Match demands of female team sports: a scoping review

María L. Pérez Armendáriz; Konstaninos Spyrou; Pedro E. Alcaraz

doi:10.5114/biolsport.2024.129476

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

Current Issue Manuscripts accepted About the journal Editorial board Abstracting and indexing Archive Ethical standards and procedures Contact Instructions for authors Journal's Reviewers Special Information

Editorial System

Submit your Manuscript

Editorial Policies

Sarajevo Declaration on Integrity and Visibility of Scholarly Publications

Open access

1/2024
vol. 41

Send email

Copy url:

Review paper

Match demands of female team sports: a scoping review

María L. Pérez Armendáriz

¹

,

Konstaninos Spyrou

^{1, 2, 3}

,

Pedro E. Alcaraz

^{1, 3}

UCAM Research Center for High Performance Sport, UCAM Universidad Católica de Murcia, Murcia, Spain
Facultad de Deporte, UCAM Universidad Católica de Murcia, Murcia, Spain
Strength and Conditioning Society, Murcia, Spain

Biol Sport. 2024;41(1):175–199

DOI: https://doi.org/10.5114/biolsport.2024.129476

Online publish date: 2023/07/24

Article file

- 17_03308_Article.pdf [1.01 MB]

Get citation

PlumX metrics:

INTRODUCTION

Female team sports’ participation and popularity have increased considerably in the last decade [1]. This increase has attracted more sports scientists, strength and conditioning coaches, and medical staff into the field [1–4]. However, a recent scoping review [5] about external load monitoring with wearable technology from 2015 to 2020 reported that only 16.2% of the investigations were carried out with female athletes, compared to 80.6% with male counterparts. Moreover, current sports performance methods and strategies in female team sports are often supported by evidence derived from male athletes [3, 4]. Consequently, sport practitioners should understand better the physiological and mechanical demands during match play in female team sports [6].

The external load represents the basic measurement of a monitoring system [7] and expresses the activities performed by an athlete [8] independently of its internal characteristics (i.e., internal load) [9]. The consensus statement of the International Olympic Committee on load in sports and risk of injury states that a successful training load monitoring system is fundamental to ensure the adaptation to stress, maximize physical performance, and possibly minimize the risk of injury [10]. In team sports, physical activity can be registered by different tracking systems, such as global positioning systems (GPS), local positioning systems (LPS), inertial measurement units (IMU), and time-motion analysis (TMA) [11–16]. Each system has its limitations; therefore a pragmatic and systematic approach to data collection, analysis, and interpretation is necessary [11]. Total distance (TD) is generally used as an indicator of overall training volume [11, 17], while high-speed running (HSR), acceleration (ACC) and deceleration (DEC) actions refer to a neuromuscular type of loading, which is likely more related to injury risk [18–20], and lastly Player Load (Pload) provides an estimate of the total cost of movement actions [17, 21].

The analysis of the physical demands during matches is an essential element for broadening knowledge of the stress that players experience at this level [22]. This information may help professionals to design appropriate training and return to play programme sessions regarding the match [16, 22]. For example, Taylor et al. [16] analysed the demands of athletes in both men and women in different team sports (soccer, basketball, handball, futsal, and field hockey) and categories (elite, sub-elite and junior), where only 10 studies were found in elite female players (soccer = 5, basketball = 2, handball = 2, field hockey = 1). Therefore, more research, characterizing the match demands in female team sports, to implement further evidence-based practices, is warranted.

To the authors’ knowledge, this is the first study to review the professional female athletes’ match demands, collected by external load from six different team sports (soccer, rugby, field hockey, basketball, handball, and futsal). The aim of this scoping review was to characterize and quantify the demands of external load (i.e., TD, moderatespeed running [MSR], HSR, sprint, ACC, DEC, and Pload) in professional female multi-directional team sports and highlight the importance of research on female sport [4, 23].

MATERIALS AND METHODS

Protocol and registration

The scoping review protocol was preliminarily submitted and published on the Open Science Framework, with the registration number 10.17605/OSF.IO/E4H9M on 29^th April 2023.

Study design

The present study is a scoping review focused on the match demands of professional women’s team sports (i.e., soccer, rugby, field hockey, basketball, handball, and futsal) measured with a tracking system. The review was carried out in accordance with the recommendations for Systematic Reviews and Meta-Analyses (PRISMA) [24] and did not require institutional review board approval.

Data sources and searches

A scoping review of the literature was performed using three different online databases – PubMed, Scopus, and Web of Science – until April 15^th, 2023. In order to ensure that all research related to this topic was identified, a broad and general search was carried out, searching for the following terms: [(“match analysis” OR “GPS” OR “demands” OR “external load”) AND (“basketball”/ “field hockey”/ “football OR soccer”/ “handball”/ “rugby”/ “futsal”) AND (“female” OR “women”) NOT “male”], to ensure that all studies related to this topic were identified, and the search was repeated for each sport individually. This search was performed by two authors (MLPA and KS), and search results were uploaded to reference management software (Zotero) where duplicates were automatically removed. All titles and abstracts of all remaining studies were screened by two authors (MLPA and KS) using the eligibility criteria below. Any disagreements about study inclusion/exclusion that could not be resolved between the two authors were decided by a third party (PEA).

Eligibility criteria

Studies were eligible for inclusion if they met the following criteria: 1) a sample of highly trained and competitive/professional female athletes according to classification of levels of competition adapted from Russell et al. [25], aged > 18 years; 2) competing in soccer, rugby, field hockey, basketball, handball, and futsal; and lastly 3) incorporating tracking systems (i.e., GPS, LPS, TMA or IMU) and analysing some external load variables (i.e., TD, distance per zone, ACC, DEC, Pload).

Studies were excluded if they: 1) did not include original data; 2) were not available in English and full text; 3) reported simulated games and/or drills; and 4) scored < 8 in methodological quality assessment.

Study selection

The initial search was carried out by two researchers (MLPA and KS). After the elimination of duplicates, an intensive review of all titles and abstracts obtained was completed and those not related to the review’s topic were discarded. The full version of the remaining articles was read. All studies not meeting the inclusion criteria were excluded.

Data extraction

Data were extracted into a custom-made Microsoft Excel sheet (2007) by one author (MLPA), with two other authors (KS and PEA) checking for the accuracy. The results were selected with the following order: participant’s information (i.e., sample size, age, height, weight), number of matches, country, equipment used (i.e., device brand, model details, sampling frequency (Hz), according to recommendations for the collecting, processing and reporting of data from GPS devices [26] external load metrics (i.e., TD, distance at MSR [12.6–19.8 km · h⁻¹], HSR [19.8–25.2 km · h⁻¹], and sprinting [≥ 25.2 km · h⁻¹], ACC, DEC, Pload). The mean and standard deviation (SD) were extracted for all the variables, and presented as full match-play. Intensity thresholds for ACC and DEC were presented. A meta-analysis was not performed due to the heterogeneous nature of sport specific study designs and inability to pool data.

Risk of bias

The risk of bias was evaluated independently by two authors (MLPA and KS), who reanalysed the process in cases of disagreement. If a consensus was not reached, a final decision was made by a third author (PEA). The Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS) was utilized to evaluate the included studies’ risk of bias, as it has demonstrated moderate reliability and good feasibility and validity [27]. The tool comprises six domains, which are the selection of participants, confounding variables, measurement of exposure, blinding of outcome assessments, incomplete outcome data, and selective outcome reporting, and these domains are classified as ‘low’, ‘high’, or ‘unclear’ risk of bias [27].

Methodological quality assessment

The methodological quality of the included studies was assessed by two researchers (MLPA and KS) using the modified Downs & Black [28] evaluation scale. Of the total 27 criteria, 12 were used according to the study’s design (i.e., descriptive), as observed in similar systematic reviews [13, 14, 29].

RESULTS

Search results

Figure 1 depicts the PRISMA flow diagram of the search and selection process. The initial databases yielded 1175 studies, and 24 additional records were added through other sources. After duplicate removal, 703 articles remained. Upon title and abstract screening, 150 were left for full-text review. Of the 150 articles reviewed, 86 met the inclusion criteria in this systematic review: 40 on soccer [30–70], 23 on rugby [71–93], 8 on field hockey [94–101], 8 on basketball [102–109], 6 on handball [110–115], and 1 on futsal [116].

FIG. 1

Flow diagram.

/f/fulltexts/BS/51090/JBS-41-51090-g001_min.jpg

Risk of bias

The results of the risk of bias assessment can be seen in Table 1. Overall the confounding variables were unclear in the majority (64%) of the articles. This is because contextual factors (e.g., sleep, nutrition, training, climate) were not reported or not controlled for. The risk of bias in the measurement of exposure was unclear in 16% of the articles and high in 13%, as assessments of demands were not conducted over a considerable period of time (> 4 matches) or in relation to the reliability of the measurement instrument. All included studies had a low risk of bias in the selection of participants.

TABLE 1

Risk of bias assessment of non-randomized studies.

Author (year)	Selection of participants	Confounding variables	Measurement of exposure	Blinding of outcome assessments	Incomplete outcome data	Selective outcome reporting
Andersson et al. (2010) [51]	Low	Unclear	Unclear	Low	Low	Low

Bradley et al. (2014) [60]	Low	Unclear	Low	Low	Low	Low

Busbridge et al. (2020) [92]	Low	Unclear	Low	Low	Low	Low

Callanan et al. (2021) [73]	Low	Low	Low	Low	Low	Low

Choi et al. (2020) [96]	Low	Unclear	Low	Low	Low	Low

Choi et al. (2022) [67]	Low	Unclear	Low	Low	Low	Low

Clarke et al. (2014a) [88]	Low	Low	High	Low	Low	Low

Clarke et al. (2014b) [86]	Low	Unclear	Low	Low	Low	Low

Clarke et al. (2015) [85]	Low	Unclear	Low	Low	Unclear	Low

Clarke et al. (2017) [90]	Low	Unclear	Low	Low	Low	Low

Conte et al. (2015) [105]	Low	Unclear	High	Low	Low	Low

Conte et al. (2022) [77]	Low	Unclear	Low	Low	Low	Low

Datson et al. (2017) [47]	Low	Unclear	Unclear	Low	Low	Low

Datson et al. (2019) [61]	Low	Unclear	High	Low	Low	Low

Del coso et al. (2013) [89]	Low	Low	Unclear	Low	Low	Low

Delextrat et al. (2017) [104]	Low	Unclear	Unclear	Low	Low	Low

Delextrat et al. (2012) [108]	Low	Low	High	Low	Low	Low

Delves et al. (2021) [101]	Low	Unclear	Low	Low	Low	Low

DeWitt et al. (2018) [45]	Low	Low	Low	Low	Low	Low

Diaz-Seradilla et al. (2022) [58]	Low	Unclear	Low	Low	Low	Low

Doeven et al. (2019) [91]	Low	Low	Low	Low	Low	Low

Emmonds et al. (2020) [75]	Low	Unclear	Unclear	Low	Low	Low

Fernandes et al. (2022) [56]	Low	Low	Low	Low	Low	Low

Gabbett et al. (2008) [62]	Low	Unclear	Unclear	Low	Low	Low

García-Ceberino et al. (2022) [57]	Low	Low	Unclear	Low	Low	Low

Gonçalves et al. (2021) [54]	Low	Unclear	Low	Unclear	Low	Low

Goodale et al. (2006) [84]	Low	Unclear	Unclear	Low	Low	Low

Griffin et al. (2021) [38]	Low	Unclear	Low	Low	Low	Low

Hewitt et al. (2014) [50]	Low	Unclear	Low	Low	Low	Low

Julian et al. (2021) [37]	Low	Low	Unclear	Low	Low	Low

Kapteijns et al. (2021) [94]	Low	Unclear	Low	Low	Low	Low

Kim et al. (2016) [99]	Low	Unclear	High	Low	Low	Low

Kniubaite et al. (2019) [110]	Low	Low	Low	Low	Low	Low

Kobal et al. (2022a) [69]	Low	Unclear	Low	Low	Low	Low

Kobal et al. (2022b) [68]	Low	Unclear	Low	Low	Low	Low

Krustrup et al. (2005) [53]	Low	Unclear	Low	Low	Low	Low

Krustrup et al. (2021) [36]	Low	Low	Low	Low	Low	Low

Luteberget et al. (2016) [111]	Low	Unclear	Low	Low	Low	Low

Luteberget et al. (2017) [112]	Low	Low	Low	Low	Low	Low

Malone et al. (2020) [78]	Low	Unclear	Low	Low	Low	Low

Manchado et al. (2013) [115]	Low	Unclear	High	Low	Low	Low

Mara et al. (2016) [63]	Low	Unclear	Low	Low	Low	Low

Mara et al. (2017) [46]	Low	Unclear	Low	Low	Low	Low

McGuinness et al. (2018) [100]	Low	Low	Low	Low	Low	Low

McMahon et al. (2019) [98]	Low	Unclear	Low	Low	Low	Low

Meylan et al. (2016) [49]	Low	Unclear	Low	Low	Low	Low

Michalsik et al. (2014) [114]	Low	Low	Unclear	Low	Low	Low

Misseldine et al. (2018) [79]	Low	Low	Low	Low	Low	Low

Mohr et al. (2008) [52]	Low	Unclear	High	Low	Low	Low

Morencos et al. (2019) [97]	Low	Unclear	Low	Low	Low	Low

Nakamura et al. (2017) [64]	Low	Unclear	Unclear	Low	Low	Low

Newans et al. (2021) [72]	Low	Unclear	Low	Low	Low	Low

Nolan et al. (2023) [93]	Low	Unclear	Low	Low	Low	Low

Oliva Lozano et al. (2021) [116]	Low	Unclear	Low	Low	Low	Low

Palmer et al. (2021) [102]	Low	Low	Low	Low	Low	Low

Palmer et al. (2022) [107]	Low	Low	Low	Low	Low	Low

Panduro et al. (2021) [34]	Low	Unclear	Unclear	Low	Low	Low

Park et al. (2018) [42]	Low	Low	Low	Low	Low	Unclear

Portillo et al. (2014) [82]	Low	Low	Low	Low	Low	Low

Principe et al. (2021) [35]	Low	Unclear	Low	Unclear	Low	Low

Quinn et al. (2019) [87]	Low	Unclear	Low	Low	Low	Low

Ramos et al. (2017) [65]	Low	Unclear	Low	Low	Low	Low

Ramos et al. (2019a) [41]	Low	Unclear	Low	Low	Low	Low

Ramos et al. (2019b) [66]	Low	Low	Low	Low	Low	Low

Reina et al. (2022) [109]	Low	Low	Low	High	Low	Low

Reyneke et al. (2018) [80]	Low	Unclear	Low	Low	Low	Low

Romero-Moraleda et al. (2021) [33]	Low	Low	Low	Low	Low	Low

Sánchez-Migallón et al. (2020) [95]	Low	Low	High	Low	Low	Low

Scanlan et al. (2012) [106]	Low	Low	High	Low	Low	Low

Scott et al. (2020a) [39]	Low	Unclear	Low	Low	Low	Low

Scott et al. (2020b) [40]	Low	Unclear	Unclear	Low	Low	Low

Sheppy et al. (2020) [74]	Low	Unclear	Low	Low	Low	Low

Stauton et al. (2018) [103]	Low	Unclear	Low	Low	Low	Low

Suarez-Arrones et al. (2014) [76]	Low	Low	High	Low	Low	Low

Suarez-Arrones et al. (2012) [83]	Low	Low	High	Low	Low	Low

Trewin et al. (2017) [43]	Low	Low	Unclear	Low	Low	Low

Trewin et al. (2018) [48]	Low	Low	Low	Low	Low	Low

Vescovi et al. (2012) [44]	Low	Low	Low	Low	Low	Low

Vescovi et al. (2015) [81]	Low	Unclear	Low	Low	Low	Low

Vescovi et al. (2019) [55]	Low	Unclear	Low	Low	Low	Low

Villaseca-Vicuña et al. (2021) [32]	Low	Low	Low	Low	Low	Low

Villaseca-Vicuña et al. (2023) [70]	Low	Low	Low	Low	Low	Low

Wik et al. (2016) [113]	Low	Unclear	Low	Low	Low	Low

Winther et al. (2021) [31]	Low	Unclear	Low	Low	Low	Low

Woodhouse et al. (2021) [71]	Low	Unclear	Low	Low	Low	Low

Yousefian et al. (2021) [30]	Low	Unclear	Low	Low	Low	Low

Soccer

Table 2 presents the match demands, anthropometric data and origin of players in soccer. The match demands were collected by TMA (n = 9) and GPS devices (n = 31). Female players covered a total distance of 9556 ± 795 m and 103 ± 6 m × min⁻¹ during matches [30–41, 43, 45, 48–63, 65–70]. Considering zones of intensity, women soccer players performed 1429 ± 702 m in MSR, 830 ± 1414 m in HSR and 267 ± 275 m in sprinting; other studies presented these variables relative to time (MSR = 15 ± 7 m × min⁻¹; HSR = 4 ± 1 m × min⁻¹; sprinting = 3 ± 2 m × min⁻¹) [30, 33, 37, 47, 49, 55, 58, 66, 68–70]. Regarding the number of sprints, the players did a mean of 30 ± 19 sprint actions per match [34, 45, 53, 54, 58, 61, 67]. Moreover, studies [32–35, 41, 43, 46, 48, 54, 56, 63, 65, 67, 69, 70] reported that female players completed a total of 165 ± 129 ACC and 146 ± 141 DEC actions during the match. These actions were also presented in frequency per minute [30, 43, 48, 49, 54, 57, 66, 68], distance travelled [31, 36] and duration [38] (Table 2).

TABLE 2

Summary of the match demands in soccer.

Study (year)	Sport	Country	Players (n)	Age (years) Height (cm) Mass (kg)	Match (n)	Device	TD (m)	TD (m · min⁻¹)	MSR (m) 12.6–19.8 km · h⁻¹	HSR (m) 19.8–25.2 km · h⁻¹	Sprint (m) ≥ 25.2 km · h⁻¹	ACC (n)	DEC (n)
Andersson et al. (2010) [51]	Soccer	Sweden-Denmark	17	27 ± 1 168 ± 2 61 ± 1	6	TMA (Canon DM-MV 600, Canon Inc.)	9800 ± 141	N-R	N-R	1430 ± 141 (≥ 18 km · h⁻¹)	239 ± 25 (≥ 25 km · h⁻¹)	N-R	N-R

Bradley et al. (2014) [60]	Soccer	N-R	59	N-R	N-R	TMA (Multiple-camera system, Amisco Pro) 25 Hz	10754 ± 150	N-R	2374 ± 70 (12–18 km · h⁻¹)	718 ± 34 (18–25 km · h⁻¹)	N° 59 ± 9 (≥ 25 km · h⁻¹)	N-R	N-R

Choi et al. (2022) [67]	Soccer	South Korea	24	29 ± 4 166 ± 5 59 ± 6	21	GPS (APEX STATSports) 10 Hz	9520 ± 676		1685 ± 395 (13–19 km · h⁻¹)	371 ± 82 (19–23 km · h⁻¹)	129 ± 81 Nº 16 ± 5 (≥ 23 km · h⁻¹)	75 ± 13 (N-R)	85 ± 15 (N-R)

Datson et al. (2017) [47]	Soccer	N-R	107	N-R	1–4	TMA (Prozone Sports Ltd., Leeds)	10321 ± 859	N-R	2520 ± 580 (14–19.8 km · h⁻¹)	776 ± 247 (≥ 19.8 km · h⁻¹)	168 ± 82 (≥ 25 km · h⁻¹)	N-R	N-R

Datson et al. (2019) [61]	Soccer	N-R	107	N-R	2–4	TMA (Semi-automated multi-camera image recognition system, STATS)	N-R	N-R	N-R	N° 169 ± 50 (≥ 19.8 km · h⁻¹)	N° 33 ± 13 (≥ 25 km · h⁻¹)	N-R	N-R

DeWitt et al. (2018) [45]	Soccer	USA	18	25 ± 3 168 ± 5 61 ± 5	20	GPS (Optimeye S5, Catapult Innovations) 10 Hz	8883 ± 877	99 ± 22	570 ± 407 (≥ 17.8 km · h⁻¹)	N-R	N° 9 ± 11 (≥ 22.7 km · h⁻¹)	N-R	N-R

Diaz-Seradilla et al. (2022) [58]	Soccer	Spain	17	23 ± 5 166 ± 6 60 ± 7	1	GPS (WIMU PRO, Real rack Systems) 10 Hz	9347 ± 1013	96 ± 9	1110 ± 332 12 ± 4 m · min⁻¹ (≥ 16 km · h⁻¹)	N-R	235 ± 21 N° 1 3 ± 4 3 ± 2 m · min⁻¹ (≥ 21 km · h⁻¹)	32 ± 2 m · min⁻¹ (N-R)	32 ± 1 m · min⁻¹ (N-R)

Fernandes et al. (2022) [56]	Soccer	Portugal	10	24 ± 2 165 ± 6 58 ± 9	15	GPS (PlayerTeck, Catapult) 10 Hz	7616 ± 395	90 ± 5	880 ± 102 (≥ 15 km · h⁻¹)	N-R	N-R	177 ± 8 (2–3 m · s⁻²)	169 ± 5 (-2–3 m · s⁻²)

Gabbett et al. (2008) [62]	Soccer	Australia	30	21 ± 2 N-R N-R	12	TMA (37-mm digital video cameras, Sony, DCR-TRV 950E)	9967 ± 610 5618 ± 67 s	N-R	1484 ± 402 266 ± 71 s 15 ± 3% ^B (N-R)	N-R	995 ± 182 159 ± 35 s 10 ± 2% ^B (N-R)	N-R	N-R

García-Ceberino et al. (2022) [57]	Soccer	Spain	10	26 ± 4 166 ± 1 61 ± 7	3	GPS (SPRO, RealTrack Systems) 18 Hz	N-R	91 ± 12	2 ± 2 N° · min⁻¹ (N-R)	N-R	7 ± 15 n · min⁻¹ (N-R)	31 ± 3 n · min⁻¹ (N-R)	32 ± 3 n · min⁻¹ (N-R)

Gonçalves et al. (2021) [54]	Soccer	Portugal	22	25 ± 6 162 ± 7 59 ± 9	10	GPS (SPI HPU, GPSports) 15 Hz	8237 ± 206	100 ± 1	758 ± 50 9 ± 0.2 m · min⁻¹ (14–18 km · h⁻¹)	306 ± 46 4 ± 0.4 m · min⁻¹ (18–24 km · h⁻¹)	N° 15 ± 0.1 22 ± 3 (≥ 24 km · h⁻¹)	41 ± 0.6 0.5 ± 0.02 n · min⁻¹ (2–3 m · s⁻²)	44 ± 0.2 0.5 ± 0.03 n · min⁻¹ (-2–3 m · s⁻²)

Griffin et al. (2021) [38]	Soccer	Australia	33	NAT = 15 26 ± 3 167 ± 8 61 ± 6 INT = 18 26 ± 4 167 ± 8 60 ± 7	36	GPS (SPI HPU, GPSports) 10 Hz	9080 ± 499	N-R	687 ± 112 (16–20 km · h⁻¹)	335 ± 40 (≥ 20 km · h⁻¹)	N-R	176 ± 17 s (2–3 m · s⁻²)	172 ± 13 s (2–3 m · s⁻²)

Hewitt et al. (2014) [50]	Soccer	Austalia	15	23 ± 1 170 ± 1 65 ± 1	13	GPS (MinimaxX v2.5, Catapult Innovations) 5 Hz	9631 ± 175	N-R	2407 ± 125 (12–19 km · h⁻¹)	338 ± 30 (≥ 19 km · h⁻¹)	N-R	N-R	N-R

Julian et al. (2021) [37]	Soccer	Germany	15	23 ± 4 169 ± 1 64 ± 8	4–7	GPS (Tracktics TT01) 5 Hz	N-R	103 ± 1	18 ± 2 m · min⁻¹ (13–20 km · h⁻¹)	4 ± 0.2 m · min⁻¹ N° 24 ± 13 (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Kobal et al. (2022a) [69]	Soccer	Brazil	24	28 ± 5 164 ± 5 59 ± 8	38	GPS (Catapult Innovations) 10 Hz	9830 ± 42	104 ± 3	N-R	7234 ± 327 8 ± 0.4 m · min⁻¹ (≥ 18 km · h⁻¹)	N-R	726 ± 15 (≥ 3 m · s⁻²)	912 ± 2 (≥ 3 m · s⁻²)

Kobal et al. (2022b) [68]	Soccer	Brazil	23	28 ± 5 165 ± 5 59 ± 5	14	GPS (Catapult Innovations) 10 Hz	N-R	96 ± 14	N-R	8 ± 3 m · min⁻¹ (≥ 18 km · h⁻¹)	N-R	1 ± 0.2 n · min⁻¹ (≥ 3 m · s⁻²)	1 ± 0.2 n · min⁻¹ (≤ -3 m · s⁻²)

Krustrup et al. (2005) [53]	Soccer	Denmark	14	24 ± 8 167 ± 17 58 ± 22	4	TMA (VHS movie camera NV-M50, Panasonic)	10300 (9700–1300)	N-R	N-R	1310 (700–1700) 4.8% * (2.8–6.1) (≥ 18 km · h⁻¹)	160 (50–280) N° 26 (9–43) (≥ 25 km · h⁻¹)	N-R	N-R

Krustrup et al. (2021) [36]	Soccer	N-R	17	23 ± 4 166 ± 5 60 ± 7	1	GPS (S5, Catapult Innovations) 10 Hz	8500 ± 1200	N-R	903 ± 275 (16–20 km · h⁻¹)	N-R	N-R	233 ± 52 m (≥ 2 m · s⁻²)	172 ± 40 m (≤ -2 m · s⁻²)

Mara et al. (2016) [63]	Soccer	Australia	12	24 ± 4 172 ± 5 65 ± 5	7	TMA (High-definition video cameras, Legria HF R38, Canon) 25 Hz	N-R	N-R	N-R	N-R	N-R	423 ± 126 (≥ 2 m · s⁻²)	430 ± 125 (≤ -2 m · s⁻²)

Mara et al. (2017) [46]	Soccer	Australia	12	24 ± 4 172 ± 5 65 ± 5	7	TMA (8 stationary high-definition video cameras Legria HF R38; Canon)	10025 ± 775	N-R	2452 ± 36 (12–19 km · h⁻¹)	615 ± 258 N° 70 ± 29 (≥ 19 km · h⁻¹)	N-R	N-R	N-R

Meylan et al. (2016) [49]	Soccer	N-R	13	27 ± 5 170 ± 6 66 ± 5	34	GPS (MinimaX S4, Catapult Innovations) 10 Hz	N-R	107 ± 16	6 ± 2 m · min⁻¹ (16–20 km · h⁻¹)	3 ± 1 m · min⁻¹ (≥ 20 km · h⁻¹)	N-R	2 ± 1 n · min⁻¹ (≥ 2.26 m · s⁻²)	N-R

Mohr et al. (2008) [52]	Soccer	Sweden-Denmark	34	N-R	1–2	TMA (VHS movie cameras NV-M50)	10385 ± 150	N-R	N-R	1490 ± 95 (≥ 18 km · h⁻¹)	420 ± 35 (≥ 25 km · h⁻¹)	N-R	N-R

Nakamura et al. (2017) [64]	Soccer	Brazil	11	21 ± 3 164 ± 4 60 ± 8	10	GPS (SPI 119 Elite, GPSports Systems). 5 Hz	N-R	N-R	N-R	285 ± 164 N° 18 ± 9 3 ± 0.5 s (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Panduro et al. (2021) [34]	Soccer	Denmark	94	23 ± 4 170 ± 6 64 ± 6	2–4	GPS (Polar Team Pro Electro Oy) 10 Hz	10033 ± 454	N-R	1496 ± 256 (≥ 15 km · h⁻¹)	676 ± 156 (≥ 18 km · h⁻¹)	N° 49 ± 27 (≥ 25 km · h⁻¹)	8 ± 5 (3–5 m · s⁻²)	15 ± 4 (-3–5 m · s⁻²)

Park et al. (2018) [42]	Soccer	N-R	27	25 ± 4 169 ± 5 63 ± 4	52	GPS (MinimaX S4, Catapult Innovations) 10 Hz	N-R	N-R	843 (812–876) (12–20 km · h⁻¹)	101 (96–107) (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Principe et al. (2021) [35]	Soccer	Brazil	23	28 ± 5 165 ± 6 61 ± 5	22	GPS (Polar Team Pro Electro Oy) 10 Hz	8017 ± 360	N-R	2025 ± 224 (12–20 km · h⁻¹)	306 ± 35 (≥ 20 km · h⁻¹)	N-R	240 ± 18 (≥ 2 m · s⁻²)	242 ± 17 (≤ -2 m · s⁻²)

Ramos et al. (2017) [65]	Soccer	Brazil	12	18 ± 1 167 ± 6 62 ± 6	7	GPS (MinimaxX S5, Catapult Innovations) 10 Hz	8704 ± 432	N-R	688 ± 183 (16–20 km · h⁻¹)	223 ± 120 (≥ 20 km · h⁻¹)	N-R	15 ± 2 (≥ 2 m · s⁻²)	17 ± 6 (≤ -2 m · s⁻²)

Ramos et al. (2019a) [41]	Soccer	Brazil	17	27 ± 4 187 ± 5 61 ± 4	6	GPS (MinimaxX S5, Catapult Innovations) 10 Hz	10110 ± 245	N-R	736 ± 153 (16–20 km · h⁻¹)	307 ± 80 (≥ 20 km · h⁻¹)	N-R	214 ± 3 (≥ 1 m · s⁻²)	174 ± 4 (≤ -1 m · s⁻²)

Ramos et al. (2019b) [66]	Soccer	Brazil	21	26 ± 4 167 ± 6 N-R	6	GPS (MinimaxX S5, Catapult Innovations) 10 Hz	N-R	109 ± 4	22 ± 2 m · min⁻¹ (12–20 km · h⁻¹)	3 ± 1 m · min⁻¹ (≥ 20 km · h⁻¹)	N-R	0.05 ± 0.01 n · min⁻¹ (≥ 2.5 m · s⁻²)	0.12 ± 0.03 n · min⁻¹ (≤ -2.5 m · s⁻²)

Romero-Moraleda et al. (2021) [33]	Soccer	Spain	18	26 ± 6 164 ± 5 59 ± 6	94	GPS (SPI Pro X, GPSports Systems) 5 Hz	9040 ± 938	95 ± 9	1108 ± 294 12 ± 2 m · min⁻¹ (≥ 15 km · h⁻¹)	N-R	N-R	255 ± 40 (≥ 1 m · s⁻²)	78 ± 16 (≤ -1 m · s⁻²)

Scott et al. (2020a) [39]	Soccer	USA	36	24 ± 4 168 ± 6 63 ± 5	220	GPS (Optimeye S5, Catapult Innovations) 10 Hz	10068 ± 615	N-R	2401 ± 454 (≥ 12.5 km · h⁻¹)	398 ± 153 (≥ 19 km · h⁻¹)	162 ± 69 (≥ 22.5 km · h⁻¹)	N-R	N-R

Scott et al. (2020b) [40]	Soccer	USA	220	25 ± 3 167 ± 6 64 ± 6	N-R	GPS (Optimeye S5, Catapult Innovations) 10 Hz	10073 ± 425	N-R	2409 ± 263 (≥ 12.5 km · h⁻¹)	479 ± 114 (≥ 19 km · h⁻¹)	139 ± 32 (≥ 22.5 km · h⁻¹)	N-R	N-R

Trewin et al. (2017) [43]	Soccer	N-R	45	N-R	7 ± 6	GPS (MinimaX S4, Catapult Innovations) 10 Hz	10368 ± 952	108 ± 10	930 ± 348 10 ± 4 m · min⁻¹ (≥ 16 km · h⁻¹)	0.2 ± 0.1 N° · min⁻¹ N° 20 ± 9 (≥ 20 km · h⁻¹)	N-R	174 ± 33 1.8 ± 0.3 n · min⁻¹ (≥ 2 m · s⁻²)	N-R

Trewin et al. (2018) [48]	Soccer	N-R	45	24 ± 13 N-R N-R	47	GPS (MinimaX S4, Catapult Innovations) 10 Hz	N-R	107 ± 10	10 ± 3 m · min⁻¹ (≥ 16 km · h⁻¹)	0.2 ± 0.1 N° · min⁻¹ N° 20 ± 9 (≥ 20 km · h⁻¹)	N-R	1.8 ± 0.3 n · min⁻¹ (≥ 2 m · s⁻²)	N-R

Vescovi et al. (2012) [44]	Soccer	USA	71	N-R	12	GPS (SPI Pro, GPSports) 5 Hz	N-R	N-R	N-R	550 ± 186 (18–21 km · h⁻¹)	N-R	N-R	N-R

Vescovi et al. (2019) [55]	Soccer	N-R	28	N-R	2	GPS (SPI Pro, GPSports) 5 Hz	N-R	111 ± 12	27 ± 1 m · min⁻¹ (12–20 km · h⁻¹)	4 ± 1 m · min⁻¹ (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Villaseca-Vicuña et al. (2021) [32]	Soccer	Chile	26	27 ± 3 158 ± 21 59 ± 5	26	GPS (Optimeye S5, Catapult Innovations) 10 Hz	9415 ± 766	108 ± 7	N-R	515 ± 162 N° 35 ± 11 (≥ 18 km · h⁻¹)	N-R	102 ± 28 (≥ 2 m · s⁻²)	N-R

Villaseca-Vicuña et al. (2023) [70]	Soccer	Chile	10	27 ± 3 163 ± 4 60 ± 5	6	GPS (Optimeye S5, Catapult Innovations) 10 Hz	9737 ± 448	108 ± 4	N-R	566 ± 49 6 ± 1 m · min⁻¹ Nº 42 ± 4 (≥ 18 km · h⁻¹)	N-R	N-R	N-R

Winther et al. (2021) [31]	Soccer	Norway	108	22 ± 4 N-R N-R	60	GPS (APEX STATSports) 10 Hz	9603 ± 480	N-R	1499 ± 300 (≥ 16 km · h⁻¹)	369 ± 116 (≥ 20 km · h⁻¹)	N-R	486 ± 62 m (≥ 2 m · s⁻²)	389 ± 69 m (≤ -2 m · s⁻²)

Yousefian et al. (2021) [30]	Soccer	Sweden	21	27 ± 4 172 ± 5 65 ± 4	7	GPS (S5, Catapult Innovation) 10 Hz	N-R	99 ± 4	22 ± 3 m · min⁻¹ (12–19 km · h⁻¹)	4 ± 0.5 m · min⁻¹ (≥ 19 km · h⁻¹)	N-R	0.2 ± 0.04 n · min⁻¹	0.2 ± 0.04 n · min⁻¹

* Percentage of total time.

B Percentage of total distance. ACC: accelerations; DEC: decelerations; GPS: global positions system; HSR: high-speed running; MSR: moderate-speed running; N-R: no reported; TD: total distance; TMA: time-motion analysis; USA: United States of America.

Rugby

Table 3 depicts the match demands, anthropometric data and origin of rugby, rugby sevens, and field hockey. Out of the 23 results found in rugby, 6 correspond to rugby union [71, 73, 74, 76, 92, 93], 3 to rugby league [72, 75, 87] and 14 to rugby sevens [76–82, 84–86, 88–91]. Regarding external match load, GPS devices were used. Players covered an average of 5351 ± 855 m, while only three studies reported the density of 70 ± 8 m × min⁻¹ [71–73]. Rugby female players performed 916 ± 386 m in MSR and a mean of 135 ± 63 m HSR per match. Few studies presented the locomotive zones of intensity in terms of proportion (18 ± 9%) [73, 76] and density (21 ± 4 m × min ⁻¹) [75]. The authors reported that players did a mean of 8 ± 8 sprints per game [75]. Regarding ACC, one study [76] reported number (Nº = 19 ± 8) and two [71, 72] the frequency per minute (0.7 ± 0.4 ACC × min⁻¹) (Table 3).

TABLE 3

Summary of the match demands of rugby union and sevens and field hockey.

Study (year)	Sport	Country	Players (n)	Age (years) Height (cm) Mass (kg)	Match (n)	Device	TD (m)	TD (m · min⁻¹)	MSR (m) 12.6–19.8 km · h⁻¹	HSR (m) 19.8–25.2 km · h⁻¹	Sprint (m) ≥ 25.2 km · h⁻¹	ACC (n)	DEC (n)
Busbridge et al. (2020) [92]	Rugby Union	New Zealand	20	24 ± 4 170 ± 6 79 ± 11	7	GPS (VX Log 340b, Firmware V1.62-03, VX Sport) 10 Hz	5812 ± 470	N-R	483 ± 276 7 ± 4 m · min⁻¹ (≥ 16 km · h⁻¹)	N-R	N-R	N-R	N-R

Callanan et al. (2021) [73]	Rugby Union	Ireland	128	Forwards 26 ± 4 172 ± 7 80 ± 8 Backs 25 ± 4 167 ± 5 70 ± 6	12	Triaxial magnetometer (PlayerTek, Catapult Innovations) 10 Hz	5696 ± 822	68 ± 7	1380 ± 383 24%* (10–18 km · h⁻¹)	220 ± 156 4%* (≥ 18 km · h⁻¹)	N-R	N-R	N-R

Nolan et al. (2023) [93]	Rugby Union	N-R	53	N-R	12	GPS (STATSports Apex; STATSports) 10 Hz	4177 ± 206	60 ± 9	1254 ± 637 18 ± 6 m · min⁻¹ (10–19.5 km · h⁻¹)	106 ± 126 1 ± 2 m · min⁻¹ (≥ 19.5 km · h⁻¹)	N-R	N-R	N-R

Sheppy et al. (2020) [74]	Rugby Union	Wales	29	24 ± 3 167 ± 1 75 ± 11	8	GPS (Optimeye S5, Catapult Innovations) 10 Hz	5784 ± 569	N-R	N-R	N-R	N-R	N-R	N-R

Suarez-Arrones et al. (2014) [76]	Rugby Union	Spain	8	Backs = 4 27 ± 3 170 ± 2 68 ± 4 Forwards = 4 27 ± 2 174 ± 6 77 ± 10	1	GPS (SPI Pro X; GPSports) 5 Hz	5820 ± 512	N-R	658 ± 264 11.3%^* (14–20 km · h⁻¹)	73 ± 107 N° 5 ± 5 1.2%^* (≥ 20 km · h⁻¹)	N-R	19 ± 8 (≥ 3 m · s⁻²)	N-R

Woodhouse et al. (2021) [71]	Rugby Union	England	78	25 ± 4 171 ± 6 77 ± 10	53	GPS (Viper, STATSports) 18 Hz	4271 ± 814	66 ± 4	1314 ± 367 21 ± 4 m · min⁻¹ (11–20 km · h⁻¹)	N° 8 ± 5 0.1 ± 0.07 N · min⁻¹ (≥ 20 km · h⁻¹)	N-R	1 ± 0.1 n · min⁻¹ (2–3 m · s⁻²)	1 ± 0.1 n · min⁻¹ (-2 to -3 m · s⁻²)

Emmonds et al. (2020) [75]	Rugby League	N-R	58	N-R	9	GPS (Optimeye S5, Catapult Innovations) 10 Hz	5383 ± 780	75 ± 2	N-R	140 ± 90 2 ± 1 m · min⁻¹ (≥ 18 km · h⁻¹)	N° 8 ± 8 0.1 ± 0.1 m · min⁻¹ (≥ 25 km · h⁻¹)	N-R	N-R

Quinn et al. (2019) [87]	Rugby League	Australia	18	26 ± 4 N-R N-R	7	GPS (SPI Pro X, GPSports) 10 Hz	6712 (6203–6951)	N-R	542 (368–644) (≥ 15 km · h⁻¹)	N-R	N-R	N-R	N-R

Newans et al. (2021) [72]	Rugby League	Australia	117	26 ± 5 170 ± 1 77 ± 12	4 ± 2	GPS (Optimeye S5, Catapult Innovations) 10 Hz	4504 ± 1029	79 ± 2	774 ± 210 (≥ 12 km · h⁻¹)	N-R	N-R	0.4 ± 0.02 n · min⁻¹ (N-R)	N-R

Clarke et al. (2014a) [88]	Rugby sevens	Australia	12	25 ± 5 168 ± 1 69 ± 7	N-R	GPS (SPI Pro X; GPSports) 5 Hz	N-R	86 ± 7	N-R	N-R	N-R	N-R	N-R

Clarke et al. (2014b) [86]	Rugby sevens	Australia	12	23 ± 5 168 ± 1 68 ± 8	6	GPS (SPI HPU, GPSports) 5 Hz	1164 ± 255	106 ± 7	36 ± 2%^* (≥ 12.6 km · h⁻¹)	13 ± 2%^* (≥ 18 km · h⁻¹)	N-R	N-R	N-R

Clarke et al. (2015) [85]	Rugby sevens	Australia	12	22 ± 2 167 ± 4 66 ± 5	4–6	GPS (SPI HPU, GPSports) 5 Hz	3142 ± 879	95 ± 10	629 19%* (12–18 km · h⁻¹)	482 ± 14 13%* (≥ 18 km · h⁻¹)	N-R	N-R	N-R

Clarke et al. (2017) [90]	Rugby sevens	Australia	11	(N-R) 169 ± 2 69 ± 4	12	GPS (SPI HPU, GPSports) 5 Hz	1078 ± 197	86 ± 4	323 ± 87 30 ± 4%^* (12–18 km · h⁻¹)	120 ± 41 11 ± 3%* (≥ 18 km · h⁻¹)	149 ± 39 14 ± 3%* (N-R)	N-R	N-R

Conte et al. (2022) [77]	Rugby sevens	Brazil	14	Backs = 6 24 ± 3 161 ± 7 59 ± 5 Forwards = 8 22 ± 3 167 ± 5 71 ± 6	12	GPS (OptimEye X4, Catapult Innovations) 10 Hz	1119 ± 416	92 ± 1	66 ± 4 5 ± 0.2 m · min⁻¹ (18–20 km · h⁻¹)	97 ± 25 8 ± 2 m · min⁻¹ (≥ 20 km · h⁻¹)	N-R	14 ± 2 1 ± 0.1 n · min⁻¹ (≥ 1.8 m · s⁻²)	21 ± 1 1 ± 0.4 n · min⁻¹ (≤ -1.8 m · s⁻²)

Del coso et al. (2013) [89]	Rugby sevens	Spain-Netherlands	8	23 ± 2 166 ± 7 66 ± 7	3	GPS (SPI Pro X, GPSports) 5 Hz	N-R	87 ± 8	N-R	N-R	N-R	N-R	N-R

Doeven et al. (2019) [91]	Rugby sevens	N-R	10	25 ± 4 169 ± 4 64 ± 5	5	GPS (JOHAN Sports) 10 Hz	1466 ± 120	N-R	366 ± 45 (≥ 12 km · h⁻¹)	N-R	N-R	N-R	N-R

Goodale et al. (2006) [84]	Rugby sevens	N-R	20	24 ± 4 168 ± 6 69 ± 5	N-R	GPS (Minimax S4, Catapult Innovations) 10 Hz	1352 ± 306	87 ± 11	255 ± 94 16 ± 5 m · min⁻¹ (12–18 km · h⁻¹)	112 ± 51 7 ± 3 m · min⁻¹ (18–23 km · h⁻¹)	38 ± 31 2 ± 2 m · min⁻¹ (≥ 23 km · h⁻¹)	N-R	N-R

Malone et al. (2020) [78]	Rugby sevens	N-R	27	24 ± 2 168 ± 7 68 ± 4	36	GPS (Viper; STATSports) 10 Hz	1625 ± 132	116 ± 9	N-R	199 ± 44 14 ± 3 m · min⁻¹ (16–20 km · h⁻¹)	118 ± 45 N° 3.5 ± 1 (≥ 20 km · h⁻¹)	2 ± 1 (≥ 2.5 m · s⁻²)	N-R

Misseldine et al. (2018) [79]	Rugby sevens	N-R	12	Fowards = 5 27 ± 2 170 ± 3 70 ± 2 Backs = 7 24 ± 5 167 ± 5 62 ± 4	6	GPS (JOHAN trackers, JOHAN Sports) 5 Hz	1564 ± 52	98 ± 1	255 ± 30 17 ± 1%* (14–20 km · h⁻¹)	86 ± 37 N° 6 ± 1 6 ± 3%* (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Portillo et al. (2014) [82]	Rugby sevens	Spain	20	INT = 10 26 ± 4 167 ± 7 65 ± 5 NAT = 10 32 ± 6 167 ± 3 66 ± 5	4	GPS (SPI HPU, GPSports) 5 Hz	1503 ± 197	N-R	312 ± 94 (14–20 km · h⁻¹)	83 ± 51 N° 4 ± 3 (≥ 20 km · h⁻¹)	N-R	4 ± 1 (≥ 2 m · s⁻²)	N-R

Reyneke et al. (2018) [80]	Rugby sevens	N-R	15	24 ± 4 168 ± 7 67 ± 6	15	GPS (VX sport 220,Visuallex Sport International) 4 Hz	N-R	90 ± 3	18 ± 1 m · min⁻¹ (12–18 km · h⁻¹)	6 ± 3 m · min⁻¹ (18–21 km · h⁻¹)	4 ± 1 m · min⁻¹ (≥ 21 km · h⁻¹)	N-R	N-R

Suarez-Arrones et al. (2012) [83]	Rugby sevens	N-R	12	28 ± 4 165 ± 6. 64 ± 5	5	GPS (SPI Elite, GPSports) 1 Hz	1556 ± 189	N-R	437 ± 149 28%^* (12–20 km · h⁻¹)	84 ± 65 5.4%* (≥ 20 km · h⁻¹)	N-R	N-R	N-R

Vescovi et al. (2015) [81]	Rugby sevens	Canada	16	N-R	5	GPS (SPI Pro 5, GPSports) 5 Hz	1468 ± 88	95 ± 5	552 ± 76 36 ± 5 m · min⁻¹ (8–16 km · h⁻¹)	224 ± 55 14 ± 3 m · min⁻¹ (16–20 km · h⁻¹)	128 ± 67 8 ± 4 m · min⁻¹ (20–32 km · h⁻¹)	N-R	N-R

Choi et al. (2020) [96]	Field hockey	Korea	52	26 ± 3 165 ± 4 59 ± 5	65	GPS (SPI-HPU, GPSports) 15 Hz	5760 ± 88	N-R	859 ± 90 (≥ 15 km · h⁻¹)	N-R	N-R	16 ± 1 (≥ 3 m · s⁻²)	32 ± 2 (≤ -3 m · s⁻²)

Delves et al. (2021) [101]	Field hockey	Australia	11	22 ± 2 167 ± 6 62 ± 7	14	GPS Catapult (OptimEye X4, Catapult Innovations) 10 Hz	5310 ± 50	N-R	N-R	325 ± 109 (≥ 18 km · h⁻¹)	N-R	N-R	N-R

Kapteijns et al. (2021) [94]	Field hockey	N-R	20	23 ± 4 169 ± 5 62 ± 5	26	GPS (APEX, County Down, STATSports) 18 Hz	5384 ± 835	147 ± 16	796 ± 221 (15–19 km · h⁻¹)	274 ± 105 (≥ 19 km · h⁻¹)	N-R	27 ± 12 (≥ 3 m · s⁻²)	40 ± 15 (≤ -3 m · s⁻²)

Kim et al. (2016) [99]	Field hockey	N-R	32	28 ± 3 165 ± 4 60 ± 4	N-R	GPS (SPI-HPU, GPSports) 5 Hz	5268 ± 77	N-R	580 ± 11 (12–14 km · h⁻¹)	775 ± 19 (18–24 km · h⁻¹)	371 ± 9 n: 28 ± 1 (≥ 24 km · h⁻¹)	N-R	N-R

McGuinness et al. (2018) [100]	Field hockey	N-R	16	23 ± 3 163 ± 13 66 ± 6	7	GPS (S5, Catapult Innovations) 10 Hz	5147 ± 628	113 ± 9	16 ± 5 m · min⁻¹ (≥ 16 km · h⁻¹)	N-R	N-R	N-R	N-R

McMahon et al. (2019) [98]	Field hockey	Ireland	19	23 ± 4 (N-R) 64 ± 6	13	GPS Catapult (OptimEye S5, Catapult Innovations) 10 Hz	5167 ± 1030	N-R	959 ± 294 298 ± 7 m · min⁻¹ (11–19 km · h⁻¹)	N-R	N-R	N-R	N-R

Morencos et al. (2019) [97]	Field hockey	Spain	16	25 ± 3 165 ± 5 58 ± 6	5	GPS (SPI ELITE, GPSport) 10 Hz	5834 ± 931	N-R	892 ± 41 (≥ 15 km · h⁻¹)	848 ± 45 N° 65 ± 1 (≥ 21 km · h⁻¹)	N-R	35 ± 5 3 ± 0.5 n · min⁻¹ (2–3 m · s⁻²)	24 ± 3 2 ± 1 n · min⁻¹ (2–3 m · s⁻²)

Sánchez-Migallón et al. (2020) [95]	Field hockey	N-R	30	23 ± 4 160 ± 1 60 ± 7	1	GPS (RealTrack Systems, WimuProTM) 10 Hz	5456 ± 699	N-R	852 ± 282 16 ± 5%* (12–18 km · h⁻¹)	108 ± 76 1.98 ± 1.40%* (18–21 km · h⁻¹)	n: 24 ± 29 0.5 ± 0.5%* (21–24 km · h⁻¹)	N-R	N-R

[i] ^BPercentage of total distance. ACC: accelerations; DEC: decelerations; GPS: global positions system; HSR: high-speed running; MSR: moderate-speed running; N-R: no reported; TD: total distance; TMA: time-motion analysis.

All rugby sevens studies used GPS devices to record external match load. Rugby sevens female players covered an average of 1549 ± 562 m and 94 ± 9 m × min⁻¹. Regarding zones of intensity, female players performed 355 ± 168, 165 ± 129 and 108 ± 49 m in MSR, HSR, and sprinting respectively. Some studies [79, 83, 85, 86, 90] reported MSR and HSR in terms of proportion of TD (MSR = 28 ± 8%; HSR = 10 ± 4%; sprinting = 14 ± 3%). Regarding distance relative to time in MSR, HSR, and sprinting, players performed 19 ± 13, 10 ± 4, and 5 ± 3 m × min⁻¹, respectively [77, 80, 81, 84]. Lastly, players performed 5 ± 1 sprints, 7 ± 6 ACC and 21 ± 1 DEC per match [77, 78, 82] (Table 3).

Field hockey

In field hockey, GPS devices were used to record external match load (Table 3). Female players covered an average of 5433 ± 265 m TD [94–99, 101, 117], while only two studies reported this variable relative to time 130 ± 24 m × min⁻¹ [94, 100]. Players covered 823 ± 131 m in MSR [94–99], 466 ± 326 m in HSR [94, 95, 97, 99, 101] and 371 ± 9 m in sprinting [99]. Moreover, female field hockey players performed 39 ± 23 sprints, 26 ± 10 ACC and 32 ± 8 DEC per game (Table 3).

Basketball

Table 4 shows the results for basketball, handball and futsal. In basketball, the variables were recorded by TMA (n = 4) and LPS (n = 1). Female basketball players covered 5285 ± 2480 m per match (MSR = 459 ± 70 m; HSR = 1850 ± 12 m; sprint = 925 ± 184 m) [106, 109]. The minutes played were 27 ± 2 min, of which 16 ± 14%, 7 ± 4% and 7 ± 5% corresponded to MSR, HSR, and sprinting respectively [102–105, 107]. This metric was also reported in numbers of actions relative of time (MSR = 2 ± 0.6 m × min⁻¹; HSR = 0.2 ± 0.5 m × min⁻¹; sprint = 0.6 ± 0.6 m × m in⁻¹) [104, 108].

TABLE 4

Summary of the match demands of basketball, handball, and futsal.

Study (year)	Sport	Country	Players (n)	Age (years) Height (cm) Mass (kg)	Match (n)	Device	TD (m)	Pload (AU · min⁻¹)	MSR (m) 12.6–19.8 km · h⁻¹	HSR (m) 19.8–25.2 km · h⁻¹	Sprint (m) ≥ 25.2 km · h⁻¹	ACC (n)	DEC (n)
Conte et al. (2015) [105]	Basketball	Italy	12	27 ± 4 184 ± 1 77 ± 15	5	TMA (Dartfish 6.0 hfixed camera, Sony HD AVCHD HDR-CX115)	N-R	N-R	N° 56 ± 16 9.6 ± 2.5%^* (N-R)	N°63 ± 16 11 ± 1.8%^* (N-R)	n: 44 ± 15 7.8 ± 2.2%^* (N-R)	N-R	N-R

Delextrat et al. (2012) [108]	Basketball	England	9	24 ± 4 173 ± 8 65 ± 11	1	TMA (JVC-x400)	N-R	N-R	N-R	N° 40 ± 14 02 ± 0.5 N° × min⁻¹ (N-R)	n: 26 ± 16 1 ± 0.5 N° × min⁻¹ (N-R)	N-R	N-R

Delextrat et al. (2017) [104]	Basketball	Spain	42	26 ± 4 183 ± 9 (N-R)	3	TMA (LINCE multiplatform sport analysis software Observesport) 25 Hz	N-R	N-R	1.2 ± 0.6 n × min⁻¹ 4.9 ± 2.6%^* (≥ 9 km · h⁻¹)	N-R	0.2 ± 0.2 n × min⁻¹ 0.6 ± 0.6%^*	N-R	N-R

Palmer et al. (2021) [102]	Basketball	Australia	12	25 ± 6 180 ± 11 79 ± 17	20	Triaxial accelerometer (GT9X Actigraph) 100 Hz	N-R	N-R	16.7%^* (15.7–17.4) (≥ 40–90% VO²)^◊	3.3%^* (1.1–3.8) (90–100% VO²)^◊	3.8%^* (2.5–5.3) (≥ 100% VO²)^◊	N-R	N-R

Palmer et al. (2022) [107]	Basketball	Australia	13	25 ± 6 181 ± 11 79 ± 17	21	Triaxial accelerometer (GT9X Actigraph) 100 Hz	N-R	N-R	40.2%^* (35.9–49.1) (40–90% VO² reserve)^◊	10.7%^* (9.8–12.0) (90–100% VO² reserve)^◊	15.1%^* (9.7–25.0) (≥ 100% VO² reserve)^◊	N-R	N-R

Reina et al. (2022) [109]	Basketball	Spain	10	24 ± 3 195 ± 1 93 ± 16	1	LPS (WIMU PROTM systems RealTrack Systems)	3531 ± 310 69 ± 3 m × min⁻¹	1 ± 0.15	459 ± 70 9 ± 1 m × min⁻¹ (≥ 15 km · h⁻¹)	N-R	N-R	18 ± 1 n × min⁻¹	18 ± 1 n × min⁻¹

Scanlan et al. (2012) [106]	Basketball	Australia	12	22 ± 4 174 ± 7 73 ± 14	1	TMA (Labviewsoftware, National Instruments) 7.5 Hz	7039 ± 446	N-R	N-R	1850 ± 13 (11 –25 km · h⁻¹)	925 ± 184 (≥ 25 km · h⁻¹)	N-R	N-R

Stauton et al. (2018) [103]	Basketball	Australia	10	27 ± 5 182 ± 8 81 ± 12	18	Triaxial accelerometer (Link; Actigraph) 100 Hz	N-R	N-R	11 ± 0.5%^* (60–90% VO²)^◊	4 ± 1%^* (90–100% VO²)^◊	6 ± 5%^* (100% VO²)^◊	N-R	N-R

Kniubaite et al. (2019) [110]	Handball	Lithuania	8	23 ± 2 173 ± 5 68 ± 7	14	Triaxial accelerometer (IMU; Optimeye S5 Catapult Innovations) 100 Hz	N-R	9	N-R	N-R	N-R	N-R	N-R

Luteberget et al. (2016) [111]	Handball	Norway	20	25 ± 4 175 ± 4	9	Triaxial accelerometer (IMU; Optimeye S5 Catapult Innovations) 100 Hz	N-R	8.8 ± 2.1	N-R	N-R	N-R	0.7 ± 0.4 n × min⁻¹ (≥ 2.5 m × s⁻²)	2.3 ± 0.9 n × min⁻¹ (≤ -2.5 m × s⁻²)

Luteberget et al. (2017) [112]	Handball	Norway	31	22 ± 3 171 ± 6 68 ± 7	9	Triaxial accelerometer (IMU; Optimeye S5 Catapult Innovations) 100 Hz	N-R	9.85 ± 0.36	N-R	N-R	N-R	N-R	N-R

Manchado et al. (2013) [115]	Handball	Germany-Norway	25	25 ± 3 175 ± 6 68 ± 5	N-R	TMA (camera 25 Hz)	2882 ± 1506	N-R	752 ± 484 m 29.7 ± 3.9%^B 3.4 ± 0.6 n × min⁻¹ (11–20 km · h⁻¹)	272 ± 224 m 10.5 ± 4.1%^B 0.8 ± 0.4 n × min⁻¹ (≥ 20 km · h⁻¹)	N-R	16.7 ± 6.7 n × min⁻¹ (1.5–3 m × s⁻²)	N-R

Michalsik et al. (2014) [114]	Handball	Denmark	24	26 ± 4 174 ± 6 70 ± 7	1–8	TMA (No reported)	4002 ± 551	N-R	93 ± 67 m 0.8 ± 0.5% ^* 2.5 ± 1.8% ^B (≥ 15.5 km · h⁻¹)	10 ± 11 m 0.1%^* 0.2% ^B (≥ 22 km · h⁻¹)	N-R	N-R	N-R

Wik et al. (2016) [113]	Handball	Norway	18	25 ± 4	9	Triaxial accelerometer (IMU; Optimeye S5 Catapult Innovations) 100 Hz	N-R	9.5 ± 1.1	N-R	N-R	N-R	N-R	N-R

Oliva Lozano et al. (2021) [116]	Futsal	Spain	14	24 ± 4 165 ± 6 63 ± 6	5	LPS (WIMU PROTM systems RealTrack Systems) 33 Hz	N-R	N-R	N-R	5 ± 0.4 m · min (≥ 20 km · h⁻¹)	N-R	0.4 ± 0.3 m × min⁻¹ (4–5 m × s−2) 28 ± 0.2 m × min⁻¹ 240 ± 55 m × min⁻¹	28 ± 0.2 m × min⁻¹

* Percentage of total time;

Handball

In handball, variables were extracted using IMU (n = 4) and TMA (n = 2) (Table 4). Handball female players competed an average of 37.6 ± 11.2 min [111, 113, 114] and covered 3442 ± 792 m TD [114, 115] during match-play. Regarding the intensity of the matches (Pload), three studies reported that a mean of 9 ± 0.5 au · min⁻¹ was performed [110–113]. Female handball players covered 423 ± 466 m in MSR and 141 ± 185 m in sprinting, which correspond to 16 ± 19% and 5 ± 7 % respectively of TD [114, 115]. During the competition, the players performed 8.7 ± 11 ACC × min⁻¹ and 2.3 ± 0.9 DEC × min⁻¹ [111, 115] (Table 4).

Futsal

In futsal, only one study met the inclusion criteria [116]. Five matches were monitored using LPS. The players covered a mean of 5 ± 0.4 m × min⁻¹ in HSR. The maximum ACC was 6 ± 0.2 m × s⁻²; a total of 240 ± 55 m × min⁻¹ in ACC was recorded, with a total of 28 ± 0.3 ACC × min⁻¹, of which 0.4 ± 0.3 ACC × min⁻¹ was performed above 4–5 m × s⁻². The maximum DEC was 6 ± 2 m × s⁻² and an average of 28 ± 0.2 per minute [116] (Table 4).

TABLE

Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist

SECTION	ITEM	PRISMA-ScR CHECKLIST ITEM	REPORTED ON PAGE #
TITLE

Title	1	Identify the report as a scoping review.	175

ABSTRACT

Structured summary	2	Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives.	175

INTRODUCTION

Rationale	3	Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.	175–176

Objectives	4	Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.	176

METHODS

Protocol and registration	5	Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.	176

Eligibility criteria	6	Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.	176

Information sources*	7	Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed.	176

Search	8	Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated.	176

Selection of sources of evidence†	9	State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.	176

Data charting process‡	10	Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.	176

Data items	11	List and define all variables for which data were sought and any assumptions and simplifications made.	176

Critical appraisal of individual sources of evidence§	12	If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).	176

Synthesis of results	13	Describe the methods of handling and summarizing the data that were charted.	176

RESULTS

Selection of sources of evidence	14	Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.	176–178

Characteristics of sources of evidence	15	For each source of evidence, present characteristics for which data were charted and provide the citations.	176–178

Critical appraisal within sources of evidence	16	If done, present data on critical appraisal of included sources of evidence (see item 12).	178


Results of individual sources of evidence	17	For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives.	176–194

Synthesis of results	18	Summarize and/or present the charting results as they relate to the review questions and objectives.	176–194

DISCUSSION

Summary of evidence	19	Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups.	193–194

Limitations	20	Discuss the limitations of the scoping review process.	194

Conclusions	21	Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps.	194–195

FUNDING

Funding	22	Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.	195

Note: JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.

* Where sources of evidence (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites.

† A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with information sources (see first footnote).

‡ The frameworks by Arksey and O’Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting.

§ The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of “risk of bias” (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).

From: Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMAScR): Checklist and Explanation. Ann Intern Med. 2018;169:467–473. doi: 10.7326/M18-0850.

DISCUSSION

This scoping review provides an overview of research on the physical demands of female athletes in elite team sports. Football was the most researched sport. In contrast, women’s indoor sports have been less researched. In particular, GPS have emerged as the main devices used to monitor the physical demands of outdoor team sports (i.e., soccer, rugby, field hockey) and, on the other hand, accelerometers and TMA have been more commonly used to measure the physical demands of indoor sports (i.e., basketball, handball, futsal). It should be noted that the demands of matches vary significantly between sports, as each sport has its own characteristics and requirements. Therefore, a thorough understanding of the physical demands of different team sports is crucial to optimise training and performance, reduce the risk of injury and improve player well-being.

Considering female soccer, TD covered were ~9556 m and 103 ± 6 m × min⁻¹ when considered in relative distance. Similar results were obtained in a previous meta-analysis [118] but with male players. Regarding intensity zones, there was observed high variability in MSR (range: 570–2520 m), HSR (range: 101–1490 m), and sprinting (range: 22–995 m). This could be explained by the differences in devices (TMA vs. GPS), sampling frequencies (i.e. 1–15 Hz) or ranges of velocity used. The same was observed when relative distance in MSR (6–27 m × min⁻¹) was analysed. Although the number of sprints was reported, no previous consensus was established about the velocity that should be considered (e.g. > 21 km × h⁻¹ – > 25 km × h⁻¹); this phenomenon could explain the differences in results (9–70 number of sprint), and it was repeated in male studies as well [16]. In relation to the ACC and DEC actions, these variables can be strongly influenced by the device used and its sensitivity, as well as the duration of the action to be considered as ACC or DEC (i.e. 2–3 seconds) [119]. Studies [32–35, 41, 43, 54, 56, 63, 65] revealed that players performed a range of 8–423 and 15–430 in ACC-DEC actions per match respectively, while male soccer players performed about 64 ACC and 58 DEC actions per match (2–3 m × s⁻¹) [120]. Knowledge of the demands of elite women’s soccer matches can be very useful for coaches, physical trainers, and physiotherapists to plan tailor-made training and return-to-play sessions.

In rugby league and union very similar TD were reported, with a mean of ~5533 m [72, 75, 87] and ~5458 m [71, 73, 74, 76] respectively. Considering TD performed per minute, the rugby league players performed ~77 m × min⁻¹ and the rugby union players about ~65 m × min⁻¹. In rugby sevens TD was ~1549 m [76–79, 81, 82, 84–86, 90, 91], ~72% lower than rugby league and union; however, when reported relative to time it was slightly higher at 94 m × min⁻¹. Considering distances, female rugby league and union players covered 934 m and 114 m in MSR and HSR, respectively, whilst sevens elite female players performed 355 m in MSR, 165 m in HSR, and 108 m in sprinting. A recent meta-analysis [121] found that male sevens players covered 1100–2486 m of TD, 77–121 m × min⁻¹, ~449 m in MSR and ~190 m in HSR – greater distance than women players, especially at high speeds. The same was observed in rugby league and union male players, who performed greater distances [122, 123]. Female rugby players completed a mean of 7 and 5 sprints per match in rugby league/union and rugby sevens respectively. The variability of results may be explained by positional differences of rugby demands (i.e., backs, forwards) and the differences in the sports’ rules and discipline. Therefore, reference values from different rugby disciplines are important, especially when players interchange within rugby sports, or return to play following a long-term injury or illness.

In field hockey, TD covered was similar in studies, ~5403 m [94–99, 101], of which ~823 m were in MSR, ~466 m in HSR and ~371 m in sprinting. Slightly lower results were found by James et al. [124] in male players (TD = ~4861 m; > 14.5 km × h⁻¹ = ~1193 m; > 19 km × h⁻¹ = ~402 m). Elite female field hockey players performed a mean of ~39 sprints, ~26 ACC and ~32 DEC actions; however, male field hockey players [124] reported that they performed ~21 sprints, ~50 ACC and ~60 DEC actions per match. Coaches and physical trainers may know the demands that competition requires, and in consequence these values can help to better understand the efforts that hockey players make during the competition. This would make it possible to compare the physical level with elite hockey reference values and draw the lines of work for both conditioning and recovery; however, more research is needed.

In female basketball, the TD covered was 7039 m, using TMA [82], and similar results were obtained for male players in a systematic review [125] (TD = ~7558 m) when the same system was used. Reina et al. [109] used LPS and found that women players covered 3531 m. The studies indicated that the proportion of movement performed by female basketball players was: MSR ~16%, HSR ~7%, and sprinting ~7%; while male players covered ~40% in MSR, ~25% in HSR, and ~0.4% in sprinting [125]. Also, elite female basketball players did ~35 sprint actions per match [105, 108]. Although few studies are available, these values can help to better understand the demands of elite women’s basketball, and further investigation is warranted.

Regarding handball demands, studies that used TMA analysis reported that TD ranged between 2882 and 4002 m [114, 115]. Similar results were found in male players (i.e., ~3.5 km) [126, 127]. Elite female handball players covered ~423 m in MSR and ~141 m in HSR; similarly, during professional men’s matches, players covered 356–670 m in MSR and 133–153 m in HSR. Moreover, the range of Pload was 8.8–10.6 au × min⁻¹ [110–113] and women players performed 8.7–2.3 ACC and DEC per minute respectively.

Considering futsal, only one study [116] recorded female futsal match demands, using LPS. Players ran an average of ~5 m × min in HSR, with a threshold close to 20 km × h⁻¹. In addition, approximately ~0.4 ACC per minute of play (> 4–5 m × s⁻²) were performed, the maximum ACC was 6 m × s⁻² and 240 m × min⁻¹ were covered in ACC, which corresponds to a total of ~28 ACC × min⁻¹. The maximum DEC was ~-6 m × s⁻² and ~28 DEC × min⁻¹ was performed. However, male futsal players presented higher match demands when compared to female futsal players [29]. Given that, methods and strategies in female’s team sports should not be supported by evidence derived from male athletes.

There is limited evidence available regarding external load monitoring in indoor sports. This could be attributed to the fact that many indoor sports are practised in confined spaces, which makes it challenging to use tracking and monitoring devices compared to outdoor sports (due to e.g. high cost, complex installation, variables) [128, 129]. Each tracking technology has unique approaches to monitoring athletes, resulting in distinct advantages and disadvantages when tracking external load; therefore, it is essential to consider how the technology and its manufacturer process data within the context of the sport [11].

It should be noted that there are a number of contextual factors (i.e., team characteristics, style of play, opponent characteristics, moods, starter/non-starter, competition situations and venue) that may have influenced the variability of the data [130, 131]. The context can significantly affect the performance of the players and, therefore, the results obtained through the tracking system. It is important for staff to consider these variables when analysing the demands of competition and the variation that can occur from match to match. Therefore, it is recommended to avoid drawing absolute conclusions from a single measurement and instead analyse multiple data points to gain an overall understanding of the demands of competition.

On the other hand, this study established specific speed ranges for MSR, HSR, and sprinting to simplify the summary and comparison of results regarding the distance covered. However, the selection of speed thresholds lacks consensus, particularly regarding external load monitoring with wearable devices for female athletes. While most studies have focused on male athletes, some have suggested that speed thresholds set for men may not be applicable for women due to underestimation of efforts and inaccuracy of results [86, 132, 133]. Therefore, the authors recommend using relative thresholds in monitoring with wearable devices for better interpretation of results. Considering individual athlete performance and the use of absolute thresholds allows for a broader comparison and establishment of general goals [86, 134–136]. Consequently, further evidence is needed to determine whether female athletes require a different external load control approach than male athletes and whether it differs between sports.

Another point to consider is the definition of “elite” status in sports, which is a complex issue depending on several factors [137]. Generally, elite athletes are those who have achieved a high level of performance in their sport and compete at a professional level or in international competitions; criteria such as world ranking in a given sport discipline, history of achievement in major competitions, Olympic medal winning, or participation in national teams could be used [138, 139]. Nonetheless, defining elite status in sport can be challenging because it can vary depending on the sport and country in question [137]. Additionally, the level of performance required to be considered an elite athlete may change over time as sports evolve and athletes become stronger and faster [140, 141].

This study is limited by the lack of consistency of the devices (i.e., GPS, TMA, LPS), thresholds of different actions (i.e. zones of intensity, sprint, ACC, DEC), and sampling frequencies (1–15 Hz) that have been used. Lower sampling frequencies (e.g. 1 Hz, 5 Hz) have been shown to be less reliable than 10 Hz [119, 142], whereas with 10 Hz, the occurrence of high-intensity ACC and DEC actions can be obtained reliably, although distance and time-related variables are less reliable [119, 143]. The data filtering technique used by different software and upgrades can also influence the quality, reliability, and usefulness of the data [143, 144]. In addition, the minimum time that an ACC or DEC action must stabilize above the threshold to be determined as effort could generate inaccuracies in the frequencies of ACC and DEC of greater intensity [145]. Depending on the variables analysed, in elite female athletes, analysing between 3 and 9 matches, less than 10% error was found for profiling [146]. Finally, the present study did not consider positional differences or other variables (e.g., impacts, ACC and DEC zones, and peak velocity, among others) that might be of interest. Therefore, practitioners and researchers should carefully consider the methodology used and the criteria used to delineate the variables of interest.

CONCLUSIONS

In conclusion, this systematic review provides information regarding the match demands of elite female team sports. Soccer is the most investigated sport; female players perform ~9500 m TD; also they do ~580 m in HSR with a great number of ACC, DEC, and sprints. Rugby league and union players cover a greater distance (~5450 m) when compared to rugby sevens (~1550 m); however, rugby sevens is more demanding in terms of high-intensity actions. Women’s field hockey players perform ~5400 m TD; also, it is a high-intensity sport, with high-speed and sprint actions. Women’s indoor sports are less studied, which could be due to the difficulty and high cost of measuring the external load indoors. Female basketball players cover ~5300 m TD, of which 7% are in MSR. In handball, elite women’s players perform ~3500 m TD; also, they cover ~423 m in MSR and ~141 m in HSR. Finally, female elite futsal players perform ~5 m × min⁻¹ in HSR and they do a great number of high-intensity activities (i.e., HSR, ACC, and DEC actions). We consider that the results obtained from the existing research on the competitive demands of female athletes in team sports should be considered as a starting point, while keeping in mind the limitations discussed earlier. Additionally, it is important to customize the methods for external load monitoring based on the particular context and objectives of each sport. Lastly, we strongly recommend that researchers and professionals continue to explore and expand the knowledge on external load monitoring in female athletes.

Acknowledgments

This research received no external funding.

Competing interests

Authors declare that they have no competing interests.

REFERENCES

Fink JS. Female athletes, women’s sport, and the sport media commercial complex: Have we really “come a long way, baby”? Sport Manag Rev. 2015 Jul 1; 18(3):331–42.

O’Brien M, Robertson A. Women and Sport. Scott Med J. 2010 May; 55(2):25–8.

Emmonds S, Heyward O, Jones B. The Challenge of Applying and Undertaking Research in Female Sport. Sports – Open. 2019 Dec; 5(1):51.

Elliott-Sale KJ, Minahan CL, de Jonge XAKJ, Ackerman KE, Sipilä S, Constantini NW, Lebrun CM, Hackney AC. Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants: A Working Guide for Standards of Practice for Research on Women. Sports Med. 2021 May; 51(5):843–61.

Benson LC, Räisänen AM, Volkova VG, Pasanen K, Emery CA. Workload a-WEAR-ness: Monitoring Workload in Team Sports With Wearable Technology. A Scoping Review. J Orthop Sports Phys Ther. 2020 Oct; 50(10):549–63.

Nassis GP, Brito J, Tomás R, Heiner-Møller K, Harder P, Kryger KO, Krustrup P. Elite women’s football: Evolution and challenges for the years ahead. Scand J Med Sci Sports. 2022 Apr; 32 Suppl 1:7–11.

Halson SL. Monitoring Training Load to Understand Fatigue in Athletes. Sports Med. 2014 Nov; 44(S2):139–47.

Vanrenterghem J, Nedergaard NJ, Robinson MA, Drust B. Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways. Sports Med. 2017 Nov; 47(11):2135–42.

Wallace LK, Slattery KM, Coutts AJ. The Ecological Validity and Application of the Session-RPE Method for Quantifying Training Loads in Swimming. J Strength Cond Res. 2009 Jan; 23(1):33–8.

Soligard T, Schwellnus M, Alonso JM, Bahr R, Clarsen B, Dijkstra HP, Gabbett T, Gleeson M, Hägglund M, Hutchinson MR, Janse van Rensburg C, Khan KM, Meeusen R, Orchard JW, Pluim BM, Raftery M, Budgett R, Engebretsen L. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med. 2016 Sep; 50(17):1030–41.

Torres-Ronda L, Beanland E, Whitehead S, Sweeting A, Clubb J. Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis. Sports Med – Open. 2022 Jan 25; 8(1):15.

Ben Abdelkrim N, El Fazaa S, El Ati J, Tabka Z. Time-motion analysis and physiological data of elite under- 19-year-old basketball players during competition * Commentary. Br J Sports Med. 2007 Feb 1; 41(2):69–75.

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 Nov; 48(11):2549–75.

Cummins C, Orr R, O’Connor H, West C. Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Med. 2013; 43(10):1025–42.

Barbero-Alvarez JC, Soto VM, Barbero-Alvarez V, Granda-Vera J. Match analysis and heart rate of futsal players during competition. J Sports Sci. 2008 Jan; 26(1):63–73.

Taylor JB, Wright AA, Dischiavi SL, Townsend MA, Marmon AR. Activity Demands During Multi-Directional Team Sports: A Systematic Review. Sports Med. 2017; 47(12):2533–51.

Buchheit M, Simpson BM. Player-Tracking Technology: Half-Full or Half-Empty Glass? Int J Sports Physiol Perform. 2017 Apr; 12(Suppl 2):S235–41.

Colby MJ, Dawson B, Heasman J, Rogalski B, Gabbett TJ. Accelerometer and GPS-Derived Running Loads and Injury Risk in Elite Australian Footballers. J Strength Cond Res. 2014 Aug; 28(8):2244–52.

Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2017 Mar; 51(5):452–9.

Duhig S, Shield AJ, Opar D, Gabbett TJ, Ferguson C, Williams M. Effect of high-speed running on hamstring strain injury risk. Br J Sports Med. 2016 Dec; 50(24):1536–40.

Osgnach C, Poser S, Bernardini R, Rinaldo R, Di Prampero PE. Energy Cost and Metabolic Power in Elite Soccer: A New Match Analysis Approach. Med Sci Sports Exerc. 2010 Jan; 42(1):170–8.

Vescovi JD, Fernandes E, Klas A. Physical Demands of Women’s Soccer Matches: A Perspective Across the Developmental Spectrum. Front Sports Act Living. 2021; 3:634696.

Costello JT, Bieuzen F, Bleakley CM. Where are all the female participants in Sports and Exercise Medicine research? Eur J Sport Sci. 2014 Nov 17; 14(8):847–51.

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg Lond Engl. 2010; 8(5):336–41.

Russell JL, McLean BD, Impellizzeri FM, Strack DS, Coutts AJ. Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices. Sports Med. 2021 Jan; 51(1):81–112.

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 Apr; 12(s2):S2–18–S2–26.

Kim SY, Park JE, Lee YJ, Seo HJ, Sheen SS, Hahn S, Jang BH, Son HJ. Testing a tool for assessing the risk of bias for nonrandomized studies showed moderate reliability and promising validity. J Clin Epidemiol. 2013 Apr; 66(4):408–14.

Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998 Jun 1; 52(6):377–84.

Spyrou K, Freitas TT, Marín-Cascales E, Alcaraz PE. Physical and Physiological Match-Play Demands and Player Characteristics in Futsal: A Systematic Review. Front Psychol. 2020 Nov 6; 11:569897.

Yousefian F, Hüttemann H, Borjesson M, Ekblom P, Mohr M, Fransson D. Physical workload and fatigue pattern characterization in a top-class women’s football national team: a case study of the 2019 FIFA Women’s World Cup. J Sports Med Phys Fitness. 2021 Aug; 61(8):1081–90.

Winther AK, Baptista I, Pedersen S, Randers MB, Johansen D, Krustrup P, Pettersen SA. Position specific physical performance and running intensity fluctuations in elite women’s football. Scand J Med Sci Sports. 2021 Nov 26;

Villaseca-Vicuña R, Otero-Saborido FM, Perez-Contreras J, Gonzalez-Jurado JA. Relationship between Physical Fitness and Match Performance Parameters of Chile Women’s National Football Team. Int J Environ Res Public Health. 2021 Aug 9; 18(16).

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 Print. 2021 Oct; 29(5):449–61.

Panduro J, Ermidis G, Røddik L, Vigh-Larsen JF, Madsen EE, Larsen MN, Pettersen SA, Krustrup P, Randers MB. 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 Mar 22;

Principe VA, Seixas-da-Silva IA, de Souza Vale RG, Moreira Nunes R de A. GPS technology to control of external demands of elite Brazilian female football players during competitions. Retos-Nuevas Tend En Educ Fis Deporte Recreacion. 2021; (40):18–26.

Krustrup P, Mohr M, Nybo L, Draganidis D, Randers MB, Ermidis G, Ørntoft C, Røddik L, Batsilas D, Poulios A, Ørtenblad N, Loules G, Deli CK, Batrakoulis A, Nielsen JL, Jamurtas AZ, Fatouros IG. Muscle metabolism and impaired sprint performance in an elite women’s football game. Scand J Med Sci Sports. 2021 Jun 25;

Julian R, Skorski S, Hecksteden A, Pfeifer C, Bradley PS, Schulze E, Meyer T. Menstrual cycle phase and elite female soccer match-play: influence on various physical performance outputs. Sci Med Footb. 2021 May; 5(2):97–104.

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; 3:604605.

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 Sep 4; 1–7.

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 Footb. 2020 Jul 2; 4(3):211–5.

Ramos GP, Nakamura FY, Penna EM, Wilke CF, Pereira LA, Loturco I, Capelli L, Mahseredjian F, Silami-Garcia E, Coimbra CC. Activity Profiles in U17, U20, and Senior Women’s Brazilian National Soccer Teams During International Competitions: Are There Meaningful Differences? J Strength Cond Res. 2019a; 33(12):3414–22.

Park LAF, Scott D, Lovell R. Velocity zone classification in elite women’s football: where do we draw the lines? Sci Med Footb. 2018 Jan 2; 3(1):21–8.

Trewin J, Meylan C, Varley MC, Cronin J. The match-to-match variation of match-running in elite female soccer. J Sci Med Sport. 2017 Feb; 21(2):196–201.

Vescovi JD. Sprint profile of professional female soccer players during competitive matches: Female Athletes in Motion (FAiM) study. J Sports Sci. 2012; 30(12):1259–65.

DeWitt JK, Gonzales M, Laughlin MS, Amonette WE. External loading is dependent upon game state and varies by position in professional women’s soccer. Sci Med Footb. 2018 Jul 3; 2(3):225–30.

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 Jun; 31(6):1500–8.

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 Sep; 31(9):2379–87.

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 Jul; 32(7):2002–9.

Meylan C, Trewin J, McKean K. Quantifying Explosive Actions in International Women’s Soccer. Int J Sports Physiol Perform. 2016 Mar; 12(3):310–5.

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 Dec; 32(20):1874–80.

Andersson HA, Randers MB, Heiner-Moller 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 Apr; 24(4):912–9.

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 Mar; 22(2):341–9.

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 Jul; 37(7):1242–8.

Goncalves L, Manuel Clemente F, Ignacio Barrera J, Sarmento H, Tomas Gonzalez-Fernandez F, Palucci Vieira LH, Jose Figueiredo A, Clark CCT, Cancela Carral JM. Relationships between Fitness Status and Match Running Performance in Adult Women Soccer Players: A Cohort Study. Med-Lith. 2021 Jun; 57(6).

Vescovi JD, Falenchuk O. Contextual factors on physical demands in professional women’s soccer: Female Athletes in Motion study. Eur J Sport Sci. 2019 Mar; 19(2):141–6.

Fernandes R, Ceylan Hİ, Clemente FM, Brito JP, Martins AD, Nobari H, Reis VM, Oliveira R. In-Season Microcycle Quantification of Professional Women Soccer Players— External, Internal and Wellness Measures. Healthcare. 2022 Apr 7; 10(4):695.

García-Ceberino JM, Bravo A, de la Cruz-Sánchez E, Feu S. Analysis of Intensities Using Inertial Motion Devices in Female Soccer: Do You Train like You Compete? Sensors. 2022 Apr 8; 22(8):2870.

Diaz-Seradilla E, Rodríguez-Fernández A, Rodríguez-Marroyo JA, Castillo D, Raya-González J, Villa Vicente JG. Inter- and intra-microcycle external load analysis in female professional soccer players: A playing position approach. Clemente FM, editor. Plos One. 2022 Mar 22; 17(3):e0264908.

Pedersen S, Welde B, Sagelv EH, Heitmann KA, B. Randers M, Johansen D, Pettersen SA. Associations between maximal strength, sprint, and jump height and match physical performance in high-level female football players. Scand J Med Sci Sports. 2022 Apr; 32(S1):54–61.

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 Feb; 33:159–71.

Datson N, Drust B, Weston M, Gregson W. Repeated high-speed running in elite female soccer players during international competition. Sci Med Footb. 2019 Apr 3; 3(2):150–6.

Gabbett TJ, Mulvey MJ. Time-Motion Analysis of Small-Sided Training Games and Competition in Elite Women Soccer Players. J Strength Cond Res. 2008 Mar; 22(2):543–52.

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 Sep; 20(9):867–72.

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 Jul; 31(7):1802–10.

Ramos G, Nakamura F, Pereira L, Junior W, Mahseredjian F, Wilke C, Garcia E, Coimbra C. 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 Sep; 38(10):747–54.

Ramos GP, Datson N, Mahseredjian F, Lopes TR, Coimbra CC, Prado LS, Nakamura FY, Penna EM. Activity profile of training and matches in Brazilian Olympic female soccer team. Sci Med Footb. 2019b Jul 3; 3(3):231–7.

Choi JH, Joo CH. Match activity profile of professional female soccer players during a season. J Exerc Rehabil. 2022 Oct 26; 18(5):324–9.

Kobal R, Carvalho L, Jacob R, Rossetti M, Oliveira L, Do Carmo E, Barroso R. Comparison among U-17, U-20, and Professional Female Soccer in the GPS Profiles during Brazilian Championships. Int J Environ Res Public Health. 2022b Dec; 19(24).

Kobal R, Aquino R, Carvalho L, Serra A, Sander R, Gomes N, Concon V, Ramos G, Barroso R. Does the Number of Substitutions Used during the Matches Affect the Recovery Status and the Physical and Technical Performance of Elite Women’s Soccer? Int J Environ Res Public Health. 2022a Sep; 19(18).

Villaseca-Vicuna R, Perez-Contreras J, Zabaloy S, Merino-Munoz P, Valenzuela L, Burboa J, Gonzalez-Jurado J. Comparison of Match Load and Wellness between Friendly and World Cup Matches in Elite Female Soccer Players. Appl Sci-BASEL. 2023 Feb; 13(3).

Woodhouse LN, Tallent J, Patterson SD, Waldron M. Elite international female rugby union physical match demands: A five-year longitudinal analysis by position and opposition quality. J Sci Med Sport. 2021 Nov; 24(11):1173–9.

Newans T, Bellinger P, Buxton S, Quinn K, Minahan C. Movement Patterns and Match Statistics in the National Rugby League Women’s (NRLW) Premiership. Front Sports Act Living. 2021; 3:618913.

Callanan D, Rankin P, Fitzpatrick P. An Analysis of the Game Movement Demands of Women’s Interprovincial Rugby Union. J Strength Cond Res. 2021 Dec 1; 35(Suppl 2):S20–5.

Sheppy E, Hills SP, Russell M, Chambers R, Cunningham DJ, Shearer D, Heffernan S, Waldron M, McNarry M, Kilduff LP. Assessing the whole-match and worst-case scenario locomotor demands of international women’s rugby union match-play. J Sci Med Sport. 2020 Jun; 23(6):609–14.

Emmonds S, Weaving D, Dalton-Barron N, Rennie G, Hunwicks R, Tee J, Owen C, Jones B. Locomotor characteristics of the women’s inaugural super league competition and the rugby league world cup. J Sports Sci. 2020 Nov; 38(21):2454–61.

Suarez-Arrones L, Portillo J, Pareja-Blanco F, Sáez de Villareal E, Sánchez-Medina L, Munguía-Izquierdo D. Match-play activity profile in elite women’s rugby union players. J Strength Cond Res. 2014 Feb; 28(2):452–8.

Conte D, Arruda AFS, Clemente FM, Castillo D, Kamarauskas P, Guerriero A. Assessing the Relationship Between External and Internal Match Loads in Elite Women’s Rugby Sevens. Int J Sports Physiol Perform. 2022; 1–6.

Malone S, Earls M, Shovlin A, Eddy A, Winkelman N. Match-Play Running Performance and Exercise Intensity in Elite International Women’s Rugby Sevens. J Strength Cond Res. 2020 Jun; 34(6):1741–9.

Misseldine ND, Blagrove RC, Goodwin JE. Speed Demands of Women’s Rugby Sevens Match Play. J Strength Cond Res. 2018; 35(1):183–9.

Reyneke J, Hansen K, Cronin JB, Macadam P. An investigation into the influence of score differential on the physical demands of international women’s rugby sevens match play. Int J Perform Anal SPORT. 2018; 18(4):523–31.

Vescovi JD, Goodale T. Physical Demands of Women’s Rugby Sevens Matches: Female Athletes in Motion (FAiM) Study. Int J Sports Med. 2015 Oct; 36(11):887–92.

Portillo J, Ma Gonzalez-Rave J, Juarez D, Garcia JM, Suarez-Arrones L, Newton RU. Comparison of Running Characteristics And Heart Rate Response Of International And National Female Rugby Sevens Players During Competitive Matches. J Strength Cond Res. 2014 Aug; 28(8):2281–9.

Suarez-Arrones L, Nuñez FJ, Portillo J, Mendez-Villanueva A. Match running performance and exercise intensity in elite female Rugby Sevens. J Strength Cond Res. 2012 Jul; 26(7):1858–62.

Goodale TL, Gabbett TJ, Tsai MC, Stellingwerff T, Sheppard J. The Effect of Contextual Factors on Physiological and Activity Profiles in International Women’s Rugby Sevens. Int J Sports Physiol Perform. 2006 Mar; 12(3):370–6.

Clarke AC, Anson JM, Pyne DB. Neuromuscular Fatigue and Muscle Damage After a Women’s Rugby Sevens Tournament. Int J Sports Physiol Perform. 2015 Sep; 10(6):808–14.

Clarke AC, Anson J, Pyne D. Physiologically based GPS speed zones for evaluating running demands in Women’s Rugby Sevens. J Sports Sci. 2014; 33(11):1101–8.

Quinn K, Newans T, Buxton S, Thomson T, Tyler R, Minahan C. Movement patterns of players in the Australian Women’s Rugby League team during international competition. J Sci Med Sport. 2019; 23(3):315–9.

Clarke AC, Presland J, Rattray B, Pyne DB. Critical velocity as a measure of aerobic fitness in women’s rugby sevens. J Sci Med Sport. 2014 Jan; 17(1):144–8.

Del Coso J, Portillo J, Muñoz G, Abián-Vicén J, Gonzalez-Millán C, Muñoz-Guerra J. Caffeine-containing energy drink improves sprint performance during an international rugby sevens competition. Amino Acids. 2013 Jun; 44(6):1511–9.

Clarke AC, Anson JM, Pyne DB. Game movement demands and physical profiles of junior, senior and elite male and female rugby sevens players. J Sports Sci. 2017 Apr 18; 35(8):727–33.

Doeven SH, Brink MS, Huijgen BCH, de Jong J, Lemmink KAPM. High Match Load’s Relation to Decreased Well-Being During an Elite Women’s Rugby Sevens Tournament. Int J Sports Physiol Perform. 2019 Sep 1; 14(8):1036–42.

Busbridge AR, Hamlin MJ, Jowsey JA, Vanner MH, Olsen PD. Running Demands of Provincial Women’s Rugby Union Matches in New Zealand. J Strength Cond Res. 2022 Apr; 36(4):1059–63.

Nolan D, Curran O, Brady AJ, Egan B. Physical Match Demands of International Women’s Rugby Union: A Three-Year Longitudinal Analysis of a Team Competing in The Women’s Six Nations Championship. J Funct Morphol Kinesiol. 2023 Mar 2; 8(1):32.

Kapteijns JA, Caen K, Lievens M, Bourgois JG, Boone J. Positional Match Running Performance and Performance Profiles of Elite Female Field Hockey. Int J Sports Physiol Perform. 2021 Mar 4; 1–8.

Sanchez-Migallon V, Lopez-Samanes A, Terron-Manrique P, Morencos E, Fernandez-Ruiz V, Navandar A, Moreno-Perez V. The Acute Effect of Match-Play on Hip Isometric Strength and Flexibility in Female Field Hockey Players. Appl Sci. 2020 Jul; 10(14).

Choi H, Kim T. The relationship between global positioning system variables and injury occurrences in female field hockey. Proc Inst Mech Eng Part P-J Sports Eng Technol. 2020 Dec; 234(4):291–7.

Morencos E, Casamichana D, Torres L, Romero-Moraleda B, Haro X, Rodas G. Kinematic Demands of International Competition in Women’s Field Hockey. Apunts Educ Fis Deport. 2019 Sep; (137):56–70.

McMahon GE, Kennedy RA. Changes in Player Activity Profiles After The 2015 Fih Rule Changes In Elite Women’s Hockey. J Strength Cond Res. 2019 Nov; 33(11):3114–22.

Kim T, Cha JH, Park JC. Association between in-game performance parameters recorded via global positioning system and sports injuries to the lower extremities in elite female field hockey players. Clust Comput – J Netw Softw Tools Appl. 2016 Mar; 21(1):1069–78.

100

McGuinness A, McMahon G, Malone S, Kenna D, Passmore D, Collins K. Monitoring Wellness, Training Load, and Running Performance During a Major International Female Field Hockey Tournament. J Strength Cond Res. 2020 Aug; 34(8):2312–20.

101

Delves RIM, Bahnisch J, Ball K, Duthie GM. Quantifying Mean Peak Running Intensities in Elite Field Hockey. J Strength Cond Res. 2021 Sep; 35(9):2604–10.

102

Palmer J, Wundersitz D, Bini R, Kingsley M. Effect of Player Role and Competition Level on Player Demands in Basketball. Sports Basel Switz. 2021 Mar 8; 9(3).

103

Staunton C, Wundersitz D, Gordon B, Kingsley M. Accelerometry-Derived Relative Exercise Intensities in Elite Women’s Basketball. Int J Sports Med. 2018 Oct; 39(11):822–7.

104

Delextrat A, Badiella A, Saavedra V, Matthew D, Schelling X, Torres-Ronda L. Match activity demands of elite Spanish female basketball players by playing position. Int J Perform Anal SPORT. 2017; 15(2):687–703.

105

Conte D, Favero TG, Lupo C, Francioni FM, Capranica L, Tessitore A. Time-Motion Analysis of Italian Elite Women’s Basketball Games: Individual And Team Analyses. J Strength Cond Res. 2015 Jan; 29(1):144–50.

106

Scanlan AT, Dascombe BJ, Reaburn P, Dalbo VJ. The physiological and activity demands experienced by Australian female basketball players during competition. J Sci Med Sport. 2012 Jul; 15(4):341–7.

107

Palmer JA, Bini R, Wundersitz D, Kingsley M. On-Court Activity and Game-Related Statistics during Scoring Streaks in Basketball: Applied Use of Accelerometers. Sensors. 2022 May 27; 22(11):4059.

108

Delextrat A, Trochym E, Calleja-González J. Effect of a typical inseason week on strength jump and sprint performances in national-level female basketball players. J Sports Med Phys Fitness. 2012 Apr; 52(2):128–36.

109

Reina M, Mancha-Triguero D, Ibanez S. Monitoring of A Competitive Microcycle In Professional Women’s Basketball Through Inertial Devices. Rev Int Med Cienc Act Fis Deporte. 2022 Sep; 22(87):663–85.

110

Kniubaite A, Skarbalius A, Clemente FM, Conte D. Quantification of external and internal match loads in elite female team handball. Biol Sport. 2019 Dec; 36(4):311–6.

111

Luteberget LS, Spencer M. High-Intensity Events in International Women’s Team Handball Matches. Int J Sports Physiol Perform. 2016 Jan; 12(1):56–61.

112

Luteberget LS, Trollerud HP, Spencer M. Physical demands of game-based training drills in women’s team handball. J Sports Sci. 2017 Mar; 36(5):592–8.

113

Wik EH, Luteberget LS, Spencer M. Activity Profiles in International Women’s Team Handball Using PlayerLoad. Int J Sports Physiol Perform. 2016 Aug; 12(7):934–42.

114

Michalsik LB, Madsen K, Aagaard P. Match performance and physiological capacity of female elite team handball players. Int J Sports Med. 2014 Jun; 35(7):595–607.

115

Manchado C, Pers J, Navarro F, Han A, Sung E, Platen P. Time-motion analysis in women’s team handball: Importance of aerobic performance. J Hum Sport Exerc. 2013; 8(2 SUPPL):376–90.

116

Oliva-Lozano JM, Muyor JM, Puche Ortuño D, Rico-González M, Pino-Ortega J. Analysis of key external and internal load variables in professional female futsal players: a longitudinal study. Res Sports Med. 2021;

117

McGuinness A, Passmore D, Malone S, Collins K. Peak Running Intensity of Elite Female Field Hockey Players During Competitive Match Play. J Strength Cond Res. 2020 Apr 1;

118

Julian R, Page RM, Harper LD. The Effect of Fixture Congestion on Performance During Professional Male Soccer Match-Play: A Systematic Critical Review with Meta-Analysis. Sports Med. 2021 Feb; 51(2):255–73.

119

Delaney JA, Cummins CJ, Thornton HR, Duthie GM. Importance, Reliability, and Usefulness of Acceleration Measures in Team Sports. J Strength Cond Res. 2018 Dec; 32(12):3485–93.

120

Pons E, García-Calvo T, Cos F, Resta R, Blanco H, López del Campo R, Díaz-García J, Pulido-González JJ. Integrating video tracking and GPS to quantify accelerations and decelerations in elite soccer. Sci Rep. 2021 Dec; 11(1):18531.

121

Ball S, Halaki M, Orr R. Movement Demands of Rugby Sevens in Men and Women: A Systematic Review and Meta-Analysis. J Strength Cond Res. 2019 Dec; 33(12):3475–90.

122

Glassbrook DJ, Doyle TLA, Alderson JA, Fuller JT. The Demands of Professional Rugby League Match-Play: a Metaanalysis. Sports Med – Open. 2019 Dec; 5(1):24.

123

Quarrie KL, Hopkins WG, Anthony MJ, Gill ND. Positional demands of international rugby union: Evaluation of player actions and movements. J Sci Med Sport. 2013 Jul; 16(4):353–9.

124

James CA, Gibson OR, Dhawan A, Stewart CM, Willmott AGB. Volume and Intensity of Locomotor Activity in International Men’s Field Hockey Matches Over a 2-Year Period. Front Sports Act Living. 2021 May 28; 3:653364.

125

Stojanović E, Stojiljković N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanović Z. The Activity Demands and Physiological Responses Encountered During Basketball Match-Play: A Systematic Review. Sports Med Auckl NZ. 2018 Jan; 48(1):111–35.

126

Font R, Karcher C, Reche X, Carmona G, Tremps V, Irurtia A. Monitoring external load in elite male handball players depending on playing positions. Biol Sport. 2021; 38(3):475–81.

127

Manchado C, Pueo B, Chirosa-Rios LJ, Tortosa-Martínez J. Time–Motion Analysis by Playing Positions of Male Handball Players during the European Championship 2020. Int J Environ Res Public Health. 2021 Mar 10; 18(6):2787.

128

Alarifi A, Al-Salman A, Alsaleh M, Alnafessah A, Al-Hadhrami S, Al-Ammar M, Al-Khalifa H. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors. 2016 May 16; 16(5):707.

129

Serpiello FR, Hopkins WG, Barnes S, Tavrou J, Duthie GM, Aughey RJ, Ball K. Validity of an ultra-wideband local positioning system to measure locomotion in indoor sports. J Sports Sci. 2018 Aug 3; 36(15):1727–33.

130

Rico-González M, Oliveira R, Palucci Vieira LH, Pino-Ortega J, Clemente F. Players’ performance during worst-case scenarios in professional soccer matches: a systematic review. Biol Sport. 2022; 39(3):695–713.

131

Romero-Moraleda B, González-García J, Morencos E, Giráldez-Costas V, Moya J, Ramirez-Campillo R. Internal workload in elite female football players during the whole in-season: starters vs non-starters. Biol Sport [Internet]. 2023 [cited 2023 Apr 28]; Available from: https://www.termedia.pl/doi/10.5114/biolsport.2023.124849

132

Cummins C, Charlton G, Paul D, Buxton S, Murphy A. How fast is fast? Defining velocity zones in women’s rugby league. Sci Med Footb. 2022 Apr 21; 1–6.

133

Nyman DLE, Spriet LL. External Training Demands in Women’s Varsity Rugby Union Players Quantified by Wearable Microtechnology With Individualized Speed Thresholds. J Strength Cond Res. 2021 Jun 22;

134

Ibáñez SJ, Gómez-Carmona CD, Mancha-Triguero D. Individualization of Intensity Thresholds on External Workload Demands in Women’s Basketball by K-Means Clustering: Differences Based on the Competitive Level. Sensors. 2022 Jan 1; 22(1).

135

Scott D, Lovell R. Individualisation of speed thresholds does not enhance the dose-response determination in football training. J Sports Sci. 2018 Jul; 36(13):1523–32.

136

Casamichana D, Morencos E, Romero-Moraleda B, Gabbett TJ. The Use of Generic and Individual Speed Thresholds for Assessing the Competitive Demands of Field Hockey. J Sports Sci Med. 2018 Sep; 17(3):366–71.

137

McAuley ABT, Baker J, Kelly AL. Defining “elite” status in sport: from chaos to clarity. Ger J Exerc Sport Res. 2022 Mar 1; 52(1):193–7.

138

Swann C, Moran A, Piggott D. Defining elite athletes: Issues in the study of expert performance in sport psychology. Psychol Sport Exerc. 2015 Jan; 16:3–14.

139

Pauw KD, Roelands B, Cheung SS, de Geus B, Rietjens G, Meeusen R. Guidelines to Classify Subject Groups in Sport-Science Research. Int J Sports Physiol Perform. 2013 Mar; 8(2):111–22.

140

Jacobson BH, Conchola EG, Glass RG, Thompson BJ. Longitudinal Morphological and Performance Profiles for American, NCAA Division I Football Players. J Strength Cond Res. 2013 Sep; 27(9):2347–54.

141

Fitzgerald CF, Jensen RL. A Comparison of the National Football League’s Annual National Football League Combine 1999–2000 to 2015–2016. J Strength Cond Res. 2020 Mar; 34(3):771–81.

142

Varley MC, Fairweather IH, Aughey RJ. Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J Sports Sci. 2012 Jan; 30(2):121–7.

143

Thornton HR, Nelson AR, Delaney JA, Serpiello FR, Duthie GM. Interunit Reliability and Effect of Data-Processing Methods of Global Positioning Systems. Int J Sports Physiol Perform. 2019 Apr 1; 14(4):432–8.

144

Varley MC, Jaspers A, Helsen WF, Malone JJ. Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology. Int J Sports Physiol Perform. 2017 Sep; 12(8):1059–68.

145

Harper DJ, Carling C, Kiely J. High-Intensity Acceleration and Deceleration Demands in Elite Team Sports Competitive Match Play: A Systematic Review and Meta-Analysis of Observational Studies. Sports Med. 2019 Dec; 49(12):1923–47.

146

Hughes M, Evans S, Wells J. Establishing normative profiles in performance analysis. Int J Perform Anal Sport. 2001 Jul; 1(1):1–26.

Copyright: Institute of Sport. This is an Open Access article distributed under the terms of the Creative Commons CC BY License (https://creativecommons.org/licenses/by/4.0/). This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

Match demands of female team sports: a scoping review

María L. Pérez Armendáriz 1 , Konstaninos Spyrou 1, 2, 3 , Pedro E. Alcaraz 1, 3

INTRODUCTION

MATERIALS AND METHODS

Protocol and registration

Study design

Data sources and searches

Eligibility criteria

Study selection

Data extraction

Risk of bias

Methodological quality assessment

RESULTS

Search results

FIG. 1

Risk of bias

TABLE 1

Soccer

TABLE 2

Rugby

TABLE 3

Field hockey

Basketball

TABLE 4

Handball

Futsal

TABLE

DISCUSSION

CONCLUSIONS

Acknowledgments

Competing interests

REFERENCES

María L. Pérez Armendáriz

¹

,

Konstaninos Spyrou

^{1, 2, 3}

,

Pedro E. Alcaraz

^{1, 3}