; 1–2, >2 m·s-2 [77]; 0.5–1.99, 2–2.99, >3 m·s-2 [62]; 0.5–1.49, 1.5–2.99, >3 m·s-2 [74]; 1–2, 2–3, 3–4, >4 m·s-2 [57]).

3.4.1 Whole-match physical characteristics.

The majority of studies quantifying physical characteristics quantified whole-match absolute values (n = 33; [8, 20, 26, 29, 31, 32, 34, 35, 3840, 4245, 51, 52, 56, 57, 61, 62, 65, 66, 69, 70, 73, 74, 7881, 86, 89]. Table 6 presents whole-match absolute values of the most frequently reported physical characteristics (i.e. total distance (TD); TD in velocity zones (high-speed running (HSR), very-high-speed running (VHSR), and sprinting (SPR)), maximum velocity, number of accelerations and decelerations). Whilst S2 and S3 Tables present the specific HSR and SPR characteristics (i.e. number of efforts and repeated efforts, distance, duration, recovery duration), and acceleration and deceleration characteristics (i.e. number of efforts, total duration), respectively. In addition to the physical characteristics presented, studies quantified the number of game activities or (i.e. the total number of individual efforts across all velocity zones; 1326–1641) [43, 44, 65], and percentage of game activity for HSR (3.7–24%) [34, 43, 44, 51, 65] and SPR (0.54–2.7%) [34, 43, 44, 51] for senior international and domestic players.

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Table 6. Studies quantifying physical characteristics of women’s soccer match-play per whole-match as absolute data.

https://doi.org/10.1371/journal.pone.0268334.t006

Whole-match relative physical characteristics are presented in Table 7. In addition to those presented, Ramos et al. [33] also quantified relative repeated acceleration and SPR actions per playing position (0.12–0.15 n∙min-1). Only 14 studies quantified whole-match physical characteristics relative to match-duration [19, 20, 33, 36, 40, 46, 49, 50, 63, 71, 72, 80, 84, 89]. The majority of these studies reported relative values to explore the impact of contextual factors on physical characteristics [36, 46, 49, 50, 63, 71, 84, 89].

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Table 7. Studies quantifying physical characteristics of women’s soccer match-play per whole-match as relative data.

https://doi.org/10.1371/journal.pone.0268334.t007

3.4.2 Half-match physical characteristics.

Eighteen studies reported half-match physical characteristics [26, 31, 32, 34, 38, 40, 41, 43, 44, 49, 50, 54, 69, 70, 73, 74, 77, 78, 86, 89], with the reported data presented in S4 Table. In addition to the data presented, Mara et al. [69] identified players performed a total of 226 and 221 decelerations during the first and second half of elite senior match-play, respectively. Furthermore, Mara et al. [69] analysed six different accelerations and decelerations by intensity, categorising accelerations/decelerations dependent upon starting and final velocity. Only six studies reported half-match data according to playing position [26, 40, 41, 74, 77, 86], whilst the remaining studies presented sample or group averages.

3.4.3 Segmental physical characteristics.

Fifteen studies quantified physical characteristics by 15-minute segmental time-periods (i.e. 0–15, 15–30 minutes etc.) [31, 35, 38, 4245, 51, 54, 65, 69, 70, 74, 78], however, four studies selectively reported 15-minute time-periods [35, 43, 65, 74]. For example, Panduro et al. [74] presented only the initial and final 15-minute time-periods. Additionally, four studies reported physical characteristics as a mean of all 5-minute segmental periods within match-play [26, 43, 51, 78]. The data presented by segmental time-periods are presented in S5 Table.

3.4.4 Peak physical characteristics.

Eight studies quantified the peak physical characteristics of women’s soccer match-play [19, 20, 26, 43, 44, 51, 74, 78]. The reported peak data are presented in Table 8. Panduro et al. [74] also quantified mean heart rate, and observed values between 181 and 183 beats per minute (BPM) dependent upon playing position. All studies quantified peak data in 5-minute durations except for Harkness-Armstrong et al. [19] who quantified 1-10-minute durations for TD, HSR and VHSR. Only two studies [19, 20] adopted a moving average analysis to determine peak durations, whilst the remaining studies adopted a pre-determined segmental analysis approach (i.e. 0–5 minutes, 5–10 minutes etc.).

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Table 8. Peak physical characteristics of women’s soccer match-play.

https://doi.org/10.1371/journal.pone.0268334.t008

3.5 Technical characteristics

Of the twenty-six studies (38%) which included technical characteristics [2528, 31, 34, 37, 44, 47, 48, 50, 53, 55, 5860, 64, 65, 67, 68, 75, 76, 83, 85, 87, 88], six studies quantified technical characteristics in addition to the quantification of physical characteristics [26, 31, 34, 44, 50, 65], two studies predicted the impact of technical characteristics [53] and situational variables [60] on match outcome, and three studies further analysed heading [58, 75] or tackling actions [85] to explore frequency, characteristics, and incidence of associated injuries to understand potential areas of risk. Whilst four studies [28, 64, 68, 87] explored the technical-tactical characteristics of shooting and the respective play prior to shots by elite senior players.

The whole-match technical characteristics reported as player or team averages in these studies are presented in Table 9. In addition to the data presented, studies also presented characteristics as season or tournament totals [27, 31, 48, 59, 64, 67, 75, 83, 85, 87, 88] or position-specific characteristics (defenders vs midfielders vs forwards [44, 55, 58, 75, 87, 88] or central defenders vs wide defenders vs central midfielders vs wide midfielders vs forwards [37]), selected technical characteristics by pitch location (e.g. ball possession, touches, passes, ball recoveries, headers, shots [25, 27, 28, 55, 58, 64, 67, 68, 76, 87, 88], technical-tactical offensive characteristics [27, 28, 64, 68, 87], team set-piece characteristics [27, 31, 37, 47, 48, 67] and reported yellow or red cards [27, 55, 67]. Technical characteristics were predominantly quantified as whole-match characteristics, however three and two studies also presented results by halves [26, 44, 87] and 15-minute segmental periods [85, 87], respectively. Eleven studies included contextual factors within analysis; playing standard [34, 44], age-group [37, 58, 75], match outcome [60, 67, 83], team or opposition ranking [60, 68, 76], match location [60], competition type [50], and playing surface [55,60]. Lastly, only 9 studies [27, 37, 50, 53, 67, 68, 75, 76, 85] either included or provided a reference for the definition of technical characteristics.

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Table 9. Whole-match technical characteristics of women’s soccer match-play, presented as player or team averages.

https://doi.org/10.1371/journal.pone.0268334.t009

3.6 Tactical characteristics

Table 10 presents the two studies which explored tactical characteristics of women’s soccer match-play. Both studies [30, 90] quantified playing length and playing width (m), which were defined as the range of all 20 outfield players’ longitudinal positioning, and the range of all 20 outfield players’ lateral positioning, respectively. Zubillaga et al. [90] included additional tactical variables; distance from defender to goal-line (m; distance between least longitudinally advanced opposition defender and opposition goal-line, only when in possession), distance from attacker to goal-line (m; distance between most longitudinally advanced opposition attacker and opposition goal-line, only when in possession), distance between goalkeeper and defender (m; least longitudinally advanced defender), distance between goalkeeper and attacker (m; most longitudinally advanced attacking opponent), and individual playing area (m2; derived from dividing playing length and playing width by 20). Only open-play data, collected at 5-second intervals was included within Zubillaga et al. [90], whilst Tenga et al. [30] included ball-in-play data collected at 5 Hz frequency, excluding the initial 5-seconds following set-pieces.

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Table 10. Studies quantifying tactical characteristics of women’s soccer match-play.

https://doi.org/10.1371/journal.pone.0268334.t010

4 Discussion

This is the first systematic review to summarise women’s soccer match-play, including the technical, tactical and physical characteristics. A total of 69 studies were included, predominantly quantifying physical characteristics (68%), whilst 38% quantified technical characteristics, and only 3% quantified tactical characteristics. Studies reported whole-, half-, segmental and/or peak match characteristics for physical data, however studies reporting technical and tactical characteristics were predominantly limited to whole-match analysis. There has been an increase in the number of studies quantifying women’s soccer match-play characteristics in recent years, however, studies predominantly involved senior international (39%) and domestic players (43%), with only 10% quantifying match-play characteristics of youth (age-group) soccer players. Physical characteristics appear to increase between age-groups and playing standards, and are dependent upon playing position. Furthermore, there are between-half decrements in physical performance, with the opening 15-minutes of match-play the most physically demanding. Further research quantifying the technical and tactical characteristics is required to understand differences within and between age-groups and playing standards. The results of this review provide insight into the current match-play characteristics across different playing standards and playing positions, which will be beneficial for researchers and practitioners implementing evidence-based practice with women’s soccer players.

4.1 Methodologies for quantifying match-play characteristics

There are important methodological limitations to acknowledge when interpreting and comparing the extracted data. Firstly, over half of the included studies which reported number of teams involved only a single-team or single-club. This is problematic, as results may not be reflective of the population due to individual team/club strategies, tactics or playing styles, which may influence players’ physical, technical and tactical performance. Future research should adopt multi-club data collection approaches to overcome this issue. Secondly, positional categorisation was inconsistent, which may be a consequence of small sample size within studies. Only nine of the twenty-six studies [8, 19, 26, 33, 37, 51, 52, 74, 80] quantifying position-specific characteristics differentiated both central and wide defenders and midfielders. High-level positional categorisations (i.e. defender or midfielder) may not provide accurate insights into match-play characteristics, given the positional differences observed when central and wide players are compared [19, 37, 51, 74]. Thus, in addition to adopting a multi-club approach, future research should ensure sufficient sample size (participants and match observations) to differentiate central and wide playing-positions.

Thirdly, comparing physical characteristics quantified by different equipment requires caution due to between-system differences (i.e., video-based time-motion analyses vs GPS units vs optical tracking; 5 vs 10 vs 15 Hz GPS) [9194]. Furthermore, where studies have adopted the same data collection method (e.g. 10 Hz GPS units), differences exist between manufacturers’ hardware, firmware, data filtering and data processing methods [95, 96]. Therefore, direct comparison of findings between studies adopting different data collection methods may not be appropriate, and this further limits the potential insights which can be gained regarding the physical characteristics.

Lastly, a range of velocity and acceleration/deceleration thresholds have been adopted, with methods for establishing or adopting thresholds also differing between studies (e.g. arbitrary thresholds within men’s soccer, derived from physical performance characteristics, derived from match-play data of senior women’s soccer players). This has resulted in varying velocity and acceleration/deceleration zones, impacting comparisons between studies. There is a need for future research to establish a standardised approach for adopting velocity and acceleration/deceleration thresholds within women’s soccer to facilitate comparisons between studies. However, practitioners and researchers should also have an awareness and understanding of the potential impact thresholds may have on physical characteristics. For example, recent research found adopting senior-derived velocity thresholds for youth match-play, resulted in an underestimation of higher-speed distances as senior-derived thresholds are too excessive to accurately reflect the physical characteristics of youth players [97]. In this instance, adoption of senior-derived velocity thresholds may lead to misleading data and subsequently erroneous interpretations of the physical characteristics of youth match-play. Therefore, we recommended that researchers and practitioners make an informed-decision, depending upon their study aim or intended use of the data, as to whether it may be more appropriate to adopt senior-derived velocity thresholds or population-specific velocity thresholds.

The underpinning methodological limitations within the body of literature limits the insights which can be gained across women’s soccer populations. Consequently, researchers and practitioners using the match-play characteristics presented within this review, particularly the physical characteristics should be cautious in their interpretation and application of the data. Furthermore, subsequent discussion within this systematic review, is reflective of the limitations highlighted.

4.2 Whole-match characteristics

4.2.1 Absolute physical characteristics.

The TD covered during match-play by (outfield) women’s soccer players ranged between 5480–11160 m, and appeared to increase between playing standards, with similarities between senior international and domestic match-play. When considering the most common velocity thresholds implemented across respective velocity zones, HSR distance and percentage of TD also increased between playing standards (>15 km∙h-1: international = 13.8–17.9%; domestic = 5.9–17.0%; college = 10.1–13.5%) [26, 35, 43, 61, 62, 65, 74, 80, 81]. Whilst, VHSR distance and percentage of TD increased between youth and senior playing standards (>19 km∙h-1: domestic = 3.5–6.4%; youth = 1.5–4.2%) [8, 19, 82]. Similar SPR distances were covered by senior international and domestic players when considering the most commonly applied SPR threshold (>20 km∙h-1) [31, 40, 57, 73, 78, 79]. Whilst Ramos et al. [78] and Vescovi [40] observed a progressive increase in SPR distances covered by youth players, between U17, U20 and senior international age-groups, and U15, U16 and U17 domestic age-groups, respectively. The progressive increase in distances covered across playing standards and age-groups may be consequential of increasing physical capacities, greater match-specific fitness, reflective of increased technical-tactical demands or potentially differing contextual factors within playing standards. Furthermore, the increase in absolute distances between age-groups may be consequential of differing match-durations between youth and senior match-play [19, 37, 40].

The number of HSR and SPR efforts performed, differed between studies (HSR = 44–376; SPR = 4–70) [20, 29, 31, 40, 43, 44, 52, 63, 65, 69, 71, 73] which is likely due to differing methodological approaches (i.e. data collection, velocity thresholds). However, the mean distance per HSR and SPR efforts was predominantly <10 m [52, 63, 69] and <20 m [31, 40, 43, 52, 63, 65, 86], respectively, which suggests that acceleration ability is important within women’s soccer [12]. Yet, the number of accelerations differed vastly between studies [20, 31, 57, 62, 69, 74, 78, 79, 80], which may also be due to the different methods adopted. For example, the largest discrepancy was observed in studies adopting >2 m∙s-2 thresholds (i.e., 20 Hz radio frequency tracking = 161 accelerations [31]; 10 Hz GPS = 13–80 accelerations; [62, 78]; 25 Hz optical tracking = 342–475 accelerations [69]). Future research may aim to quantify acceleration and decelerations into zones or the starting and finishing velocities [69], to further understand the intensities of these movements [57, 62, 64, 77]. In comparison to distances covered in match-play, there is limited understanding of the accelerations and decelerations, across all playing standards. Therefore, future research should aim to further investigate these actions within match-play, particularly considering the associated high metabolic and mechanical loads [57], this information will be useful for preparing players for match-play, or informing player load monitoring, and injury prevention and rehabilitation practices.

4.2.2 Absolute technical characteristics.

There were consistent findings in technical characteristics across studies. Unsurprisingly, passes were the most frequent technical action in possession across playing standards, with an increasing pass success rate from youth (63–65%) [37] to senior match-play (67–80%) [26, 27, 31, 50, 55, 67, 76]. Tackles and interceptions were the most common defensive actions in senior and youth match-play [27, 31, 37, 55]. However, given the limited number of technical characteristics quantified across studies, it is difficult to explore potential differences in playing styles across playing standards. Additionally, the technical data presents on-the-ball technical actions for both in-possession (e.g. passing, crosses, or shots) and out-of-possession (e.g. clearances, interceptions, or tackles), which can be useful for practitioners informing coaching practice. However, given the small duration of time spent on the ball (senior = 67 ± 3 s; youth = 32–35 s) [26, 37] and low frequency of these technical actions, future research should aim to quantify off-the-ball technical actions, technical-tactical or physical-technical actions [17], such as pressing, pass effectiveness or sequences/patterns of play [47, 83], to gain better understanding of technical performance.

4.2.3 Absolute tactical characteristics.

Tactical characteristics referred to players’ positioning which provide insight to teams’ playing length and width, and players’ individual playing area, dependent upon ball location [30, 90]. The data presented may help practitioners determine appropriate dimensions for training drills, conditioned or small-sided games. However, the reported data lacks physical, technical and situational context. Therefore, further research is required to better understand team behaviours, and how physical and technical characteristics may interact with tactical strategies [27, 28, 68]. Furthermore, tactical characteristics are often dynamic, thus future research should aim to improve our understanding of the tactical characteristics across match-play, or prior-to and following key moments in match-play, and how contextual factors (e.g. match status) may affect tactical performance. However, it is important to acknowledge the challenges and complexities associated with this research and within practice [98, 99]. For instance, collecting sufficient data for appropriate analyses, the advanced theoretical and computational underpinning knowledge required for analyses, ability to work with complex datasets, and multidisciplinary collaboration. We anticipate that as the involvement of big data technologies within the women’s game increases, and more commercial data providers provide readily-available access to physical and technical data [53, 55], the body of literature exploring tactical characteristics in senior international and professional playing standards will grow accordingly.

4.2.4 Position-specific absolute characteristics.

Senior goalkeepers cover 4445–5622 m during match-play [8, 62, 74], predominantly at lower speeds. Unsurprisingly, goalkeepers covered less TD, HSR, VHSR and SPR distances, than outfield players. However, given the different movements and technical-tactical skills associated with goalkeeping, position-specific characteristics (e.g. number, intensity, and direction of movements such as diving, jumping or kicking) [100] would be more appropriate than distances covered. Future research should aim to improve our very limited understanding of women’s soccer goalkeeper match-play characteristics, to help inform goalkeeper-specific training and coaching practice.

When studies differentiated central and wide players, central defenders typically covered the lowest TD, HSR, VHSR, and SPR distances [8, 19, 20, 26, 51, 70, 74, 78, 79], performed the least HSR and SPR efforts [20, 52, 73], and had the least total and average duration of possession, touches per possession, and offensive touches during youth match-play [37]. Central midfielders covered the most TD [8, 19, 26, 51, 62, 74], and had the lowest maximum velocity [8, 19]. Furthermore, Harkness-Armstrong et al. [37] reported central midfielders had the greatest active involvement in youth match-play with the most offensive touches and passes, as well as the most tackles. Considering HSR distance, three studies reported wide midfielders covered the most [8, 19, 74], whilst Bradley et al. [26] and Datson et al. [51] reported forwards and central midfielders covered the most, respectively. Forwards and wide midfielders consistently covered the most VHSR and SPR distances [8, 19, 51, 74], and attempted the most dribbles, crosses and shots during youth match-play [37]. There was a discrepancy in position-specific accelerations and decelerations between studies, however this may be due to respective samples adopting notably different acceleration and/or deceleration thresholds. Although comparison of physical and technical characteristics are limited due to methodological differences, clear differences exist between playing positions and the data reported within the current review can be used to inform position-specific training drills and coaching practice to prepare players accordingly for match-play.

4.2.5 Relative characteristics.

Between playing standards, college players covered the highest relative TD [36, 49, 50, 71, 89], whilst senior international players covered more relative TD [20, 33, 72, 84] than senior domestic players [46, 63, 80], and youth players covered the least relative TD [19, 40]. Where similar SPR thresholds (>20 km∙h-1) were adopted, the data suggests an increase in relative SPR distance between playing standards [33, 46, 50, 72].

Quantifying relative characteristics normalises data to minutes played, removing potential differences in data due to match-duration across match observations, which is particularly useful for comparisons between groups, especially age-groups with differing match durations [19]. However, it is important to acknowledge that relative whole-match data includes ball out of play time, which has been found to be between 38.0 and 41.6% of the time in women’s soccer [37, 51]. Whilst inclusion of ball out of play time has been found to underestimate physical characteristics in men’s soccer populations [101, 102], the effect on women’s soccer players is yet to be quantified. Therefore, future research across women’s soccer populations should explore the differences between ball in play and whole-match characteristics, to better understand the physical and technical characteristics, and to ensure practitioners can implement coaching practice and training drills which better represent match-play demands.

4.2.6 Influence of contextual factors.

Excluding playing standard, age-group and playing position, only fourteen studies [36, 38, 46, 49, 50, 55, 63, 67, 68, 71, 76, 83, 84, 89] quantified the influence of contextual factors on match-play characteristics. Studies predominantly reported whole-match characteristics, with physical characteristics primarily presented as relative values. Whereas technical data were either presented as absolute or relative to match duration, which may lead to erroneous interpretation when comparing the effect of contextual factors. For example, Póvoas et al. [76] found international low-ranking teams performed more successful tackles, recoveries and challenges than high-ranking teams. However, this may be due to low-ranking teams having less possession, and thus more opportunity to perform defensive actions than high-ranking teams. Therefore, future research should present possession-dependent technical characteristics relative to possession status [37]. Additionally, studies quantified contextual factors in isolation (i.e. match location, match congestion, or opposition quality), with only Trewin et al. [84] combining contextual factors (i.e. match outcome and opposition quality; win vs higher ranked opponent). Quantifying contextual factors in isolation may be consequential of limited sample size or match observations. However, caution should be taken when interpreting the influence of isolated contextual factors given the complex, multifaceted nature of match-play performances [103].

Acknowledging these limitations; high temperatures [36, 84], altitude [49, 84], match congestion (<42h) [71, 84], playing against lower ranked opposition [38, 84], playing on grass rather than artificial turf [46], in-conference matches as opposed to out-of-conference fixtures (type of competition) [50], and competing in matches post-season compared to regular-season (seasonal changes) [89], resulted in reduced physical characteristics. However, all studies were conducted with a single-team, thus further research with a multi-club approach, larger sample size and greater number of match observations is required to identify whether these findings are generalizable to the wider women’s soccer population. Four studies quantified match characteristics according to match outcome [46, 67, 83, 84]. Differences in physical and technical performances were observed in teams who won, drew or lost across studies, however caution should be taken when interpreting this data. Grouping match observations by match outcome can be an overly-simplistic approach, which does not reflect the fluidity of match status during observations, and may subsequently lead to erroneous categorisation. For example, a team may score within the final minutes of match-play after drawing for the majority of the match, yet be categorised as a win. Therefore, we recommend future research explores the influence of match status (i.e. drawing, winning or losing) as opposed to match outcome on match-play characteristics. Finally, one study [63] attempted to explore sex-specific considerations on match-play performance. The authors compared physical characteristics during match-play between the follicular and luteal phase of the menstrual cycle. However, due to the complexities of data collection (e.g. small sample size due to strict participant inclusion criteria, individual and match-to-match variability) the authors could not attribute changes in performance to the menstrual cycle. Whilst there are considerable complexities to conducting such research [104], further work is warranted to explore the influence of sex-specific considerations on match-play performances.

4.3 Half-match characteristics

The twenty-two studies which quantified half-match characteristics [26, 31, 32, 34, 3841, 43, 44, 49, 50, 54, 69, 70, 73, 74, 77, 78, 86, 87, 89] predominantly reported between-half decrements in performances. Reductions in TD (0.2–29.7%), HSR (0.9–26.6%), VHSR (4.6–12.0%) and SPR (4.6–29.5%) distances were observed between-halves, across all playing standards. Only two instances of increased HSR distance between-halves were observed [26, 40]. Whilst Vescovi & Favero [41] were the only authors to report an increase in SPR distance (college = 3.1–32.2%) between-halves. This inconsistency may be consequential of the authors’ data inclusion of half-match observations as opposed to whole-match, which resulted in differing numbers of player observations which may have involved different player samples in each half, and thus may not be a true reflection of between-half differences in performance. Fewer HSR and sprint efforts were performed during the second-half, whilst, the sprint characteristics (e.g. mean distance per sprint, mean duration per sprint) [40, 69, 73] also reduced between halves, as the interval between sprint efforts increased (10–15%) [70, 73]. Additionally, senior domestic players performed fewer accelerations (5.5–52.1%) and decelerations (2.2–29.5%) during the second-half [69, 74, 77].

Interestingly, alongside the observed reductions in physical performances, senior domestic players had less individual time in possession in the second half (-7.8%; 34.6s vs 31.9s) [26]. The remaining technical characteristics show similar technical performances between-halves. However, only a small number of technical characteristics were quantified, which were predominantly infrequent on-the-ball technical actions (e.g. duels, headers and tackles) [26, 44], therefore limited insight can be gained as to potential differences in technical performances between-halves. Additionally, no position-specific technical characteristics were quantified which is problematic, as player averages may provide limited insight into technical performances, given that technical characteristics differ between positions for whole-match [37, 55, 88]. Furthermore, position-specific differences and between-half decrements were observed in physical half-match characteristics [26, 74, 77]. Therefore, future research across playing standards should aim to quantify half-match physical, technical and tactical characteristics according to playing position. This information will enable practitioners to design and deliver position-specific practices to prepare players for match-play and improve their ability to sustain performances between-halves.

4.4 Segmental characteristics

The opening 15-minutes of match-play was consistently the most demanding period across all physical characteristics quantified. Reductions in TD (4.9–26.2%) [35, 38, 44, 70, 74, 78], HSR (15.5–41.0%) [31, 35, 38, 43, 44, 51, 65, 70, 74, 78], VHSR (25.5–35.7%) [74], and SPR (7.8–73%) [31, 38, 43, 44, 70, 74, 78] distances, number of accelerations and decelerations (3.8–66.7%) [74, 78], alongside an increase in mean interval between HSR and SPR efforts (45.5%; 48.5%) [70], were observed across studies from the opening and final 15-minutes of match-play. Only four instances of contrasting data were reported [35, 78], with increased performances observed between the first and last 15-minute period. However, these inconsistencies may also be explained by the studies’ samples. For instance, Bendiksen et al. [35] only observed one team for one match, and Ramos et al. [78] only included 12 players from one team. Consequently, data may be influenced by small sample size and specific team strategies, and therefore may not be representative of the wider populations.

The observed reductions in physical characteristics across match-play (half-match and segmental), may be consequential of reduced physical performance capacities due to physical fatigue, pacing strategies, or an increased perception of effort due to mental fatigue [18, 44, 66]. Furthermore, technical-tactical performance, situational and contextual variables may also contribute to these reductions, however physical characteristics have predominantly been quantified in isolation. Thus, future research should aim to quantify technical and tactical characteristics alongside physical characteristics, as well as exploring the influence of situational and contextual factors, to understand their influence in reductions in physical performance across match-play. For example, playing styles, team strategies or formation may differ over the duration of match-play but also in response to match status [105], substitution strategies influence on observed players’ physical characteristics [106, 107], or whether ball-in play time differs across the match and thus influences physical performances [96, 108]. This information would be important for practitioners when informing technical-tactical drills or conditioned games to prepare players for the fluctuating demands of the game, or training prescription which aims to increase players’ physical capabilities to sustain physical performances throughout match-play, but also for informing tactical coaching interventions during match-play.

4.5 Peak characteristics

All eight studies reporting peak characteristics, quantified physical characteristics for a 5-minute period [19, 20, 26, 43, 44, 51, 74, 78]. TD covered appeared to increase with increasing playing standards [19, 20, 74, 78]. However, comparison of HSR, VHSR and SPR distances and number of accelerations and decelerations, during peak-periods is limited, given the different methods adopted across the eight studies (e.g. five HSR velocity thresholds, three accelerations/ decelerations thresholds). Furthermore, two studies [19, 20] quantified peak periods by a moving-averages approach, whilst the remaining studies adopted a pre-determined segmental analysis approach. Previous research within other sporting populations (e.g. men’s soccer, men’s rugby union) found adopting segmental periods can underestimate peak TD and HSR distances by up to 25% in comparison to moving average analysis [109112]. Therefore, it is likely peak-data quantified via segmental analysis [26, 43, 44, 51, 74, 78] underestimates the peak characteristics of women’s soccer players. Therefore, it is not possible to determine whether differences across playing standards is reflective of the increased demands, or a consequence of different methods. Thus, practitioners utilising peak data to inform coaching practice and training programme design to prepare players for the worst-case scenarios in match-play should be aware of the methods of analysis adopted. Furthermore, future research should adopt a moving average analysis when quantifying peak periods of women’s soccer.

Only one study [19] quantified peak periods at differing durations (1–10 minutes), and observed youth players covered the greatest distances during the 1-minute period, whilst relative distances reduced as duration increased, with the least distances covered during the 10-minute period. This is consistent with previous research in men’s soccer [110, 111]. Whilst underlying reasons why this reduction may occur is not known (e.g. reduction in physiological capacity; differing technical-tactical demands between peak periods) [96], duration-specific peak characteristics can be used to inform duration-specific training programme design or coaching practice. However, it is not appropriate to inform duration-specific training drills when duration-specific data does not align (i.e. 5-minute data to inform 3-minute training drills) [110]. This is problematic, as only 5-minute peak periods have been quantified for senior populations. Therefore, future research should quantify peak characteristics of 1-10-minute durations, to understand the duration-specific worst case scenarios within senior match-play, which can be used to inform duration-specific practices to optimally prepare players for the most physically demanding periods of match-play.

Four studies [19, 20, 74, 78] quantified position-specific peak physical characteristics. During 5-minute peak periods, central defenders typically covered the least TD [19, 20, 74], and HSR distance [19, 20, 74, 78], whilst central defenders and central midfielders covered the least VHSR [19, 74], and SPR [74, 78] distances. Where studies differentiated central and wide defenders and midfielders [19, 74], central midfielders covered the greatest TD, and wide midfielders covered the greatest HSR, VHSR and SPR distances. Furthermore, Harkness-Armstrong et al. [19] reported position-specific differences in TD, HSR and VHSR across 1–10 minute peak durations. The data indicates that peak physical characteristics are position-dependent, thus practitioners should implement position-specific practices to prepare players accordingly for the varying worst case scenarios in match-play. Additionally, the data highlights the need for research to quantify peak characteristics beyond TD (e.g. distances in velocity zones, number of accelerations) to facilitate position-specific differentiation of specific worst-case scenarios.

Consistent with other areas within this review, peak characteristics have quantified physical characteristics in isolation, which provides limited insight into the true demands and context of these worst-case scenarios within match-play [17]. Additionally, recent research in elite men’s soccer [113] found physical peak characteristics lack context due to the multifaceted nature of worst case scenarios, which consequently results in high variability. Therefore, future research should; quantify the associated technical and tactical characteristics during peak physical periods, to understand how technical-tactical roles may influence worst case scenarios; attempt to quantify the peak technical and tactical periods of match-play and the associated physical characteristics; explore how contextual factors (e.g. match status, formation, opposition quality, ball possession) influence worst case scenarios [111, 114], and quantify the variability of peak characteristics in women’s soccer match-play. As previously discussed, attempting to integrate the physical, technical and tactical characteristics, and understand the variation within and between matches will provide greater insight into these worst-case scenarios, and enable evidence-informed design and prescription of coaching practice and training programmes to optimally prepare players for the most demanding periods of match-play.

4.6 Limitations

This review has presented study limitations throughout, and the caution required when interpreting results or informing practical applications. For example, this review has identified key methodological limitations within the literature which limits comparisons between studies, including; single-team samples; differing data collection methods; and no standardised velocity and acceleration/deceleration thresholds. Consequently, researchers and practitioners should be cautious in their interpretation of the reported data, whilst future research requires greater consistency in the methods adopted to facilitate comparisons between studies. For example, multi-club samples to ensure findings are generalizable to the population, positional-categorisation of players which differentiate central and wide players as opposed to high-level categorisation (i.e. defenders vs midfielders vs forwards), and to establish and adopt standardised velocity, acceleration and deceleration thresholds/zones for women’s soccer, to facilitate comparisons between and within playing standards.

The heterogeneity of the included studies’ samples and methodologies prevented the inclusion of a meta-analyses within the current systematic review. Given the extent of the current review in summarising all physical, technical, and tactical characteristics during match-play, across all playing standards of women’s soccer, there is a very large breadth of results which may be overwhelming. However, given the recent growth, development, and investment within women’s soccer, the authors strongly believe there is a timely need for the current review; to collate all current evidence regarding women’s soccer match-play characteristics, and provide practitioners with a critical resource which can be utilised to develop evidence-informed practice within women’s soccer populations.

5 Conclusions

The quantification and understanding of match-play characteristics is important for informing practices across women’s soccer populations. This is the first systematic review to summarise the scientific literature evaluating the match-play characteristics of women’s soccer, and presents the physical, technical and tactical characteristics of women’s soccer match-play across age-groups, playing standards and playing positions. Furthermore, this review provides a critical evidence-based resource which can be used to inform population-specific practices across women’s soccer playing standards.

The current review has identified that physical characteristics appear to increase between playing standards and differ between playing positions. Furthermore, between-half reductions in physical characteristics were apparent, whilst the opening 15-minutes of match-play was consistently the most physically demanding. Additionally, peak physical characteristics were primarily quantified via a segmental analysis, which may underestimate the true worst-case scenarios of match-play. Therefore, research which quantifies the peak demands for differing durations via a moving-averages method is warranted across women’s soccer playing standards. Additionally, further research is needed to understand technical and tactical characteristics of women’s soccer match-play, and how performances may differ across playing standards. Furthermore, research should aim to integrate physical, technical and tactical characteristics rather than quantifying characteristics in isolation, to gain a holistic understanding of match-performance. In addition, further evidence is required regarding contextual factors within match-play, to understand how the characteristics players face during match-play may vary. Future research may also attempt to better our understanding of the match-to-match variation within women’s soccer populations. As currently only two studies have quantified match-to-match variation of physical characteristics utilising single-team samples, this is therefore not generalizable to the wider population [20, 72]. Finally, there is a heavy bias towards research quantifying match-play characteristics of senior players. The lack of research and subsequent knowledge and understanding of youth match-play characteristics (<U17) is problematic. Thus, further research is necessary within youth populations, to inform long-term talent development, transition of youth players across the talent pathway, and talent identification processes.

Supporting information

S1 Table. Whole-, half-, segmental- and peak-match characteristics of women’s soccer players, quantified via heart rate monitors.

https://doi.org/10.1371/journal.pone.0268334.s002

(DOCX)

S2 Table. Whole-match high-speed running and sprinting match-play characteristics of women’s soccer players.

https://doi.org/10.1371/journal.pone.0268334.s003

(DOCX)

S3 Table. Whole-match acceleration and deceleration characteristics of women’s soccer players.

https://doi.org/10.1371/journal.pone.0268334.s004

(DOCX)

S4 Table. Half-match physical characteristics of women’s soccer match-play.

https://doi.org/10.1371/journal.pone.0268334.s005

(DOCX)

S5 Table. Segmental physical characteristics of women’s soccer-match-play.

https://doi.org/10.1371/journal.pone.0268334.s006

(DOCX)

References

  1. 1. Fédération Internationale de Football Association (FIFA). Women’s football strategy. 2018. https://digitalhub.fifa.com/m/baafcb84f1b54a8/original/z7w21ghir8jb9tguvbcq-pdf.pdf
  2. 2. Fédération Internationale de Football Association (FIFA). Women’s football member associations survey report 2019. 2019. https://digitalhub.fifa.com/m/231330ded0bf3120/original/nq3ensohyxpuxovcovj0-pdf.pdf
  3. 3. Fédération Internationale de Football Association (FIFA). The vision 2020–2023. 2020. https://publications.fifa.com/en/vision-report-2021/
  4. 4. Union of European Football Associations (UEFA). Together for the future of football: UEFA strategy 2019–2024. 2019. https://www.uefa.com/MultimediaFiles/Download/uefaorg/General/02/59/06/32/2590632_DOWNLOAD.pdf
  5. 5. Union of European Football Associations (UEFA). Time for action: UEFA strategy 2019–2024. 2019. https://www.uefa.com/MultimediaFiles/Download/uefaorg/Womensfootball/02/60/51/38/2605138_DOWNLOAD.pdf
  6. 6. Union of European Football Associations (UEFA). Women’s football across the national associations 2017. 2017. https://www.uefa.com/MultimediaFiles/Download/OfficialDocument/uefaorg/Women%27sfootball/02/51/60/57/2516057_DOWNLOAD.pdf
  7. 7. Fédération Internationale de Football Association (FIFA). Physical analysis of the FIFA Women’s World Cup France 2019. 2019. https://digitalhub.fifa.com/m/4f40a98140d305e2/original/zijqly4oednqa5gffgaz-pdf.pdf
  8. 8. Scott D, Haigh J, Lovell R. Physical characteristics and match performances in women’s international versus domestic-level football players: a 2-year, league-wide study. Sci Med Football. 2020; 4(3): 211–215.
  9. 9. Okholm Kryger K, Wang A, Mehta R, Impellizzeri FM, Massey A, McCall A. Research on women’s football: a scoping review. 2021. Sci Med Football. (epub ahead of print)
  10. 10. Datson N, Hulton A, Andersson H, Lewis T, Weston M, Drust B, et al. Applied physiology of female soccer: an update. Sports Med. 2014; 44(9), 1225–1240. pmid:24803162
  11. 11. Davis JA, Brewer J. Applied physiology of female soccer players. Sports Med. 1993; 16(3), 180–189. pmid:8235191
  12. 12. Griffin J, Larsen B, Horan S, Keogh J, Dodd K., Andreatta M, et al. Women’s football: An examination of factors that influence movement patterns. J Strength Cond Res. 2020; 34(8), 2384–2393. pmid:32412968
  13. 13. Martinez-Lagunas V, Niessen M, Hartmann U. Women’s football: Player characteristics and demands of the game. J Sport and Health Sci. 2014; 3(4), 258–272.
  14. 14. Randell RK, Clifford T, Drust B, Moss SL, Unnithan VB, De Ste Croix MBA, et al. Physiological characteristics of female soccer players and health and performance considerations: a narrative review. Sports Med. 2021; 51(7); 1377–1399. pmid:33844195
  15. 15. Vescovi JD, Fernandes E, Klas A. Physical demands of women’s soccer matches: a perspective across the developmental spectrum. Front Sports Act Living. 2021; 3. pmid:33937752
  16. 16. Pae CU. Why systematic review rather than narrative review?. Psychiatry Investig. 2015; 12(3), 417–419. pmid:26207140
  17. 17. Bradley PS, Ade JD. Are current physical match performance metrics in elite soccer fit for purpose or is the adoption of an integrated approach needed?. Int J Sports Physiol Perform. 2018; 13(5), 656–664. pmid:29345547
  18. 18. Paul DJ, Bradley PS, Nassis GP. Factors affecting match running performance of elite soccer players: shedding some light on the complexity. Int J Sports Physiol Perform. 2015; 10(4), 516–519. pmid:25928752
  19. 19. Harkness-Armstrong A, Till K, Datson N, Emmonds S. Whole and peak physical characteristics of elite youth female soccer match-play. J Sports Sci. 2021; 39(12), 1320–1329. pmid:33377422
  20. 20. Trewin J, Meylan C, Varley MC, Cronin J. The match-to-match variation of match-running in elite female soccer. J Sci Med Sport. 2018; 21(2), 196–201. pmid:28595867
  21. 21. Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7), e1000097. pmid:19621072
  22. 22. Castellano J, Alvarez-Pastor D, Bradley PS. Evaluation of research using computerised tracking systems (Amisco® and Prozone®) to analyse physical performance in elite soccer: a systematic review. Sports Med. 2014; 44(5), 701–712. pmid:24510701
  23. 23. Trewin J, Meylan C, Varley MC, Cronin J. The influence of situational and environmental factors on match-running in soccer: a systematic review. Sci Med Football. 2017; 1(2), 183–194.
  24. 24. Rohatgi A. WebPlotDigitizer: Version 4.4. 2020. https://automeris.io/WebPlotDigitizer
  25. 25. Althoff K, Kroiher J, Hennig EM. A soccer game analysis of two World Cups: playing behavior between elite female and male soccer players. Footwear Sci. 2010; 2(1), 51–56.
  26. 26. Bradley PS, Dellal A, Mohr M, Castellano J, Wilkie A. Gender differences in match performance characteristics of soccer players competing in the UEFA Champions League. Hum Mov Sci. 2014; 33, 159–171. pmid:24139663
  27. 27. Casal CA, Maneiro R, Ardá A, Losada JL. Gender differences in technical-tactical behaviour of La Liga Spanish football teams. J Hum Sport Exerc. 2021; 16(1).
  28. 28. Gómez MÁ, Álvaro J, Barriopedro M. Behaviour patterns of finishing plays in female and male soccer. Kronos. 2008. 8(15), 5–14.
  29. 29. McFadden BA, Walker AJ, Bozzini BN, Sanders DJ, Arent SM. Comparison of internal and external training loads in male and female collegiate soccer players during practices vs. games. J Strength Cond Res. 2020; 34(4), 969–974. pmid:31972824
  30. 30. Tenga A, Zubillaga A, Caro O, Fradua L. Explorative study on patterns of game structure in male and female matches from elite Spanish soccer. Int J of Perf Anal Sport. 2015; 15(1), 411–423.
  31. 31. Andersen TB, Krustrup P, Bendiksen M, Ørntoft C, Randers MB, Pettersen SA. Kicking velocity and effect on match performance when using a smaller, lighter ball in women’s football. Int J Sports Med. 2016; 37(12): 996–972. pmid:27551936
  32. 32. Ohlsson A, Berg L, Ljunberg H, Södermann K, Stålnacke BM. Heart rate distribution during training and a domestic league game in Swedish elite female soccer players. Ann Sport Med Res. 2015; 2(4), 1025.
  33. 33. Ramos GP, Datson N, Mahseredjian F, Lopes TR, Coimbra CC, Prado LS, et al. Activity profile of training and matches in Brazilian Olympic female soccer team. Sci Med Football. 2019; 3(3), 231–237.
  34. 34. Gabbett TJ, Mulvey MJ. Time-motion analysis of small-sided training games and competition in elite women soccer players. J Strength Cond Res. 2008; 22(2), 543–552. pmid:18550972
  35. 35. Bendiksen M, Pettersen SA, Ingebrigtsen J, Randers MB, Brito J, Mohr M, et al. Application of the Copenhagen Soccer Test in high-level women players–locomotor activities, physiological response and sprint performance. Hum Mov Sci. 2013; 32(6), 1430–1442. pmid:24016711
  36. 36. Benjamin CL, Hosokawa Y, Curtis RM, Schaefer DA, Bergin RT, Abegg MR, et al. Environmental conditions, preseason fitness levels, and game workload: analysis of a female NCAA DI national championship soccer season. J Strength Cond Res. 2020; 34(4), 988–994. pmid:31996612
  37. 37. Harkness-Armstrong A, Till K, Datson N, Emmonds S. Technical characteristics of elite youth female soccer match-play: position and age group comparisons between under 14 and under 16 age groups. International Journal of Performance Analysis in Sport. 2020; 20(6), 942–959.
  38. 38. Hewitt A, Norton K, Lyons K. Movement profiles of elite women soccer players during international matches and the effect of opposition’s team ranking. J Sports Sci. 2014; 32(20), 1874–1880. pmid:24786319
  39. 39. Park LA, Scott D, Lovell R. Velocity zone classification in elite women’s football: where do we draw the lines? Sci Med Football. 2019; 3(1), 21–28.
  40. 40. Vescovi JD. Motion characteristics of youth women soccer matches: Female Athletes in Motion (FAiM) Study. Int J Sports Med. 2014; 35(02), 110–117. pmid:23868688
  41. 41. Vescovi JD, Favero TG. Motion characteristics of women’s college soccer matches: Female Athletes in Motion (FAiM) study. Int J Sports Physiol Perform. 2014; 9(3), 405–414. pmid:24755966
  42. 42. Williams JH, Hoffman S, Jaskowak DJ, Tegarden D. Physical demands and physiological responses of extra time matches in collegiate women’s soccer. Sci Med Football. 2019; 3(4), 307–312.
  43. 43. Andersson HÅ, Randers MB, Heiner-Møller A, Krustrup P, Mohr M. Elite female soccer players perform more high-intensity running when playing in international games compared with domestic league games. J Strength Cond Res. 2010; 24(4), 912–919. pmid:20300037
  44. 44. Mohr M, Krustrup P, Andersson H, Kirkendal D, Bangsbo J. Match activities of elite women soccer players at different performance levels. J Strength Cond Res. 2008; 22(2), 341–349. pmid:18550946
  45. 45. Paulsen KM, Butts CL, McDermott BP. Observation of women soccer players’ physiology during a single season. J Strength Cond Res. 2018; 32(6), 1702–1707. pmid:29786626
  46. 46. Vescovi JD, Falenchuk O. Contextual factors on physical demands in professional women’s soccer: female athletes in motion study. Eur J Sport Sci. 2019; 19(2), 141–146. pmid:29961405
  47. 47. Alcock A. Analysis of direct free kicks in the women’s football World Cup 2007. Eur J Sport Sci. 2010; 10(4):279–84.
  48. 48. Beare H, Stone JA. Analysis of attacking corner kick strategies in the FA women’s super league 2017/2018. Int J Perf Anal Sport. 2019; 19(6), 893–903.
  49. 49. Bohner JD, Hoffman JR, McCormack WP, Scanlon TC, Townsend JR, Stout JR, et al. Moderate altitude affects high intensity running performance in a collegiate women’s soccer game. J Hum Kin. 2015; 47, 147–154. pmid:26557199
  50. 50. Bozzini BN, McFadden BA, Walker AJ, Arent SM. Varying demands and quality of play between in-conference and out-of-conference games in Division I collegiate women’s soccer. J Strength Cond Res. 2020; 34(12), 3364–3368. pmid:33021584
  51. 51. Datson N, Drust B, Weston M, Jarman IH, Lisboa PJ, Gregson W. Match physical performance of elite female soccer players during international competition. J Strength Cond Res. 2017; 31(9), 2379–2387. pmid:27467514
  52. 52. Datson N, Drust B, Weston M, Gregson W. Repeated high-speed running in elite female soccer players during international competition. Sci Med Football. 2019; 3(2), 150–156.
  53. 53. de Jong LM, Gastin PB, Angelova M, Bruce L, Dwyer DB. Technical determinants of success in professional women’s soccer: a wider range of variables reveals new insights. PloS One. 2020; 15(10), e0240992. pmid:33091064
  54. 54. Gabbett TJ, Wiig H, Spencer M. Repeated high-intensity running and sprinting in elite women’s soccer competition. Int J Sports Physiol Perform. 2013; 8(2), 130–138. pmid:22868237
  55. 55. Garcia-Unanue J, Fernandez-Luna A, Burillo P, Gallardo L, Sanchez-Sanchez J, Manzano-Carrasco S, et al. Key performance indicators at FIFA Women’s World Cup in different playing surfaces. Plos One. 2020; 15(10), e0241385. pmid:33095837
  56. 56. Gentles JA, Coniglio CL, Besemer MM, Morgan JM, Mahnken MT. The demands of a Women’s college soccer season. Sports. 2018; 6(1), 16–27. pmid:29910320
  57. 57. Griffin J, Newans T, Horan S, Keogh J, Andreatta M, Minahan C. Acceleration and high-speed running profiles of women’s international and domestic football matches. Front Sports Act Living. 2021; 3. (epub ahead of print) pmid:33842879
  58. 58. Harriss A, Johnson AM, Walton DM, Dickey JP. The number of purposeful headers female youth soccer players experience during games depends on player age but not player position. Sci Med Football. 2019; 3(2), 109–114.
  59. 59. Hjelm J. The bad female football player: women’s football in Sweden. Soccer Soc. 2011; 12(2), 143–158.
  60. 60. Ibáñez SJ, Pérez-Goye JA, Courel-Ibáñez J, García-Rubio J. The impact of scoring first on match outcome in women’s professional football. Int J Perf Anal Sport. 2018; 18(2), 318–326.
  61. 61. Ishida A, Bazyler CD, Sayers AL, Mizuguchi S, Gentles JA. Acute effects of match-play on neuromuscular and subjective recovery and stress state in Division I collegiate female soccer players. J Strength Cond Res. 2021; 35(4), 976–982. pmid:33555825
  62. 62. Jagim AR, Murphy J, Schaefer AQ, Askow AT, Luedke JA, Erickson JL, et al. Match demands of women’s collegiate soccer. Sports. 2020; 8(6), 87–97. pmid:32545603
  63. 63. Julian R, Skorski S, Hecksteden A, Pfeifer C, Bradley PS, Schulze E, et al. Menstrual cycle phase and elite female soccer match-play: influence on various physical performance outputs. Sci Med Football. 2020. pmid:35077332
  64. 64. Konstadinidou X, Tsigilis N. Offensive playing profiles of football teams from the 1999 Women’s World Cup Finals. Int J Perf Anal Sport. 2005; 5(1), 61–71.
  65. 65. Krustrup P, Mohr M, Ellingsgaard H, Bangsbo J. Physical demands during an elite female soccer game: importance of training status. Med Sci Sports Exerc. 2005; 37(7), 1242–1248. pmid:16015145
  66. 66. Krustrup P, Zebis M, Jensen JM, Mohr M. Game-induced fatigue patterns in elite female soccer. J Strength Cond Res. 2010; 24(2), 437–441. pmid:20072057
  67. 67. Kubayi A, Larkin P. Technical performance of soccer teams according to match outcome at the 2019 FIFA Women’s World Cup. Int J Perf Anal Sport. 2020; 20(5), 908–916.
  68. 68. Mara JK, Wheeler KW, Lyons K. Attacking strategies that lead to goal scoring opportunities in high level women’s football. Int J Sport Sci Coach. 2012; 7(3), 565–577.
  69. 69. Mara JK, Thompson KG, Pumpa KL, Morgan S. The acceleration and deceleration profiles of elite female soccer players during competitive matches. J Sci Med Sport. 2017; 20(9), 867–872. pmid:28173971
  70. 70. Mara JK, Thompson KG, Pumpa KL, Morgan S. Quantifying the high-speed running and sprinting profiles of elite female soccer players during competitive matches using an optical player tracking system. J Strength Cond Res. 2017; 31(6), 1500–1508. pmid:28538298
  71. 71. McCormack WP, Hoffman JR, Pruna GJ, Scanlon TC, Bohner JD, Townsend JR, et al. Reduced high-intensity-running rate in college women’s soccer when games are separated by 42 hours. Int J Sports Physiol Perform. 2015; 10(4), 436–439. pmid:25365765
  72. 72. Meylan C, Trewin J, McKean K. Quantifying explosive actions in international women’s soccer. Int J Sports Physiol Perform. 2017; 12(3), 310–315. pmid:27295719
  73. 73. Nakamura FY, Pereira LA, Loturco I, Rosseti M, Moura FA, Bradley PS. Repeated-sprint sequences during female soccer matches using fixed and individual speed thresholds. J Strength Cond Res. 2017; 31(7), 1802–1810. pmid:27662490
  74. 74. Panduro J, Ermidis G, Røddik L, Vigh‐Larsen JF, Madsen EE, Larsen MN, et al. Physical performance and loading for six playing positions in elite female football: full‐game, end‐game, and peak periods. Scand J Med Sci Sports. 2021; (epub ahead of print) pmid:33749045
  75. 75. Peek K, Vella T, Meyer T, Beaudouin F, McKay M. The incidence and characteristics of purposeful heading in male and female youth football (soccer) within Australia. J Sci Med Sport. 2021; 24(6), 603–608. pmid:33414022
  76. 76. Póvoas S, Ascensão A, Magalhães J, Silva P, Wiig H, Raastad T, et al. Technical match actions and plasma stress markers in elite female football players during an official FIFA Tournament. Scand J Med Sci Sports. 2020. (epub ahead of print) pmid:33373070
  77. 77. Principe VA, Seixas-da-Silva IA, Vale RGDS, Nunes RDAM. GPS technology to control of external demands of elite Brazilian female football players during competitions. Retos. 2021; 40, 18–26.
  78. 78. Ramos GP, Nakamura FY, Pereira LA, B W Junior, Mahseredjian F, Wilke CF, et al. Movement patterns of a U-20 national women’s soccer team during competitive matches: influence of playing position and performance in the first half. Int J Sports Med. 2017; 38(10), 747–754. pmid:28783847
  79. 79. Ramos GP, Nakamura FY, Penna EM, Wilke CF, Pereira LA, Loturco I, et al. Activity profiles in u17, u20, and senior women’s Brazilian national soccer teams during international competitions: are there meaningful differences?. J Strength Cond Res. 2019; 33(12), 3414–3422. pmid:28767483
  80. 80. Romero-Moraleda B, Nedergaard NJ, Morencos E, Casamichana D, Ramirez-Campillo R, Vanrenterghem J. External and internal loads during the competitive season in professional female soccer players according to their playing position: differences between training and competition. Res Sports Med. 2021. (epub ahead of print) pmid:33663313
  81. 81. Sausaman RW, Sams ML, Mizuguchi S, DeWeese BH, Stone MH. The physical demands of NCAA division I women’s college soccer. J Funct Morphol Kinesiol. 2019; 4(4), 73. pmid:33467387
  82. 82. Scott D, Norris D, Lovell R. Dose–response relationship between external load and wellness in elite women’s soccer matches: do customized velocity thresholds add value?. Int J Sports Physiol Perform. 2020; 15(9), 1245–1251. pmid:32887847
  83. 83. Soroka A, Bergier J. Actions with the ball that determine the effectiveness of play in women’s football. J Hum Kin. 2010; 26, 97–104.
  84. 84. Trewin J, Meylan C, Varley MC, Cronin J, Ling D. Effect of match factors on the running performance of elite female soccer players. J Strength Cond Res. 2018; 32(7), 2002–2009. pmid:29570576
  85. 85. Tscholl P, O’Riordan D, Fuller CW, Dvorak J, Junge A. Tackle mechanisms and match characteristics in women’s elite football tournaments. Br J Sports Med, 2007; 41(1 Suppl), i15–i19. pmid:17646245
  86. 86. Vescovi JD. Sprint profile of professional female soccer players during competitive matches: Female Athletes in Motion (FAiM) study. J Sports Sci. 2012; 30(12), 1259–1265. pmid:22897497
  87. 87. Wang SH, Qin Y. Analysis of shooting and goal scoring patterns in the 2019 France Women’s World Cup. J Phys Ed Sport. 2020; 20(6), 3080–3089.
  88. 88. Wang SH, Qin Y. Differences in the match performance of Asian women’s football teams. J Phys Ed Sport. 2020; 20(3 Suppl), 2230–2238.
  89. 89. Wells AJ, Hoffman JR, Beyer KS, Hoffman MW, Jajtner AR, Fukuda DH, et al. Regular-and postseason comparisons of playing time and measures of running performance in NCAA Division I women soccer players. App Physiol, Nutr Metab. 2015; 40(9), 907–917. pmid:26288394
  90. 90. Zubillaga A, Gabbett TJ, Fradua L, Ruiz-Ruiz C, Caro Ó, Ervilla R. Influence of ball position on playing space in Spanish elite women’s football match-play. Int J Sports Sci Coach. 2013; 8(4), 713–722.
  91. 91. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci. 2014; 32(20), 1844–1857. pmid:25093242
  92. 92. Buchheit M, Simpson BM. Player-tracking technology: half-full or half-empty glass?. Int J Sports Physiol Perform. 2017; 12(2 Suppl), S2–35. pmid:27967285
  93. 93. Harley JA, Lovell RJ, Barnes CA, Portas MD, Weston M. The interchangeability of global positioning system and semiautomated video-based performance data during elite soccer match play. J Strength Cond Res. 2011; 25(8), 2334–2336. pmid:21747299
  94. 94. Scott MT, Scott TJ, Kelly VG. The validity and reliability of global positioning systems in team sport: a brief review. J Strength Cond Res. 2016; 30(5), 1470–1490. pmid:26439776
  95. 95. Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017; 12(suppl 2), 18–26. pmid:27736244
  96. 96. Whitehead S, Till K, Weaving D, Jones B. The use of microtechnology to quantify the peak match demands of the football codes: a systematic review. Sports Med. 2018; 48(11), 2549–2575. pmid:30088218
  97. 97. Harkness-Armstrong A, Till K, Datson N, Emmonds S. Determining age-specific velocity thresholds for elite youth female soccer players. Sci Med Football. 2021. (epub ahead of print)
  98. 98. Rein R, Memmert D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Springerplus. 2016; 5(1), 1410–1423. pmid:27610328
  99. 99. Goes FR, Meerhoff LA, Bueno MJO, Rodrigues DM, Moura FA, Brink MS, et al. Unlocking the potential of big data to support tactical performance analysis in professional soccer: a systematic review. Eur J Sport Sci. 2021; 21(4): 481–496. pmid:32297547
  100. 100. White A, Hills SP, Cooke CB, Batten T, Kilduff LP, Cook CJ, et al. Match-play and performance test responses of soccer goalkeepers: A review of current literature. Sports Med. 2018; 48(11), 2497–2516. pmid:30144021
  101. 101. Mernagh D, Weldon A, Wass J, Phillips J, Parmar N, Waldron M, et al. A comparison of match demands using ball-in-play versus whole match data in professional soccer players of the English Championship. Sports. 2021; 9(6), 76. pmid:34073473
  102. 102. Wass J, Mernagh D, Pollard B, Stewart P, Fox W, Parmar N, et al. A comparison of match demands using ball-in-play vs. whole match data in elite male youth soccer players. Sci Med Football. 2020; 4(2), 142–147.
  103. 103. Dalton-Barron N, Whitehead S, Roe G, Cummins C, Beggs C, Jones B. Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league. J Sports Sci. 2020; 38(10), 1161–1180. pmid:32295471
  104. 104. Elliott-Sale KJ, Minahan CL, de Jonge XAJ, Ackerman KE, Sipilä S, Constantini NW, et al. Methodological considerations for studies in sport and exerccise science with women as participants: a working guide for standards of practice for research on women. Sports Med. 2021; 51(5), 843–861. pmid:33725341
  105. 105. Moalla W, Fessi MS, Makni E, Dellal A, Filetti C, Di Salvo V, et al. Association of physical and technical activities with partial match status in a soccer professional team. J Strength Cond Res. 2018; 32(6), 1708–1714. pmid:29786627
  106. 106. Coelho DB, Coelho LGM, Morandi RF, Ferreira JB Junior, Marins JCB, Prado LS, et al. Effect of player substitutions on the intensity of second-half soccer match play. Rev Bras Cineantropometria Desempenho Hum. 2012; 14(2), 183–191.
  107. 107. Hills SP, Barwood MJ, Radcliffe JN, Cooke CB, Kilduff LP, Cook CJ, et al. Profiling the responses of soccer substitutes: a review of current literature. Sports Med. 2018; 48(10), 2255–2269. pmid:30051171
  108. 108. Lago-Peñas C, Rey E, Lago-Ballesteros J. The influence of effective playing time on physical demands of elite soccer players. Open Sports Sci J. 2012; 5(1), 188–192.
  109. 109. Cunningham DJ, Shearer DA, Carter N, Drawer S, Pollard B, Bennett M, et al. Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: rolling averages versus fixed length epochs. PloS One. 2018; 13(4), e0195197. pmid:29621279
  110. 110. Doncaster G, Page R, White P, Svenson R, Twist C. Analysis of physical demands during youth soccer match-play: Considerations of sampling method and epoch length. Res Q Exerc Sport. 2020; 91(2), 326–334. pmid:31774386
  111. 111. Fereday K, Hills SP, Russell M, Smith J, Cunningham DJ, Shearer D, et al. A comparison of rolling averages versus discrete time epochs for assessing the worst-case scenario locomotor demands of professional soccer match-play. J Sci Med Sport. 2020; 23(8), 764–769. pmid:31937507
  112. 112. Varley MC, Elias GP, Aughey RJ. Current match-analysis techniques’ underestimation of intense periods of high-velocity running. Int J Sports Physiol Perform. 2012; 7(2), 183–185. pmid:22634968
  113. 113. Novak AR, Impellizzeri FM, Trivedi A, Coutts AJ, McCall A. Analysis of the worst-case scenarios in an elite football team: Towards a better understanding and application. J Sports Sci. 2021. (epub ahead of print) pmid:33840362
  114. 114. Riboli A, Semeria M, Coratella G, Esposito F. Effect of formation, ball in play and ball possession on peak demands in elite soccer. Biol Sport. 2021; 38(2), 195–205. pmid:34079164