Training load and match-play demands in basketball based on competition level: A systematic review.

Basketball is a court-based team-sport that requires a broad array of demands (physiological, mechanical, technical, tactical) in training and competition which makes it important for practitioners to understand the stress imposed on the basketball player during practice and match-play. Therefore, the main aim of the present systematic review is to investigate the training and match-play demands of basketball in elite, sub-elite, and youth competition. A search of five electronic databases (PubMed, SportDiscus, Web of Science, SCOPUS, and Cochrane) was conducted until December 20th, 2019. Articles were included if the study: (i) was published in English; (ii) contained internal or external load variables from basketball training and/or competition; and (iii) reported physiological or metabolic demands of competition or practice. Additionally, studies were classified according to the type of study participants into elite (20), sub-elite (9), and youth (6). A total of 35 articles were included in the systematic review. Results indicate that higher-level players seem to be more efficient while moving on-court. When compared to sub-elite and youth, elite players cover less distance at lower average velocities and with lower maximal and average heart rate during competition. However, elite-level players have a greater bandwidth to express higher velocity movements. From the present systematic review, it seems that additional investigation on this topic is warranted before a "clear picture" can be drawn concerning the acceleration and deceleration demands of training and competition. It is necessary to accurately and systematically assess competition demands to provide appropriate training strategies that resemble match-play.

1. Introduction

Basketball is a court-based team sport that requires proficiency in a vast array of physical parameters and motor abilities (i.e., speed, strength, and endurance) to achieve success from both a technical and tactical standpoint [1]. The ability to accelerate, decelerate, change direction, jump, and shuffle are paramount for on-court success, due to the intermittent high-intensity nature of most actions and basketball-specific movements [2,3] as well as the demands of the sporting activity [4,5,6]. Importantly, in competition settings, the aforementioned abilities must be expressed in an efficient and economical manner over the course of four quarters with contributions from both aerobic and anaerobic energy pathways [1]. In this context, the density of game-related activity (determined by specific work-to-rest ratios) is dictated by action intensity and by the moment of the game [7]. This includes medium- to high-intensity actions that last 15 seconds (s) and high- to maximal-intensity actions that last up to 2–5 s [8,9]. It is for this reason that practitioners must have a precise overview of match-play demands as well as the load elicited during training [4,5,2,6,10,3,11,12,13,14,15]. In fact, over the past years, there have been several studies documenting match-play demands in basketball [4,5,2,6,10,3,11,12,13,14,15,16,7,17,18,19,20,21,22,23,24,9,25,26,27,28]. Particularly, a recent review by Stojanovic et al. [29] analyzed the activity demands and physiological responses obtained during basketball competition and found that playing period, playing position, level, geographical location and sex greatly influenced the stress experienced by basketball players. In their article Stojanovic et al. [29] examined heart rate (HR), blood lactate concentration, total distance, and movement patterns of male and female basketball competitions based on time-motion analysis. However, while the study clearly described the competition characteristics, the authors did not present data on the acceleration/deceleration requirements of the game nor did they examine the demands of training versus match-play. It is for these reasons that the current systematic review is justified.

It is important to note that amongst the several methods used to quantify the demands of play, and regarding internal load quantifications, HR [6,3,11,12,14,20] and blood lactate concentration [4,13,14,16,9,30] were the most frequently used. In fact, internal variables such as average and maximal HR can be extracted to quantify loading parameters during match-play [11,12,21,30,26]. Concerning external load, methods such as accelerometry and the use of positional tracking cameras [4,2,13,16,7,17,31] are amongst the most common. Within this framework, total or high-intensity accelerations and decelerations, total distance traveled, and top speed reached were the widely used variables to assign a value to the mechanical load imposed. In addition, time-motion analysis [4,14,18,22,9,26,32] measuring time and frequency of movements such as “standing”; “jogging”; “running”; “sprinting”; and “jumping” during competition can be found in the literature. Despite match-play demands based on time-motion analysis having been found to present a high level of variability according to playing position, skill level and training age [29], no robust evidence exists regarding the use of accelerometry. Therefore, a systematic analysis of both approaches to match demands quantification is warranted. Collectively, a better understanding of this ‘real-time’ feedback can give relevant and useful information concerning normative group standards, as well as relative to the individual athlete. Additionally, having a clear “picture” of both internal and external loading parameters can provide a better insight into global stress that the players deal with during training and competition [2,10,26].

In a related topic, tracking training load in this team-sport may be of extreme importance to ensure that the players are physically prepared for competition demands from a fitness standpoint, in order to avoid acute spikes in load from a fatigue and injury prevention perspective [3,11,7,17] and to provide individualized recovery strategies [33,34]. With this in mind, a copious amount of research has also been focused on investigating and describing basketball training load parameters over recent years [35,36,37,38,39,40,41,21,42,24,43,44]. As previously mentioned for competition, accelerometry is becoming an increasingly popular means of quantifying load during training [36,38,40,21]; however, no conclusive data has been reported throughout the different studies. For this reason, a more in-depth and systematic analysis of the literature is warranted. Regarding internal load, HR and session rate of perceived exertion (sRPE) (i.e., the subjective feedback from the player on a 1–10 scale multiplied by duration of training) have been shown to be a cost-effective way of providing valuable information widely used by coaches and sport scientists [35,37,41]. Remarkably, an important variability has been reported within basketball training loads based on quantification means of training load, position, perceived exertion, skill level, and training age [36,37,38,39,40,41,43,44], once again identifying the need for a systematic review of the published data.

The current state of the literature is not conclusive regarding the typical training load experienced by basketball players of different competition levels given that only match-play demands and physiological responses during competition have been previously described [29]. To our knowledge, no previous investigation has focused on systematically reviewing the literature to identify precise loads during training versus match-play whilst clearly defining different levels of competition. As such, there is an important gap in the available research that does not allow concluding whether basketball training is closely mimicking game demands, hence, adequately preparing players for the stress imposed by competition. Moreover, new technologies that allow quantifying the acceleration/deceleration demands in basketball training and competition have emerged, but no current literature review has addressed this topic. Therefore, the aim of the present systematic review is to analyze the evidence related to the training load and match-play demands of basketball across different levels of competition.

2. Materials and methods

2.1 Study design

The present study is a systematic review focused on training load and match-play demands at different levels of competition in basketball. The review was not registered prior to initiation, was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) statement [45] and did not require Institutional Review Board approval.

2.2 Search strategy

A structured search was carried out in PubMed, PubMed Central, Web of Science, SportDiscus and Cochrane databases, all high quality databases which guarantees strong bibliographic support. The electronic database search for the related articles considered all publications prior to December 20th, 2019. The following key words were used to conduct the search “basketball”, “training load”, “accelerometry”, “load monitoring”, “internal load”, “total distance”, “average distance”, “top speed”, “average speed”, “metabolic”, “heart rate”, “competition demands”, “training demands”, “training”, and “rate of perceived exertion”. In addition, the key word “basketball” was present in each search to ensure that the relevant information was catered to articles involving only this sport. The reference sections of all identified articles were also examined (by applying the “snowball methods” strategy [40]). Once the electronic search was conducted, relevant studies were identified and organized in a systematic fashion.

All titles and abstracts from the search were cross-referenced to identify duplicates and any potential missing studies, and then screened for a subsequent full-text review. The search for published studies was independently performed by two authors (AP and TTF) and disagreements were resolved through discussion.

2.3 Inclusion and exclusion criteria

This review included cross-sectional and longitudinal studies considering healthy, professional or junior, male basketball players. Study participants were categorized into three groups: elite, sub-elite, and youth. The elite basketball group was defined as teams participating in the NBA, NBA G-League, NCAA Division I, Euro League, FIBA International Competition, ACB, Top Divisions in Europe, South America, Australia, and Asia. Sub-elite was defined as professional or semi-professional that did not meet the elite criteria but were over 19 years old. Youth was considered for studies in which the participants were all 19 years of age or younger. Studies were included in the present review if they met the following criteria: (i) the study was published in English; (ii) the study included internal or external load variables from basketball training and/or competition; and (iii) the study reported physiological or metabolic demands of competition or practice.

Studies were excluded if (i) the study participants were wheelchair basketball players; (ii) the study participants were female; (iii) the data being collected did not describe training load or competition demands; and (iv) the study consisted on a review or a conference proceeding.

2.4 Study selection

The initial search was conducted by one researcher (AP). After the removal of duplicates, an intensive review of all of the titles and abstracts obtained were conducted. Following the first screening process, the full-version of the remaining articles was read. Then, on a blind, independent fashion, two reviewers excluded studies not related to the review’s topics and determined the studies for inclusion (AP and TTF), according to the criteria previously established. If no agreement was obtained, a third party intervened and settled the dispute. Moreover, PEDro scale (Fig 1) was used to evaluate whether the selected randomized controlled trials were scientifically sound (9–10 = excellent, 6–8 = good, 4–5 = fair, and <4 = poor) [46]. Papers with poor PEDro score were excluded. Final outcomes of the interventions were extracted independently by two authors (AP and TTF) using a customized spreadsheet (Microsoft Excel 2016, USA). Disagreements were resolved through discussion until a consensus was achieved.

Fig 1

PEDro scale.

3. Search results

As several databases were scrutinized, the initial database search yielded 18,805 citations. After duplicate removal, 3,282 abstracts and titles were left for review. Upon screening, 165 articles met the inclusion criteria for full-text review. Of the 165 articles reviewed, 35 met the criteria for the systematic review. Of the 35 articles that met the criteria, 12 had participants for elite competition demands [4,5,6,11,12,13,14,15,16,7,9,30,32], 16 articles had participants for elite training load [2,10,3,12,15,35,37,38,39,41,20,42,25,27,43,47], 6 for sub-elite competition demands [4,11,13,21,26,32], 3 for sub-elite training load [23,44,48], 5 for youth competition demands [11,18,22,9,28] and 1 for youth training load [24]. A full view of the search and selection process can be found in the PRISMA flow diagram [45] in Fig 2.

Fig 2

PRISMA flow diagram.

4. Competition demands

4.1 Internal competition load

Internal load outcomes pertaining to competition demands can be found in Table 1. The variables displayed in the different studies consisted of HR and blood lactate concentration.

Table 1

Internal load during competition.

Study	Competitions (n=)	Participants (Competition Level)	% of HR Lactate Threshold	Mean HR (beats/min)	Max HR%	Max HR (beats/min)	Blood Lactate Concentrate (mmol/l)
Daniel et al. [6]	n = 6	Brazilian Basketball League (Elite)	Defense- 104.2 ± 2.21 Offense- 103.7 ± 1.80 Defense Transition- 104.8 ± 2.44Offense Transition- 104.3 ± 3.55
Lopez-Laval et al. [11]	n = 3	Spanish ACB League/ABA/Spanish Juniors (Elite/Sub-Elite/Youth)		Elite Adults- 150 ± 11Amateur Adults- 168 ± 9Elite Juniors- 167 ± 10	Elite Adults- 79 ± 4Amateur Adults- 87 ± 3Elite Juniors- 84 ± 4	Elite Adults- 190 ± 2Amateur Adults- 193 ± 4Elite Juniors- 199 ± 3
Abdelkrim et al. [18]	n = 9	Tunisian U-19 National Team (Youth)		All Positions-Q1-173 ± 4Q2-173 ± 5Q3-173 ± 4Q4-167 ± 4Guards-Q1- 176 ± 4Q2- 176 ± 5Q3- 176 ± 4Q4- 167 ± 4Forwards-Q1- 173 ± 5Q2- 173 ± 5Q3- 174 ± 4Q4- 167 ± 4Center-Q1- 171 ± 3Q2- 170 ± 3Q3- 171 ± 4Q4- 165 ± 4	All Positions-91 ± 2		Mean-5.49 ± 1.24 mmol/l
Torres-Ronda et al. [12]	n = 7	Spanish ACB League (Elite)		158 ± 10	96.8 ± 2.6	198 ± 9.3
Abdelkrim et al. [9]	n = 6	Tunisian Junior National Team (Youth)					Mean-5.75 ± 1.25 mmol/LPeak- 6.22 ± 1.34
Abdelkrim et al. [30]	n = 6	Tunisian National Team (Elite)		Q1-176 ± 5Q2-176 ± 4Q3-176 ± 4Q4-172 ± 4
Narazaki et al. [21]	n = 1	NCAA Division II (Sub-Elite)		169.3 ± 4.5			4.2 ± 1.3mmol/L
Puente et al. [26]	n = 1	Spanish Basketball Federation (Sub-Elite)			Guards- 89.6 ± 4.7Forwards-87.8 ± 3.2Centers-92.7 ± 4.7Whole Group-89.8 ± 4.4

Heart Rate (HR) expressed in Beats Per Minute (BPM). Blood Lactate Concentrate express in millimoles per liter mmol/L. Q1 is 1st quarter, Q2 is 2nd quarter, Q3 is 3rd quarter, and Q4 is 4th quarter of match-play.

4.1.1 Heart rate

Heart Rate (HR) during competition (Table 1) was organized into two categories according to the classification used in the included studies: maximal (HRmax) and average HR (HRave). The values of HRmax during elite level competition ranged from 187 to 198 beats per minute (BPM) with a mean of 190 BPM [11,12,30]. With regards to sub-elite competition, values ranged from 192 to 195 BPM with a mean of 194 BPM [11,21,26]. In addition, in youth competition, the HRmax held a mean of 199 BPM [11,18]. The data extracted indicated that elite competitors presented lower HRmax values during competition, which can be interpreted as an indicator of elite players having a higher overall level of fitness and a more efficient work rate compared to sub-elite and youth players [11]. Interestingly, according to the results retrieved from the literature, the same pattern occurred with the HRave. During elite level competition the value ranged from 150 to 175 BPM [11,12,30], in sub-elite competition ranged from 168 to 169 BMP [11,21] and in youth competition the HRave ranged from 167 to 172 BPM [11,18].

4.1.2 Blood lactate concentration

Blood lactate concentration was collected as an internal measurement during select studies of elite level competition. The samples for mean blood lactate post-competition held an average of 5.1 ± 1.3 mmol/L [18,21,9] with a range of 4.2 to 5.7 ± 1.2. Abdelkrim et al. [9] observed a peak of 6.2 ± 1.3 in the fourth quarter for the Tunisian National Team. The fourth quarter peak is likely due to the build-up of blood metabolites and catabolic hormones based on the depletion of muscle glycogen later in competition. The ability to buffer these mechanisms internally may have had a direct impact on mechanical outputs during competition [30] as internal load parameters leading to fatigue have been reported to negatively affect whole-body work rate, physical and technical performance, and even decision making in team-sports [49]. It is for such a reason that there is a need for future investigation of blood metabolite accumulation during competition and the effects it has on high-speed movement.

4.2 External competition load

Table 2 displays the external load variables retrieved from the different studies. Total distance, acceleration (ACC) and deceleration (DEC) efforts during basketball competition, average and top speed reached, and time motion analysis movement frequency and duration were the outcomes extracted.

Table 2

External load during competition.

Study	Competitions (n=)	League (Level)	Average Speed	Max Speed	Total Distance	Accelerations	Decelerations
Sampaio et al. [5]	n = 1230	NBA (Elite)	Speed in offense (m·s)All-Star 1.95 ± 0.16 Non-All-Star 2.01 ± 0.12Speed in defense (m·s) All-Star 1.63 ± 0.07 Non-All-Star 1.72 ± 0.08
Scanlan et al. [13]	n = 5	Australian NBL/Queensland State Basketball League (Elite/Sub-Elite)			ProfessionalQuarter 1–1653 ± 38Quarter 2–1591 ± 24Quarter3-1531 ± 72Quarter 4–1504 ± 21SemiprofessionalQuarter 1–1549 ± 81Quarter 2–1601 ± 88 Quarter3–1501 ± 166Quarter 4–1557 ± 238
Vázquez-Guerrero et al. [27]	n = 2	Spanish ACB League (Elite)				PGs-Acc. (<3 m·s-2) #/min- 29.6 ± 3.9 Acc. (>3 m·s-2) #/min- 1.4 ± .9SGs-Acc. (<3 m·s-2) #/min- 32.7 ± 11 Acc. (>3 m·s-2) #/min- 1 ± .4SFs-Acc. (<3 m·s-2) #/min-26.7 ± 2.6 Acc. (>3 m·s-2) #/min-.8 ± .3PFs-Acc. (<3 m·s-2) #/min- 28 ± 5 Acc. (>3 m·s-2) #/min- 1.4 ± .5Cs-Acc. (<3 m·s-2) #/min- 28.3 ± 1.1 Acc. (>3 m·s-2) #/min- 1.5 ± .4	PGs-Dec. (<-3 m·s-2) #/min- 23.8 ± 3.6Dec. (>-3 m·s-2) #/min- 4.5 ± 1.4SGs-Dec. (<-3 m·s-2) #/min-25.7 ± 10 Dec. (>3 m·s-2) #/min- 4.5 ± 1.4SFs-Dec. (<-3 m·s-2) #/min- 21.7 ± 2.2 Dec. (>-3 m·s-2) #/min- 3.2 ± .7PFs-Dec. (<-3 m·s-2) #/min- 24 ± 4.6 Dec. (>-3 m·s-2) #/min-3.5 ± .7Cs-Dec. (<-3 m·s-2) #/min- 23.4 ± 1.3 Dec. (>-3 m·s-2) #/min- 3.7 ± .8
Svilar et al. [15]	n = 11	Spanish ACB League (Elite)				tACCmin- 2.19 ± 0.84 (2.07–2.31)hACCmin- 0.38 ± 0.25 (0.34–0.42)	tDECmin- 2.38 ± 0.63 (2.28–2.47)hDECmin- 0.25 ± 0.19 (0.22–0.28)
Caparrós et al. [7]	n = 87	NBA (Elite)		Average-8.09 ± 0.44 (m·s)Minimum-6.79 (m·s)Maximum-8.76 (m·s)		Acceleration- .5 (m·s-2)-262.5 ± 97.9 1 (m·s-2)- 90.2 ± 34 2 (m·s-2)- 12.8 ± 34 4 (m·s-2)- 0.7 ± 1.0	Deceleration- -.5 (m·s-2)- 172.7 ± 62.7 -1 (m·s-2)- 112.3 ± 39.1 -2 (m·s-2)- 6.6 ± 3.6 -4 (m·s-2)- 0.3 ± 0.6
Abdelkrim et al. [16]	n = 6	Tunisian National Team (Elite)		Peak Speed- (m·s) PG- 5.2 ± .52 (4.02–5.76) SG4.60 ± 0.42 (4.02–5.29)SF- 4.69 ± 0.63 (4.02–5.76)PF- 4.72 ± 0.61 (4.02–5.76) C- 4.10 ± 0.35 (3.78–4.79)	PG- 2,724 ± 711 (1,120–3,480) SG- 1,907 ± 577 (1,120–2,840) SF- 2,031 ± 867 (1,120–3,480) PF- 2,067 ± 837 (1,120–3,480) C- 1,227 ± 484 (800–2,160)
Puente et al. [26]	n = 1	Spanish Basketball Federation (Sub-Elite)		Max Speed (m·s) Guards- 6.6 ± 0.4 (5.9–7.3)Forwards- Max Speed- 6.2 ± 1.1(5.1–8.5)Center- Max Speed- 5.9 ± 0.4(5.1–6.3)Whole group-Max Speed- 6.2 ± 0.7(5.0–8.5)
Abdelkrim et al. [9]	n = 6	Tunisian National Team (Elite)			Total Distance 7,558 ± 575 (6,338–8,397). 1st half- 3,742 ± 304 2nd Half- 3,816 ± 299 m
Vázquez-Guerrero et al. [28]	n = 13	Euro League U-18 (Youth)		Peak Speed (km·h-1)Guards-Q1- 19.57 ± 0.9Q2- 19.56 ± 1.3Q3- 19.64 ± 0.8Q4- 19.36 ± 1.0Forwards-Q1- 19.35 ± 1.0Q2- 39.34 ± 1.0Q3- 18.92 ± 0.3Q4- 19.15 ± 1.0Center-Q1- 19.16 ± 0.8Q2- 18.82 ± 1.0Q3- 18.75 ± 1.0Q4- 19.07 ± 0.9	Total Distance/Playing DurationGuards-Q1- 80.46 ± 7.5Q2- 73.91 ± 8.9Q3- 76.81 ± 8.4Q4- 70.00 ± 9.8Forwards-Q1- 78.91 ± 10.0Q2- 71.90 ± 9.0Q3- 71.98 ± 11.2Q4- 69.15 ± 13.8Centers-Q1- 73.45 ± 12.9Q2- 69.10 ± 7.9Q3- 68.95 ± 9.4Q4- 64.24 ± 8.5	Acc. > 2 (m·s-1)Guards-Q1- 2.20 ± 0.4Q2- 1.99 ± 0.6Q3- 1.95 ± 0.5Q4- 1.72 ± 0.4Forwards-Q1- 2.04 ± 0.6Q2- 1.83 ± 0.5Q3- 1.72 ± 0.5Q4- 1.66 ± 0.6Centers-Q1- 1.76 ± 0.6Q2- 1.64 ± 0.4Q3- 1.44 ± 0.3Q4- 1.26 ± 0.4	Dec. > -2 (m·s-1)Guards-Q1- 2.04 ± 0.4Q2- 1.79 ± 0.5Q3- 1.82 ± 0.5Q4- 1.52 ± 0.4Forwards-Q1- 1.70 ± 0.5Q2- 1.47 ± 0.5Q3- 1.39 ± 0.5Q4- 1.28 ± 0.5Centers-Q1- 1.25 ± 0.4Q2- 1.20 ± 0.4Q3- 1.04 ± 0.3Q4- 0.99 ± 0.4

(m·s) = meters per second. (km·h) = kilometers per hour PG- Point Guard, SG-Shooting Guard, SF- Small Forward, C- Center. Acc. = accelerations. Dec. = decelerations. tACC = total accelerations. hACC = high-intensity accelerations. tDEC = total decelerations. hDEC = high-intensity decertations. #/min = number per minute. Q1 = 1st Quarter. Q2 = 2nd Quarter. Q3 = 3rd Quarter. Q4 = 4th Quarter.

4.2.1 Total distance

In elite competition, distance traveled ranged from 1,991 to 6,310 m [13,16,9]. The total distance covered during sub-elite competition ranged from 3,722 to 6,208 m [48,13]. Finally, considering youth competition, only one study tracked the distance traveled during competition and reported a value of 7,558 m [9]. Remarkably, there was a discrepancy in distance covered between elite, sub-elite, and youth athletes. Upon review, the elite level basketball athletes covered, on average, less distance (4,369 m) [4,13,16,7], compared to sub-elite (5,377 m) [4,13,48] and youth players (7,558 m) [9]. This seemingly paradoxical finding suggests that the total distance covered may be a poor indicator of in-game performance. In fact, one could infer that the observed phenomenon is a product of technical mastery relative to the demands of competition, as well as elite level players having a higher level of economy in relation to the tactical aspects of basketball [1,5,6]. Based on the present results and as it occurs in other team-sports [50], the key aspect here appears to be not “how much” distance a player covers (i.e., quantity) but “how” and at “what intensity” that distance is covered (i.e., quality). In fact, in support of the previous, Sampaio et al., [5] suggested that better players tend to make fewer mistakes when deciding when and where to run which may result in shorter paths to reach their destination. This is more than likely due to a high degree of technical and tactical discipline based on training age and experience, more hours of professional supervised practices, and higher level of coaching.

4.2.2 Accelerations and decelerations

Accelerometry in basketball is tracked via inertial units containing accelerometer, gyroscope, and magnetometer sensors [15,7,27]. These sensors allowed inertial movement analysis by recording accelerations, decelerations, jumps, and changes of direction (COD). As it can be seen in Table 2, when considering the accelerometry data collected during elite level competition, most research breaks it down into two important categories: accelerations (ACC) and decelerations (DEC) [15,7,27,28]. Additionally, two sub-sections of these categories can be found: total (T), and high-intensity (HI) [15,27]. For the purpose of this review, total accelerations (ACCT) were classified as total forward acceleration, whereas high-intensity accelerations (ACCHI) were classified as the total forward acceleration within the high band (>3.5 m·s-2) [15], and (>3 m·s-2) [27]. Total decelerations (DECT) consisted of the total number of decelerations and high-intensity decelerations (DECHI) were classified as total deceleration within the high band (>-3.5 m·s-2), and (>-3 m·s-2) [27].

During elite level match-play, the ACCT ranged from 43 to 145, and the total number of ACCHI ranged from 1 to 15 per match. Remarkably, a substantial variability can be found within the included studies, considering the ACC values. This occurrence makes it difficult to draw precise conclusions regarding the ACC demands of elite basketball competition. In fact, a similar pattern can be observed for DECT as values ranging from 24 to 95 per match were found. Regarding the total number of DECHI per match, data extracted ranged from 4 to 40. It seems evident that additional investigations on this topic are warranted before a “clear picture” can be drawn concerning the ACC and DEC demands. Moreover, researchers and sports scientists are encouraged to follow a standardized approach to ACC and DEC quantifications (e.g., determining the same HI bands) so that comparisons between studies and data sets can be conducted. None of the sub-elite or youth teams in the included studies collected accelerometry data during competition.

4.2.3 Average and top speed

Studies evaluating NBA competition [5,7] recorded average speed in miles per hour (mph), but values were converted by the authors to the global unit measurement of meters per second (m·s-1). The speed recorded by using spatial tracking cameras (Sport VU®; Chicago, USA) can be seen in Table 2. Sport VU® cameras were installed in all 30 NBA arenas from the 2012–2013 season until the 2016–2017 season and McLean et al. [51] collected data from the entire 82 games plus the playoffs. This technology uses computer vision systems designed with algorithms to measure player positions at a sampling rate of 25 frames per second [5]. Top speed was also measured by Puente [26] via SPI PRO X (GPSports®, Australia) and Abdelkrim et al. [16], as well as Vázquez-Guerrero et al.[28] via WIMU PRO Local Positioning System (Realtrack System, Almeria, Spain).

Similar to accelerometry data, positional tracking cameras have only been used to track match demands in elite level basketball, most likely due to the financial limitations on the sub-elite and youth levels. Importantly, when examining normative data points related to movements associated with basketball, it seems that the best performers on an elite level expressed certain performance characteristics. For example, Sampaio et al. [5], when examining All-Star Players versus Non-All-Star players in the NBA, found that there was a significant difference in average speed on both the offensive and defensive ends of the court. All-Star players had an average speed of 4.38 ± 0.36 mph (2.0 ± 0.2 m·s-1) offensively and 3.65 ± 0.16 mph (1.6 ± 0.1 m·s-1) defensively, whereas Non-All-Star players had an average speed of 4.50 ± 0.28 mph (2.0 ± 0.1 m·s-1) offensively and 3.86 ± 0.20 mph (1.7±0.1 m·s-1) defensively. Within the most prestigious level of basketball, the evidence suggests that the most efficient players tend to exert the least amount of energy to achieve the most productive results [5,7]. With regards to top speed, there was also variability among levels. Puente et al. [26] showed that the average top speed in sub-elite Spanish basketball competition was 6.2 m·s-1, which is lower than the 8.09 m·s-1 average top speed by NBA players identified in the work of Caparrós et al. [7]. However, the former study [26] only analyzed one single sub-elite game and, therefore, caution is warranted when directly comparing the results. For this reason, future research is needed in this area. Taken together, the distance and speed data extracted from the literature hint that higher level basketball players seem to cover less distance but achieve greater top speeds during competition, which is in line with what has been reported in other team sports [52,50].

4.2.4 Time motion analysis

Time motion analysis has been widely used to track frequency and duration of movements during competition [4,18,26,22,14,9,32]. Movements such as stand/walk, jog, run, sprint, and jump are commonly recorded among different levels of competition as well as different positions. Within this research, and based on the published literature, stand/walk was defined as movements performed at a velocity of 0–1 m·s-1 [1,14,18,22,32] and jogging was defined as intensities greater than walking but without urgency performed at 1.1–3.0 m·s-1 [4,18,26,9]. Running was defined as sagittal plane movement at a greater intensity than jogging and with a moderate degree of urgency at 3.1–7.0 m·s-1 [18,22,33]. Finally, sprinting was defined as forward movements characterized as effort close to maximum >7.0 m·s-1 [4,14,18,9,26,32].

Ferioli et al. [32] and Scanlan et al. [4] examined time motion analysis among elite and sub-elite populations. Upon review, Ferioli et al. [32] found that there was a stark difference between time spent and frequency in high-speed running and sprinting versus jogging in the first division compared to the second division. The 1st Italian Division had frequency of exposures to high-intensity actions (HIA) of 107 ± 26, compared to an average of 78 ± 35 HIA in the second division. Scanlan et al. [4] found that elite backcourt (EBC) and elite frontcourt (EFC) had a much higher frequency of running compared to sub-elite backcourt (SEBC) and sub-elite front court (SEFC) during match-play. EBC had a mean frequency of 504 ± 38 and EFC had a mean frequency of 513 ± 26 of exposures to running during competition. These figures for running during competition are much higher than the SEBC (321 ± 75) and SEFC (352 ± 25), respectively. Again, these results would suggest that top-level basketball players spend more time at high-intensity activities compared to their sub-elite counterparts. In addition, elite players tend to display greater control over the most appropriate time and situations to express high-intensity actions relative to the total distance covered whilst on the court.

Abdelkrim et al. [18] and Puente et al. [26] examined the positional differences using time motion variables during competition. Both studies showed that guards spend more time running compared to forwards and centers. Abdelkrim et al. [18] found that guards had a greater frequency of running during competition (103 ± 11), compared to forwards (88 ± 5) and centers (101 ± 19). Puente et al. [26] found that guards run a longer distance of 3.1 ± 1.1 (m.min-1) compared to forwards (2.2 ± 1.9) and centers (1.6 ± 1.6). This information, seen in Table 3, is useful and may have important implications when prescribing high-intensity running relative to each position in basketball. Based on these results, individual conditioning programs should be adapted to the specific physical requirements of guards, forwards, and centers, keeping in mind that the latter have been found to have a lower proportion of high-intensity running, acceleration, decelerations, and COD.

Table 3

Frequency, duration, and distance of time-motion analysis during competition.

Study	Participants (Competition Level)n = # of comp.	Stand/Walk	Jog	Run	Sprint	Jump	All Movements
Scanlan et al. [4]	Australian NBL/ Queensland State Basketball League (Elite/Sub-Elite) n = 5	Mean Frequency—EBC- 764 ± 86SEBC- 462 ± 74EFC- 815 ± 45SEFC- 532 ± 38Duration—mean/totalEBC- 0.91± 0.09/691 ±35SEBC- 2.13 ± 0.11/981 ± 81EFC-1.02 ± 0.10/829 ± 8SEFC- 2.16 ± 0.07 /1150 ± 68Distance—EBC- 0.48 ± .06/363 ± 4SEBC- 1.08 ±. 07/495 ± 28EFC- 0.54 ± .06/435 ± 23SEFC- 1.10 ± .05/586 ± 45	Mean Frequency—EBC- 911 ± 65SEBC- 586 ± 77EFC- 955 ± 33SEFC- 664 ± 59Duration–mean/totalEBC- 1.27 ± 0.07/1153 ± 6SEBC- 1.66 ± .18/961 ± 45EFC- 1.25 ± .05/1192 ± 24SEFC- 1.57 ± .07/1039 ± 53Distance—EBC- 2.36 ± .09/2142 ± 70SEBC- 2.97 ± .32/1723 ± 87EFC- 2.31 ± .06/2208 ± 15SEFC- 2.73 ± .13/1804 ± 89	Mean Frequency—EBC- 504 ± 38SEBC- 321 ± 75EFC- 513 ± 26SEFC- 352 ± 25Duration—mean/totalEBC- 1.34 ± .10/673 ± 9SEBC- 1.38 ± .16/436 ± 60EFC- 1.43 ± .09/730 ± 3SEFC- 1.33 ± .03/467 ± 11Distance—EBC- 5.67 ± .46/2845 ± 16SEBC- 6.11 ± .67/1926 ± 268EFC- 6.11 ± .42/3125 ± 57SEFC- 6.02 ± 0.64/2112 ± 73	Mean Frequency—EBC- 18 ± 7SEBC- 105 ± 31EFC- 24 ± 1SEFC- 140 ± 14Duration–mean/totalEBC- 0.51 ± .01/9 ± 1SEBC-0.93 ± .03/97 ± 29 EFC-0.51 ± .03/12 ± 3SEFC-0.98 ± .02/136 ± 15Distance—EBC- 3.85 ± .01/70 ± 26SEBC- 9.08 ± .38/952 ± 321EFC- 3.92 ± .25/94 ± 9SEFC- 9.48 ± .72/1329 ± 235		Mean Frequency—EBC- 2733 ± 142 SEBC-1911 ± 283 EFC- 2749 ± 137 SEFC- 2014 ± 131
Abdelkrim et al. [18]	Tunisian U-19 National Team (Youth) n = 6	Frequency-All Positions- 129 ± 10Guards-130 ± 8Forwards-126 ± 15Centers- 130 ± 8Duration- (s)All Players- 2.4 ± 0.3Guards- 2.3 ± 0.2Forwards- 2.4 ± 0.3Centers- 2.6 ± 0.1	Frequency-All Positions- 113 ± 8Guards-113 ± 8Forwards-110 ± 10Centers- 117 ± 6Duration-(s)All Players-2.2 ± 0.2Guards- 2.1 ± 0.1Forwards-2.2 ± 0.2Centers-2.3 ± 0.1	Frequency-All Positions-97 ± 14Guards-103 ± 11Forwards-88 ± 5Centers- 101 ± 19Duration-(s)All Players-2.3 ± 0.3Guards- 2.1 ± 0.4Forwards-2.4 ± 0.2Centers-2.4 ± 0.4	Frequency-All Positions- 55 ± 11Guards-67 ± 5Forwards-56 ± 5Centers- 43 ± 4Duration-(s)All Players-2.1 ± 0.2Guards- 1.9 ± 0.2Forwards-2.1 ± 0.1Centers-2.2 ± 0.1	Frequency-All Positions-44 ± 7Guards-41 ± 7Forwards-41 ± 6Centers- 49 ± 3	Frequency-All Positions-1050 ± 51Guards-1103 ± 32Forwards-1022 ± 45Centers- 1026 ± 27
Puente et al. [26]	Spanish Basketball Federation (Sub-Elite) n = 1	Distance- (m*min)All Players- 36.4 ± 3.7Guards-37.7 ± 2.9Forwards-37.2 ± 4.6Centers- 34.6 ± .6	Distance- (m*min)All Players- 30.9 ± 5.9Guards- 31.5 ± 6.9Forwards- 32.0 ± 5.3Centers- 29.5 ± 5.8	Distance- (m*min)All Players- 2.3 ± 1.6Guards- 3.1 ± 1.1Forwards- 2.2 ± 1.9Centers- 1.6 ± 1.6	Distance- (m*min)All Players- 0.2 ± 0.7Guards- 0.1 ± 0.2Forwards- 0.5 ± 1.3Centers- 0.0 ± 0.0		Distance-(m*min)All Players- 82.6 ± 7.8Guards-85.3 ± 7.3Forwards-86.8 ± 6.2Centers- 76.6 ± 6.0
Klusemann et al. [22]	Elite Australian Juniors (Youth) n = 13	Frequency-Season- 255 ± 32Tournament- 252 ± 34	Frequency-Season-102 ± 23Tournament- 99 ± 28	Frequency-Season- 90 ± 17Tournament- 82 ± 15	Frequency-Season- 33 ± 7Tournament- 28 ± 8		Frequency-Season-809 ± 80Tournament-758 ± 106
McInnes et al. [14]	Australian NBL (Elite) n = 15	Frequency-295 ± 54Duration- 2.5 ± .5	Frequency- 99 ± 36Duration- 2.5 ± 4	Frequency- 107 ± 27Duration- 2.3 ± 4	Frequency- 105 ± 52Duration- 1.7 ± .2	Frequency-46 ± 12Duration- .9 ± .1	Frequency-997 ± 183
Abdelkrim et al. [9]	Tunisian National Team (Elite) n = 6	Distance- (meters)1720 ± 143	Distance- (meters)1870 ± 322		Distance-(meters)763 ± 169		Distance (meters)-7558 ± 575
Ferioli et al. [32]	Italian 1st/2nd Division (Elite/Sub-Elite) n = 20	RECFrequency- (n)Division I- 184 ± 57Division II- 184 ± 52Duration- (s)Division I- 1599 ± 468Divisin II- 1757 ± 502	LIAFrequency—(n)Division I-306 ± 92Division II- 296 ± 77Duration- (s)Division I- 698 ± 213Division II- 748 ± 200	MIAFrequency- (n)Division I- 106 ± 31Division II- 82 ± 34Duration- (s)Division I- 184 ± 53Division II- 143 ± 62	HIAFrequency- (n)Division I- 107 ± 26Division II- 78 ± 35Duration- (s)Division I- 164 ± 48Divison II- 116 ± 69

EBC = elite back-court. EFC = elite front-court. SEBC = sub-elite back-court. SEFC = sub-elite front-court. REC = recovery. LIA = low-intensity activity. MIA = medium-intensity activity. HIA = high-intensity activity. m*min = meters per minute.

5. Training demands

5.1 Internal training demands

Internal Training Load, displayed in Table 4, considered the following variables: s-RPE, Weekly Training Load, HRmax, HRave, % HRmax, and Training Impulse (TRIMP).

Table 4

Internal training load.

Study	Training Sessions (n=)	Participants (Competition Level)	s-RPE	Weekly TL (AU)	HR Max (BPM)	HR Average (BPM)	Max HR%	TRIMP (AU)
Svilar et al. [2]	n = 12	Spanish ACB League (Elite)	390.2±135.6
Svilar et al. [10]	n = 12	Spanish ACB League (Elite)	Guards- 402.9 ± 151.8 Forwards- 385.5 ± 137.3 Centers- 385.1± 121.6
Ramos-Campo et al. [3]	n = 24	Spanish ACB League (Elite)			187.3 ± 10.9
Torres-Ronda et al. [12]	n = 15	Spanish ACB League (Elite)			5v5- 172 ± 19 4v4- 176 ± 18 3v3- 177 ± 12 2v2- 174 ± 14	5v5- 144 ± 17 4v4- 142 ± 15 3v3- 142 ± 15 2v2- 141 ± 15	5v5- 83 ± 9 4v4- 85 ± 7 3v3- 86 ± 5 2v2- 84 ± 5
Angyan et al. [25]	n = 7	Hungarian Pro League (Elite)			169 ± 5.3
Conte et al. [35]	n = 41	NCAA Division I (Elite)		Starters- 1666.2 ± 148.6 Bench- 1505.5 ± 220.8 1-game week- 1647.7 ± 251.3.2-game week- 1423.2 ± 163.1
Manzi et al. [37]	n = 200	Italian 1st Division (Elite)		No Game- 3334 1 Game- 2928 2 Games- 2791
Heishman et al. [38]	n = 16	NCAA Division I (Elite)						High PL- 135.1±35.9 Low PL- 65.6±20.0 High Readiness- 85.3±19.6 Low Readiness- 104.4±20.1Pre- 100.3±8.6 Post- 81.9±11
Aoki et al. [39]	n = 45	National Brazilian League (Elite)	Preseason- 442.9 ± 89.2 In-Season- 377.1 ± 68.3					Preseason- 27.1 ± 2.1 In-Season- 21.5 ± 1.6
Ferioli et al. [41]	n = 360	Italian 1st Division/semiprofessional (Elite/Sub-Elite)		Pro- 5058 ± 1849 Simi-Pro- 2373 ± 488
Gocentas et al. [23]	n = 42	Semiprofessional (Sub-Elite)			Guards- 194 ±14Post- 190 ± 12.7
Chatzinikolaoet al. [20]	n = 2	Greek League (Elite)			195 ± 6
Scanlan et al. [44]	n = 44	Australian State Level (Sub-Elite)	47.0 ± 15.765.0 ± 17.865.0 ± 24.274.0 ± 22.7					31.6 ± 5.030.3 ± 6.428.8 ± 4.929.9 ± 5.4
Vaquera et al. [24]	n = 26	U-18 Spanish Juniors (Youth)					5v5 condition (91.2 ± 4.7%. HRmax)Max HR 2v2 92.7 ± 3.3%

s-RPE = session rate of perceived exertion. (AU) = arbitrary units. 5v5 = 5 players versus 5 players. 4v4 = 4 players versus 4 players. 3v3 = 3 players versus 3 players. 2v2 = 2 players versus 2 players.

5.1.1 Heart rate

Heart rate in training was used to quantify the cardiovascular demands imposed on the athletes [3,12,35,20,23,24]. Torres-Ronda et al. [12] examined HRmax, HRave, and %HRmax in 5vs5, 4vs4, 3vs3, 2vs2, and 1vs1 games and found the 1vs1 situations had elicited the largest physiological response. Gocentas et al. [23] compared the HRmax between guards and forwards in different training sessions and found that on average guards had a higher HR response (194 ± 14) than forwards (190 ± 12.7). More investigation is needed in the future as it relates to the HR demands of varying training programs.

5.1.2 Session RPE and total weekly training load

A fairly common strategy to monitor players’ load is to track the total weekly load via the sRPE (RPE multiplied by session duration), collected throughout the training week. In basketball, this method has been widely used to assess Training Load [35, 37, 41] and has been shown to provide good insight on the energy cost of different movement patterns, particularly when coupled with external load data [2,10,39]. Briefly, it involves players reporting their RPE score using the Borg 10-point scale thirty minutes after the completion of each training session, multiplying the value by the number of minutes of the session [41] and then calculating the sum of the values of each training session during the week.

As noted in Table 4, the Total Weekly Training Loads in the studies analyzed ranged from 2255 to 5058 AU in elite level teams [35,37,41]. The large range observed is likely due to the high variability on the number of training sessions or practice duration based on the loads provided by the technical staff. Since sRPE is obtained by multiplying RPE by session duration, the accumulative amount of weekly training load is dependent on the duration of each training session, which can vary based on style of play, level of competition, or moment of the season [36,42,44]. In addition, Svilar et al. [2] found that sRPE showed a very strong correlation with DECT and CODT. According to the authors, the rapid eccentric actions involved in decelerations, cuts, and COD may explain the abovementioned relationship [1,2]. Nevertheless, the mechanical stress imposed on the athletes during these movements, as well as the effects of eccentric training in basketball athletes, are areas that need additional investigation in upcoming studies. A key aspect to consider when utilizing this method to monitor training loads and demands is that in the examination of coach and player perception of recovery and exertion, research has shown that coaches tend to overestimate recovery when compared to the athletes’ perception [17]. Therefore, when designing appropriate training sessions, a combination of internal and external load variables is recommended [2,10,39].

5.2 External training load

Regarding External Training Load (Table 5), the variables retrieved from the studies were the number of ACC, DEC, and COD, tracked with inertial units through accelerometry.

Table 5

External training load.

Study	Training Sessions (n=)	Participants (Competition Level)	Acceleration	Deceleration	COD
Svilar et al. [2]	n = 300	Spanish ACB League (Elite)	tACC- 49.1 ± 24.2hACC- 6.5 ± 4.6	tDEC-89.1 ± 32.2hDEC-10.2 ± 6.8	tCOD- 324.1 ± 116hCOD- 21.4 ± 12.5
Svilar et al. [10]	n = 208	Spanish ACB League (Elite)	tACC-Guards- 43.5 ± 17.5Forwards- 42 ± 21.5Centers- 59.5 ± 27.1hACC-Guards- 6.4 ± 4.4Forwards- 5.8 ± 4.3Centers- 7.2 ± 4.8	tDEC-Guards- 84.7 ± 30.1Forwards- 93.2 ± 35.4Centers- 88.5 ± 30.3hACC-Guards- 11.9 ± 57Forwards- 12.7 ± 8.3Centers- 6.8 ± 4.0	tCOD-Guards- 324.8 ± 110.2Forwards- 336.8 ± 121.4Centers- 312.1 ± 114.8hCOD-Guards- 23.5 ±1 2.5Forwards- 24.7 ±1 4.5Centers- 16.8 ± 8.6
Svilar et al. [15]	n = 16	Spanish ACB League (Elite)	tACCminRSG- 1.92 ± 0.97 (1.78–2.06)NSG- 2.20 ± 0.76 (1.88–2.52)hACCminRSG- 0.33 ± 0.26 (0.29–0.37).NSG- 0.25 ± 0.20 (0.17–0.34)	tDECminRSG- 2.40 ± 1.08 (2.24–2.55)NSG- 2.95 ± 0.88 (2.58–3.23)hDECminRSG- 0.24 ± 0.22 (0.21–0.28)NSG- 0.36 ± 0.27 (0.25–0.48)	tCODminRSG- 10.61 ± 4.40 (9.97–11.25)NSG- 13.25 ± 3.69 (11.70–14.81)hCODmin RSG- 0.73 ± 0.46 (0.66–0.80)NSG- 0.95 ± 0.58 (0.71–1.20)
Vazquez-Guerrero et al. [43]	n = 33	Spanish ACB League (Elite)	Accelerations(counts)-1/2 court- 18.0 ± 2.4 (16.6–19.4)1/2 court w/transition- 18.3 ± 2.8 (16.7–19.8)Full court- 16.9 ± 0.4 (16.2–17.6)hACC (counts)-1/2 court- 1.4 ± 0.3 (1.2–1.6)1/2 court w/transition- 1.6 ± 0.2 (1.5–1.7)Full court- 1.9 ± 0.4 (1.3–2.6)Peak Speed (m·s)-1/2 court- 4.2 ± 0.2 (4.0–4.3)1/2 court w/transition- 5.5 ± 0.3 (5.3–5.7)Full court- 5.0 + 0.3 (4.5–5.5)	Decelerations (counts)-1/2 court- 17.6 ± 2.2 (16.3–18.9)1/2 court w/transition- 17.9 ± 2.6 (16.4–19.3)Full court- 16.4 ± 0.5 (15.6–17.2)hDEC (counts)-1/2 court- 1.1 ± 0.3 (1.0–1.3)1/2 court w/transition- 1.4 ± 0.2 (1.3–1.5)Full court- 1. ± 0.3 (1.1–2.1)
Aoki et al. [39]	n = 10	National Brazilian League (Elite)	Peak Acceleration (m·s-2)-Preseason- 2.2 ± 0.2In-Season- 2.4 ± 0.2
Scanlan et al. [44]	n = 10	Australian State League (Sub-Elite)	Mean sprint speed (m·s)3.77 ± 0.383.59 ± 0.293.62 ± 0.233.58 ± 0.30
Schelling et al. [47]	n = 16	Spanish ACB League (Elite)	2v2 = 14.6 ± 2.83v3 = 18.7 ± 4.14v4 = 13.8 ± 2.55v5 = 17.9 ± 4.6

hACC = high-intensity acceleration. hDEC = high-intensity deceleration. tACC = total acceleration. tDEC = total deceleration. tCOD = total change of directions. hCOD = high-intensity change of directions. RSG- regular stoppage games. NSG- non-stoppage games.

5.2.1 Accelerations and decelerations

In elite level basketball, ACCT in training varied from 16.9 to 59.5 [2,10,15,26,47]. The ACCHI in elite training, classified as the total forward acceleration within the high band (>3.5 m·s-2), ranged from 1.9 to 7.2 with a mean of 5.56 per training session. The DECT in elite basketball training ranged from 16.4 to 93.2 with a mean of 64.6 per training session whereas the DECHI (n), which were classified as the total number of decelerations within the high band (>-3.5 m·s-2), ranged from 1.6 to 12. When interpreting this data, it is important to acknowledge that ACCT and DECT are qualified measures to quantify training volume, whereas ACCHI and DECHI are quality measures of training intensity [2,10,15,43].

Remarkably, the number of ACCT, ACCHI, DECT, and DECHI reported during training were considerably lower than the data found in competition settings [15,7,27]. The total volume of ACC in competition was 81 per match on average, as opposed to a mean of 38 accelerations per training session [36,40,43,47]. The total number of ACCHI was moderately less in training (5.6) opposed to (7.3) during match-play. This was also the case with DEC. DECT in competition was 73.1 and the DECHI 16.4, which is slightly greater than the 64.6 (DECT) and 7.4 (DECHI) in elite level training. The present data supports the notion that training, and match demands seem to be considerably different, at least considering the number of ACC and DEC [15]. Matching the volume and intensity of competition via training is important during certain times of the preparatory and competitive season to adequately prepare the athletes for competition. As a consequence, the data reported herein may be extremely pertinent for practitioners in regard to training reflecting the demands of match-playing, as well as modulating training load based on outputs of these variables during competition. In this context, to try and achieve similar or even greater ACC demands in training with respect to match-play, manipulating constraints such as the number of players, the duration of drills or court dimension may be a potential strategy [12,15,47]. Within this framework, Schelling and Torres [47] found that ACC load in 3vs3 and 5vs5 full court scrimmage drills was greater than 2vs2 and 4vs4 full court scrimmage drills, indeed suggesting that manipulating training variables may greatly affect the total load imposed to the players.

A study by Svilar et al. [10] reported interpositional differences in training load accelerometry data among guards, forwards, and centers. Interestingly, the authors examined load parameters according to positional on-court roles and found that centers had a higher volume of ACCT (59.5 ± 27.1) and ACCHI (7.2 ± 4.8) opposed to forwards (42 ± 21.5, 5.8 ± 4.3, respectively) and guards (43.5 ± 17.5, 6.4 ± 4.4, respectively). Also, noteworthy, forwards were shown to have a high volume of DECT (93.2 ± 35.0) and DECHI (12.7 ± 8.3) compared to guards (84.7 ± 30.1, 11.9 ± 5.7) and centers (88.5 ± 30.3, 6.8 ± 4). It appears that the profiles of activity are quite different amongst positions and further research is necessary to better understand each individual profile. Still, the amount of exposures to cuts, COD, or screening actions, as well as the typical movement area of each positional role may conceivably explain such findings [6,10,12,16,27,53].

Despite the aforementioned, one must consider the limitations of accelerometry when measuring external load. Even though such technology is extremely useful, accelerometers fail to measure the metabolic demands of isometric muscle contractions during player-on-player contact due to the low velocity outputs. While these actions have very low acceleration, they potentially have very high energy demands [1,19,54]. Therefore, the physical cost of player-on-player contact loading is a component of basketball that must be examined more thoroughly in future research to more accurately quantify training and competition load.

6. Limitations

Some limitations should be addressed when considering the present research on training load and competition demands among different levels of basketball. Firstly, several elite leagues (e.g., NBA or ACB) do not allow for wearable technology to be used during competition which creates a gap in the literature as far as linking demands placed on the players during elite competition and how that compares to training. Secondly, when trying to investigate these variables, most sub-elite and youth teams do not have the financial means to invest in equipment to accurately quantify load during training. Finally, the limited number and sample size of youth and sub-elite studies made it difficult to conclude the precise demands of training and competition at these levels. As such, more resources need to be invested in these areas.

7. Conclusion

Basketball is a highly competitive team-sport that requires a cascade and flow of various movement patterns relative to the technical and tactical aspects of the sport. Examining the internal and external loads imposed on the players from both training and competition provides context for the practitioner to create an optimal training environment. Having the knowledge of the stress demands on the player during competition will help to dictate the volume and dosage of load for desirable adaptations in the player’s training regimen. From the results of the present systematic review, it appears that higher-level players seem to be more efficient while moving on-court. Elite level players cover less distance, at lower average velocities, and with lower HRmax and HRave during competition. However, they seem to have greater capacities to move at higher speed. This is likely due to a heightened sense of awareness based on the schematics of the game. Such information may provide insight into personalized testing protocols as well as training recovery strategies based on each player’s response and considering mechanical and physiological loading parameters relative to competition level. Examining this holistic approach creates an ideal training environment that facilitates both technical and tactical development as it relates to the game of basketball. Future research must be dedicated to this area to provide more precise insight into the physical and interpositional demands of the sport. It is necessary to accurately and systematically assess competition demands to help determine valid training strategies that resemble match-play, considering training age, physical characteristics, and in-game role of guards, forwards, and centers. Reviewing these principals will allow priming and preparing basketball players for the rigorous of match-play demands.

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17 Dec 2019

PONE-D-19-30568

Training Load and Match-Play Demands in Basketball based on Competition Level

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5. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 3 in your text; if accepted, production will need this reference to link the reader to the Table.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: - English grammar and spellings must be revised before a resubmission of the manuscript due to the fact that too many typos appear through the manuscript. In this sense the reviewer highlighted some of them, but I did not make an extensive review of typos.

- P2, L50, please add into brackets the specific demands (e.g., physical, technical…).

- P2, l52, please replace the term athlete with player through the text.

- P2, L54, please add after competition the different levels analysed (i.e., elite…).

- P2, L54, please write Web of Science instead of WoS.

- P2, l55-57, please replace the numbers (1), (2) and (3) with (i), (ii), (iii).

- P2, L57, please add “and” before (3).

- P2, L58, please clarify the type of study participants: …study participants into elite (xxxxxxx), sub-elite (xxxxxxx), and youth (xxxxxxxxx).

- P2, L59 and L63, add the word systematic before review.

- P2, L60, please write players instead of athlete.

- P2, L61, please write players instead of athlete.

- P2, L61, please write a full stop after athletes and start a new sentence.

- P2, L61, please delete the comma after distance.

- P2, L62, please add “than the other level players” after competition.

- P2, L66, please replace “help determine” with “provide”.

- P3, L82, please delete “of two halves, or”.

- P3, L84, please write “game-related”.

- P3, L91, what does “competition alone” mean? Please clarify the sentence and meaning of Stojanovic´s article.

- Please explain in depth the Stojanovic´s article. This reference detailed some relevant factors that affect the training load in basketball: playing period, playing position, level, geographical location and sex. In fact, the authors should justify why the current systematic review is needed, mainly when the Stojanovic´s one was published in 2017 and only a few articles were published after that date.

- P3, L92, please explain the stress variables identified in the Stojanovic´s review.

- P3, L93, please replace “Of note” with “It is important to note”.

- P3, L95, replace are with were.

- P3, L95, please write fact instead of effect.

- P4, L99-100, please write “…were the variables widely used to assign”.

- P4, L104, please replace conclusive with concluding or robust.

- P4, L106, please write “a better understanding of this…”

- P4, L109, please write …”stress that the players deal with during trainings and competitions”.

- P4, L111, please write “team sport” instead of “sport”.

- P4, L111, please write “…may be of extreme importance to ensure that players are…”.

- P4, L112, please write “…prepared for competition demands, from a fitness standpoint, in order to avoid…”.

- P4, L115, please replace dedicated to with focused on.

- P5, L121, player instead of athlete.

- P5, L123, write loads instead of load.

- P5, L125-126, please rewrite the sentence.

- P5, L127-128. The rationale of the current systematic review should be better presented and justified (as was pointed out above) highlighting the novelty of studying the different level of competition, and what does the current review add to the previous one of Stojanovic et al (2017).

- P5, L132-135, please split it into 2 sentences.

- P5, L132, please write “in the available research”.

- P5, L134-135, please write “Therefore, the aim of the present systematic review was to analyse the evidence related to…”

- P5, L140, please write focused instead of focusing.

- P6, L150, the authors should redo the search due to the fact that the first search was around 8 months ago, and not too many studies were found for sub-elite and youth levels.

- P7, L170, please write 19 years old.

- P7, L172-178, please replace the numbers (1), (2) and (3) with (i), (ii), (iii), (iv).

- P7, L174 and L178, please write and before (3) and (4), respectively.

- P7, L178, please write “a conference proceeding”.

- Quality of figures 1 and 2 must be improved.

- P8, L199, please add “the” before systematic.

- Tables 1 to 5 should include the n of matches and individual observations for each study.

- The units and symbols included in tables 1 to 5 should follow the same scale/unit of measure (m*s). In addition, the description of variables should include in all the studies mean and SD.

- Tables 1 to 5, please unify the names of the leagues to use the correct terminology. For example Spanish ACB league instead of Spanish ACB or what does Spanish Basketball Federation mean? Authors should write clearly each league name or sample used in order to avoid misunderstandings of readers and researchers.

- Results, the authors must unify the way to present the results. I suggest that mean and sd will be provided for all variables. However, the authors did this approach for some variables, but not for all.

- I suggest that the CV% would be included by authors in the analysis of each article to check the variability of their findings. This information will be useful to discuss the importance (stability) of variability among levels and then justify the large differences in some variables.

- P11, L255, please write 4,369.

- P11, L255, please write 3,722 and 6,208.

- P11, L255, please write 5,377.

- P11, L255, please write 7,558.

- P11, L255, please write 4,369.

- P11, L255, please write 5,377 and 7,558.

- P11, L257, please write In particular instead of “Of note”.

- P11, L261-262, please delete “supporting that higher-level players travel the least amount of distance during competition”.

- P11, L262-270. The authors should explain and argue some relevant factors and variables that can justify the level differences such as experience, tactical discipline, hours of training, more professional supervised practice or the control of competition.

- P12, L289, please write In fact instead Of note.

- P13, L319, garner???

- P14, L322, please replace “demonstrated” with identified or found.

- The study of Puente et al (39) should be carefully discussed and argued during results and discussion because only 1 match was studied. Then, some of the authors’ arguments need to be revised to state the limitations of sample size of some studies included in the current review.

- P16, L342, please write correctly the units of running.

- P16, L343, please write sprinting with lower case letters.

- P16, L346, please add that before there was.

- P16, L354, please write these results would instead of this would.

- P16, L355, please delete “at the”.

- P16, L 355, please replace athletes with players.

- P16, L356, please add a full stop after counterparts and clarify the last sentence.

- P16, L359, please clarify that the studies analysed time motion variables or indicators, but not time motion analysis.

- P18, L381-383, please write vs or on when explaining the game situations (e.g., 3 on 3 or 3vs3, but not 3v3).

- P18, L383, please replace games with situations.

- P21, L432, please write correctly the units of measure after >3.5.

- P22, L453-454, please write vs or on for each game situation (e.g., 3 on 3 or 3vs3, but not 3v3).

- P22, L455, please replace athletes with players.

- P22, L461-462, please justify with scientific and valid arguments the training load differences by playing position. The game play actions raised were neither studied nor real facts of the actual game play of basketball.

- P22, L468-475, please discuss these issues with valid and scientific references that support yours statements.

- Limitations, the authors should include more limitations of the current systematic review such as sample size of some studies (Puente et al) or the reduced number of studies for youth players.

- P24, L496, please replace athletes with players.

- Conclusion, L505, please add positional aspects of the sport.

- Conclusion, replace athlete with player L506, L508, L509, L510, L515, L521, and L522.

- P24, L 508, please add to before dictate.

- P24, L509, please add systematic before review.

- P25, L516, wholistic???

- Reference list should be revised in depth according to the journal guidelines: (i) journal title in italics and short title (refs 1-20 and 22-53); (ii) incomplete references without number of pages, article number of journal vol/num (refs 3 and 48); or (iii) wrong journal title ref 45 (Sports medicine instead of Springer) or ref 21.

**********

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Reviewer #1: Yes: Miguel A Gómez

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

21 Jan 2020

Dear Reviewer,

We really appreciate the time you devoted to reading our manuscript and helping us craft an improved version. As a single reviewer we acknowledge the fact that this was a difficult process. We are pleased to clarify your concerns which we believe will improve the impact and quality of our work. Please find below our response to your observations. We have made a concerted attempt to systematically address the specific concerns raised for this revision and we have highlighted the alterations to this revision within the manuscript in yellow for your convenience.

In advance,

King Regards

Adam J. Petway

Reviewer #1: - English grammar and spellings must be revised before a resubmission of the manuscript due to the fact that too many typos appear through the manuscript. In this sense the reviewer highlighted some of them, but I did not make an extensive review of typos.

Author’s Response- Thank you so much for highlighting this issue. We have conducted a full revision of the English grammar to meet the standards for publication.

- P2, L50, please add into brackets the specific demands (e.g., physical, technical…).

Author’s Response- Thank you so much. Changed as suggested: (physiological, mechanical, technical, tactical) was added.

- P2, L52, please replace the term athlete with player through the text.

Author’s Response- Thanks so much. Good detail. Changed as suggested.

- P2, L54, please add after competition the different levels analysed (i.e., elite…).

Author’s Response- Agree, Changed as suggested. Elite, Sub-Elite, and Youth.

- P2, L54, please write Web of Science instead of WoS.

Author’s Response- Changed as suggested.

- P2, l55-57, please replace the numbers (1), (2) and (3) with (i), (ii), (iii).

Author’s Response- Changed as suggested.

- P2, L57, please add “and” before (3).

Author’s Response- Changed as suggested.

- P2, L58, please clarify the type of study participants: …study participants into elite (xxxxxxx), sub-elite (xxxxxxx), and youth (xxxxxxxxx).

Author’s Response- Changed as suggested. We now indicate the number of studies included for each category: Elite (20), Sub-Elite (9), Youth (6).

- P2, L59 and L63, add the word systematic before review.

Author’s Response- Changed as suggested.

- P2, L60, please write players instead of athlete.

Author’s Response- Changed as suggested.

- P2, L61, please write players instead of athlete.

Author’s Response- Changed as suggested.

- P2, L61, please write a full stop after athletes and start a new sentence.

Author’s Response- Changed as suggested.

- P2, L61, please delete the comma after distance.

Author’s Response- Changed as suggested.

- P2, L62, please add “than the other level players” after competition.

Author’s Response- Changed as suggested.

- P2, L66, please replace “help determine” with “provide”.

Author’s Response- Changed as suggested.

- P3, L82, please delete “of two halves, or”.

Author’s Response- Changed as suggested.

- P3, L84, please write “game-related”.

Author’s Response- Changed as suggested.

- P3, L91, what does “competition alone” mean? Please clarify the sentence and meaning of Stojanovic´s article.

Author’s Response- “competition alone” refers to the fact that Stojanovic et al. did not examine training load and how that compared to the demands of competition.

- Please explain in depth the Stojanovic´s article. This reference detailed some relevant factors that affect the training load in basketball: playing period, playing position, level, geographical location and sex. In fact, the authors should justify why the current systematic review is needed, mainly when the Stojanovic´s one was published in 2017 and only a few articles were published after that date.

Author’s Response- This is a great point, thank you for bringing it up. The Stojanovic article is discussed in more depth. The fact that training load was not examined and that there is not any data reported on acceleration/deceleration demands justifies the need for the current systematic review. This is now highlighted in the text.

- P3, L92, please explain the stress variables identified in the Stojanovic´s review.

Author’s Response- Heart rate, total distance, blood lactate and time-motion analysis are the stress variables mentioned in the article.

- P3, L93, please replace “Of note” with “It is important to note”.

Author’s Response- Changed as suggested.

- P3, L95, replace are with were.

Author’s Response- Changed as suggested.

- P3, L95, please write fact instead of effect.

Author’s Response- Changed as suggested.

- P4, L99-100, please write “…were the variables widely used to assign”.

Author’s Response- Changed as suggested.

- P4, L104, please replace conclusive with concluding or robust.

Author’s Response- Changed as suggested.

- P4, L106, please write “a better understanding of this…”

Author’s Response- Changed as suggested.

- P4, L109, please write …”stress that the players deal with during trainings and competitions”.

Author’s Response- Changed as suggested.

- P4, L111, please write “team sport” instead of “sport”.

Author’s Response- Changed as suggested.

- P4, L111, please write “…may be of extreme importance to ensure that players are…”.

Author’s Response- Changed as suggested.

- P4, L112, please write “…prepared for competition demands, from a fitness standpoint, in order to avoid…”.

Author’s Response- Changed as suggested.

- P4, L115, please replace dedicated to with focused on.

Author’s Response- Changed as suggested.

- P5, L121, player instead of athlete.

Author’s Response- Changed as suggested.

- P5, L123, write loads instead of load.

Author’s Response- Changed as suggested.

- P5, L125-126, please rewrite the sentence.

Author’s Response- Changed as suggested.

- P5, L127-128. The rationale of the current systematic review should be better presented and justified (as was pointed out above) highlighting the novelty of studying the different level of competition, and what does the current review add to the previous one of Stojanovic et al (2017).

Author’s Response- Changed as suggested. This is a great point. We elaborated on the fact that it is helpful for coaches and sports scientists to have information about training load versus match-play demands and how that compares based on competition level. In addition, we emphasize that the present review addresses the studies that have investigated the acceleration/deceleration demands of training and competition.

- P5, L132-135, please split it into 2 sentences.

Author’s Response- Changed as suggested. The sentence was re-arranged to make it easier for the reader to follow.

- P5, L132, please write “in the available research”.

Author’s Response- Changed as suggested.

- P5, L134-135, please write “Therefore, the aim of the present systematic review was to analyze the evidence related to…”

Author’s Response- Changed as suggested.

- P5, L140, please write focused instead of focusing.

Author’s Response- Changed as suggested.

- P6, L150, the authors should redo the search due to the fact that the first search was around 8 months ago, and not too many studies were found for sub-elite and youth levels.

Author’s Response- After conducting the search again only one new article was found that met the inclusion criteria. Vázquez-Guerrero et al. from September 2019 examined changes in physical demands between quarters. This study was conducted on youth athletes over 13 matches and, therefore, was added to the manuscript. All the other published articles were in referee, female, or wheelchair basketball and did not include training load or competition demands.

- P7, L170, please write 19 years old.

Author’s Response- Changed as suggested.

- P7, L172-178, please replace the numbers (1), (2) and (3) with (i), (ii), (iii), (iv).

Author’s Response- Changed as suggested.

- P7, L174 and L178, please write and before (3) and (4), respectively.

Author’s Response- Changed as suggested.

- P7, L178, please write “a conference proceeding”.

Author’s Response- Changed as suggested.

- Quality of figures 1 and 2 must be improved.

- P8, L199, please add “the” before systematic.

Author’s Response- Changed as suggested.

- Tables 1 to 5 should include the n of matches and individual observations for each study.

Author’s Response- Number of matches and training sessions were added to the tables, as well as the competition level for training and match-play.

- The units and symbols included in tables 1 to 5 should follow the same scale/unit of measure (m*s). In addition, the description of variables should include in all the studies mean and SD.

Author’s Response- Changed as suggested.

- Tables 1 to 5, please unify the names of the leagues to use the correct terminology. For example Spanish ACB league instead of Spanish ACB or what does Spanish Basketball Federation mean? Authors should write clearly each league name or sample used in order to avoid misunderstandings of readers and researchers.

Author’s Response- Changed as suggested to Spanish ACB League. Spanish Basketball Federation is the Sub-Elite second division in Spain.

- Results, the authors must unify the way to present the results. I suggest that mean and sd will be provided for all variables. However, the authors did this approach for some variables, but not for all.

Author’s Response- Changed as suggested. SD was added to all of the presented variables. The only case in which SD were not reported was when we presented ranges for some of the variables based on the publish data.

- I suggest that the CV% would be included by authors in the analysis of each article to check the variability of their findings. This information will be useful to discuss the importance (stability) of variability among levels and then justify the large differences in some variables.

Author’s Response- Thanks for your comment. We agree that %CV would be a great addition to the manuscript. However, given that most articles only report mean values (and not individual values) it was not possible for us to calculate the CV for each variable.

- P11, L255, please write 4,369.

Author’s Response- Changed as suggested.

- P11, L255, please write 3,722 and 6,208.

Author’s Response- Changed as suggested.

- P11, L255, please write 5,377.

Author’s Response- Changed as suggested.

- P11, L255, please write 7,558.

Author’s Response- Changed as suggested.

- P11, L255, please write 4,369.

Author’s Response- Changed as suggested.

- P11, L255, please write 5,377 and 7,558.

Author’s Response- Changed as suggested.

- P11, L257, please write In particular instead of “Of note”.

Author’s Response- Changed as suggested.

- P11, L261-262, please delete “supporting that higher-level players travel the least amount of distance during competition”.

Author’s Response- Deleted as suggested.

- P11, L262-270. The authors should explain and argue some relevant factors and variables that can justify the level differences such as experience, tactical discipline, hours of training, more professional supervised practice or the control of competition.

Author’s Response- Thank you for your suggestion. We totally agree. We have re-written the sentence to bring up the point of elite athletes having a higher degree of technical and tactical economy based on training age, professional supervised practices, and higher-level coaching.

- P12, L289, please write In fact instead Of note.

Author’s Response- Changed as suggested.

- P13, L319, garner???

Author’s Response- Changed garner to achieve.

- P14, L322, please replace “demonstrated” with identified or found.

Author’s Response- Changed as suggested.

- The study of Puente et al (39) should be carefully discussed and argued during results and discussion because only 1 match was studied. Then, some of the authors’ arguments need to be revised to state the limitations of sample size of some studies included in the current review.

Author’s Response- The limitation that only one competition was tracked was brought up as well as the fact that future research is needed.

- P16, L342, please write correctly the units of running.

Author’s Response- All units of running were changed to (m·s).

- P16, L343, please write sprinting with lower case letters.

Author’s Response- Changed as suggested.

- P16, L346, please add that before there was.

Author’s Response- Changed as suggested.

- P16, L354, please write these results would instead of this would.

Author’s Response- Changed as suggested.

- P16, L355, please delete “at the”.

Author’s Response- Deleted as suggested.

- P16, L 355, please replace athletes with players.

Author’s Response- Changed as suggested.

- P16, L356, please add a full stop after counterparts and clarify the last sentence.

Author’s Response- Changed as suggested.

- P16, L359, please clarify that the studies analyzed time motion variables or indicators, but not time motion analysis.

Author’s Response- Changed as suggested.

- P18, L381-383, please write vs or on when explaining the game situations (e.g., 3 on 3 or 3vs3, but not 3v3).

Author’s Response- Changed to 5vs5 as suggested.

- P18, L383, please replace games with situations.

Author’s Response- Changed as suggested.

- P21, L432, please write correctly the units of measure after >3.5.

Author’s Response- Changed to (m*s-2) as suggested.

- P22, L453-454, please write vs or on for each game situation (e.g., 3 on 3 or 3vs3, but not 3v3).

Author’s Response- Changed to 3vs3 as suggested.

- P22, L455, please replace athletes with players.

Author’s Response- Changed as suggested.

- P22, L461-462, please justify with scientific and valid arguments the training load differences by playing position. The game play actions raised were neither studied nor real facts of the actual game play of basketball.

Author’s Response- The reviewer makes a compelling argument and he is right. Thank you. Interpositional demands discussed were justified by previous literature [5,6,9,13,40].

- P22, L468-475, please discuss these issues with valid and scientific references that support yours statements.

Author’s Response- The section on interpositional demands was validated via previous work [5,6,9,13,40].

- Limitations, the authors should include more limitations of the current systematic review such as sample size of some studies (Puente et al) or the reduced number of studies for youth players.

Author’s Response- Limitations of the Puente et al. study was discussed as well as the lack of studies within the youth and sub-elite levels.

- P24, L496, please replace athletes with players.

Author’s Response- Changed as suggested.

- Conclusion, L505, please add positional aspects of the sport.

Author’s Response- Positional aspects were changed and added as future research lines due to the abovementioned reasons.

- Conclusion, replace athlete with player L506, L508, L509, L510, L515, L521, and L522.

Author’s Response- Changed as suggested.

- P24, L 508, please add to before dictate.

Author’s Response- Changed as suggested.

- P24, L509, please add systematic before review.

Author’s Response- Changed as suggested.

- P25, L516, wholistic???

Author’s Response- Changed to global.

- Reference list should be revised in depth according to the journal guidelines: (i) journal title in italics and short title (refs 1-20 and 22-53); (ii) incomplete references without number of pages, article number of journal vol/num (refs 3 and 48); or (iii) wrong journal title ref 45 (Sports medicine instead of Springer) or ref 21.

Author’s Response- Italics and short titles were corrected as well as the vol and title for 3, 48, 45, and 21.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

3 Feb 2020

Training Load and Match-Play Demands in Basketball based on Competition Level: A Systematic Review

PONE-D-19-30568R1

Dear Dr. Petway,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Carlos Balsalobre-Fernández

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

18 Feb 2020

PONE-D-19-30568R1

Training Load and Match-Play Demands in Basketball based on Competition Level: A Systematic Review

Dear Dr. Petway:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Carlos Balsalobre-Fernández

Academic Editor

PLOS ONE