Physical Movement Demands of Training and Matches Across a Full Competition Cycle in Elite Netball

applied sciences

Article Physical Movement Demands of Training and Matches across a Full Competition Cycle in Elite Netball

Edward R. Brooks 1,* , Amanda C. Benson 2, Aaron S. Fox 1 and Lyndell M. Bruce 1 1 Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Burwood 3125, Victoria, Australia; [email protected] (A.S.F.); [email protected] (L.M.B.) 2 Sport Innovation Research Group, Department of Health and Medical Sciences, Swinburne University of Technology, Hawthorn 3122, Victoria, Australia; [email protected] * Correspondence: [email protected]

Received: 6 October 2020; Accepted: 27 October 2020; Published: 30 October 2020

Abstract: Elite netballers perform different training and match sessions across the annual competition cycle. A comprehensive exploration of the physical movement demands imposed on the athletes and the distribution of external workloads across these sessions are yet to be reported in the literature. This study aims to quantify the movement demands of elite netball across all session types (pre-season training, pre-season practice match, pre-season cup matches, in-season training, in-season practice matches, and competition matches). Knowledge of these demands will allow for more precise season planning and may assist with the management of athletes’ workloads. Twelve elite female netballers were monitored across a full competition cycle using a local positioning system (LPS) to collect spatiotemporal (i.e., distance, velocity, and acceleration measures) and accelerometer-derived measures (i.e., PlayerLoad measures). Metreage and PlayerLoad per minute for match-play sessions (practice and competition) were higher than for training sessions for all positional groups. Differences were present across session types, and within and between positional groups, which should be incorporated into full season planning (i.e., pre-season, in-season, and finals). Coaches and support staff should be cognisant of the potential influence of changing a player’s position, or altering session structure or duration, on the workload for the athlete.

Keywords: local positioning system; ultra-wideband; wearable; team-sport; competition

1. Introduction A well-structured, periodised training program enhances physiological qualities of the athlete through a balanced exposure to stimuli, whilst managing stress and fatigue, with the aim of improving athletic performance [1]. Teams often employ thoroughly considered regimes involving variations in loading and recovery across diﬀerent facets of physical and skill development preparation, in order to periodise their programmes [2]. Elite team sports such as rugby [3], basketball [4], and Australian football [5] have demonstrated the changes in external load demands that occur in diﬀerent phases of a competitive season. The capacity to monitor the physical movement demands of elite netballers during training and competition in indoor environments has improved in recent years, predominantly due to advances in technology. Wearable devices and local positioning systems (LPS) enable outcomes such as accelerometer-derived load (e.g., PlayerLoad) and more recently, spatiotemporal outcomes (e.g., distances covered) to be collected during training and competition [6,7]. Currently, there is a scarcity of published data relating to the physical movement demands across the entire netball season, which limits the understanding of the stimuli imposed on these athletes.

Appl. Sci. 2020, 10, 7689; doi:10.3390/app10217689 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 7689 2 of 10

Accelerometer-derived variables (e.g., PlayerLoad) have been used to explore the physical movement demands in netballers [8,9]. These studies revealed new insights into the movement of netballers and demonstrated that the use of movement-tracking sensors (i.e., accelerometers) allows for the simultaneous collection of objective training load data across a netball squad in an indoor environment [8,9]. However, these early studies recruited participants from sub-elite cohorts and focused on differentiating between netballers of different skill-levels [9], or used a small sample of in-season training and competition (4 matches and 15 training sessions) [8]. Two later studies have provided measures of workload over the course of an elite netball season [6,10]. Young et al. [6] were the first to use accelerometer-derived measures of physical movement demands across the whole regular-season timeframe with an elite squad of netballers. Unfortunately, no pre-season training or pre-season competition data were included in their analyses [6]. While the study showed some informative comparisons of activity profiles between netball positions, the reported values are not directly comparable to values shown in this or other published studies [7–9]. This disparity in values may be a result of inconsistencies in definitions and application of the PlayerLoad algorithm in the literature [11]. Another recent study investigated the relationships between external and internal workload variables in elite netball using an extensive data collection period covering the entire pre-season and regular-season [10]. The findings of this correlational analysis identified a number of strong positive relationships that exist between measures of internal workload (heart rate indices, session-rating of perceived exertion) and accelerometer-derived external workload measures [10]. This supports the value of accelerometer-derived measures such as PlayerLoad in netball, as this external measure of load may be useful to infer internal physiological stress. However, the descriptive data for each session type (training/competition) or season phase (pre-season/in-season) were not presented, which precludes its use for the purposes of quantifying specific movement demands or season workload planning. Sweeting et al. [12] were the first to use a LPS in netball, employing radio-frequency tracking to assess the recurring sequences of movement in elite netball match-play. The evolution of LPS technology in sport has moved toward the use of ultra-wideband communication methods [13], as this improves signal stability and may reduce interference when compared to traditional radio-frequency signal communications [14]. A recent analysis of elite netball used a commercially available LPS to monitor the physical demands of match-play using both spatiotemporal and accelerometer-derived variables [7]. Spatiotemporal measures can provide additional context about the physical movement demands, in addition to an overall sum of movement provided by accelerometry. The study profiled all netball positions during elite match-play using a range of measures (distance, velocity, acceleration, etc.) and provided descriptive comparisons between positional demands [7]. However, the study comprised a small number of matches (n = 6) and no training data were provided. There are limited data available exploring the physical movement demands of elite netball, particularly across different session types or season phases. Currently, no studies have examined spatiotemporal and accelerometer-derived variables across a full season of elite netball, inclusive of all pre-season and in-season on-court training and competition sessions. A wide-ranging evaluation of a full season of elite netball would not only outline trends of load incurred by the athletes but would also quantify the movement demands of a number of session types that have not previously been explored (e.g., pre-season competition matches and practice matches). Additionally, club training sessions from both pre-season and in-season are yet to be quantified using spatiotemporal measures. Therefore, the aim of this study was to describe the sessional on-court movement demands and external workloads across an entire season of elite netball, using both accelerometry and positioning technologies. Appl. Sci. 2020, 10, 7689 3 of 10

2. Materials and Methods

2.1. Participants Twelve elitefemalenetballers(mean standarddeviation:age=25.9 5.1years; height = 178.6 8.9 cm; ± ± ± body mass = 71.1 7.1 kg) from one team competing in Australia’s premier netball competition ± (Suncorp Super Netball) provided written informed consent prior to participating in this study. Athletes were categorised according to their positional grouping—defender (GK, goal keeper; GD, goal defence), goaler (GS, goal shooter; GA, goal attack), or midcourter (C, centre; WA, wing attack; WD, wing defence) [15,16]—as many athletes play in more than one specific position throughout the season, but typically remain within their positional grouping. Classifying athletes within positional groups allowed for reporting while protecting the confidentiality of each individual. The athletes recruited for this study covered all netball positional groups: goalers (n = 3), defenders (n = 4), and midcourters (n = 5). Team lists are restricted to 10 main squad athletes; however, injured athletes may be replaced during the season with official team training partners or approved external injury replacement players. Replacement players were included in this study where they both trained with the team and played during an official competition match. Ethical approval was granted by the Deakin University Human Ethics Advisory Group (HEAG-H 206_2018).

2.2. Data Collection This was a longitudinal single-cohort observational research design for one team. Data collection occurred over an entire pre-season and competitive season of elite netball. Catapult T6 devices containing a 10 Hz LPS receiver tag and 100 Hz inertial sensors (tri-axial accelerometer, gyroscope, and magnetometer) (Catapult Sports, Melbourne, Australia) were worn by each athlete for every training session and match. Devices were held in a fitted pouch built into a sports vest (upper-back) to ensure a close fit for the sensors and unencumbered movement for the athletes. The Catapult ClearSky LPS (Catapult Sports, Melbourne, Australia) consisted of a pre-installed network of anchors in the team’s training centre and home competition venue to communicate with the LPS receiver tags in the T6 devices and track positional movement data using ultra-wideband technology. The ClearSky LPS has shown acceptable validity for time-motion analysis in team sport applications within an indoor environment [17]. Training sessions and matches recorded in stadia with no LPS system were limited to accelerometer-derived variables only. Data were recorded from all coach-prescribed, on-court, pre-season training sessions (n = 29), pre-season practice matches (n = 8), pre-season tournament matches (n = 4), in-season training sessions (n = 21), in-season practice matches (n = 5), and competition matches (n = 15). A total of 744 unique sessions for all athletes, across all session types over the course of the full season were recorded. LPS anchors were only available in one competition stadium, thereby limiting available LPS data capture to matches played at this venue (n = 6). All pre-season and in-season training sessions (n = 50) were captured using LPS technology, with anchors installed in the training stadium. The total number of unique sessions collected containing LPS variables was 566 of the 744 individual samples. All sessions were coded using the accompanying proprietary software—Openfield v1.22.0 (Catapult Sports, Melbourne, Australia). The times when a player was on the bench and breaks between quarters were excluded from match-play data. During training sessions, coaching stops and breaks between drill blocks were excluded from analysis. However, planned rest intervals in drills were included. Spatiotemporal and accelerometer-derived data are reported in this manner to reflect the actual volume and intensity of the drills, quarters, and prescribed tasks—rather than as an average of the entire session. No resistance training or off-court conditioning sessions were included in this analysis, as the focus of this study was on-court workloads. Appl. Sci. 2020, 10, 7689 4 of 10

2.3. Key Season Events A pre-season tournament occurred on March 8–10 requiring athletes to play up to four matches in a three-day time frame. A scheduled season break occurred between round 9 (23rd June) and round 10 (27th July) for the quadrennial Netball World Cup, coordinated by the International Netball Federation. Training and matches were halted during the Netball World Cup, as many players across the domestic competition were released for international duties.

2.4. Data Analysis The statistical computing program R (version 3.6.0) was used for data analysis [18]. Standard spatiotemporal and accelerometer-derived variables from Catapult’s Openﬁeld software were assessed based upon a previous study into netball match performance [7]. Spatiotemporal measures include total distance covered (m), metreage per minute (m min 1), acceleration density (avg acc/dec; · − m s 2), acceleration density index (avg acc/dec per 10 m; m s 2), and acceleration load (total acc/dec; · − · − m s 2). Accelerometer-derived measures include total PlayerLoad (au), PlayerLoad per minute · − (au min 1), and total jumps (count). Openﬁeld software was used to summarise and process the · − variables in the console, and then export them for analysis in R. Descriptive statistics (mean and standard deviation) were calculated using the {dplyr} package (version 0.8.4) for all variables [19]. Figures were produced using the {ggplot2} package (version 3.2.1) [20]. Comparisons are made based on the descriptive data presented, as the limited number of independent samples (squad size) constrained the use of inferential statistics.

3. Results

3.1. Absolute Session Demands Descriptive statistics (mean and standard deviation) for measures of absolute movement (volume) in each session type and positional grouping are presented in Table1. All positional groups accumulated their highest mean total distances during in-season practice matches, whereas the session type which accumulated the highest total PlayerLoad diﬀered between positional groups (midcourters—competition matches, defenders—pre-season practice matches, and goalers—in-season practice matches). Positional group summaries of each session type are presented in Table1. The distribution of total weekly training and competition load (volume) across the full season, as measured using total PlayerLoad, is presented in Figure1. A summary (mean and standard deviation) for all session types and athletes across the season is presented in Figure2. Appl. Sci. 2020, 10, 7689 5 of 10

Table 1. Descriptive statistics for parameters of session volume in netball for each positional group. Data are presented as mean standard deviation. ± Total Acceleration Acceleration Density Acceleration Total Total Metreage PlayerLoad Positional Duration Total Distance Load PlayerLoad (2D) Index Density Session Type PlayerLoad Jumps Per Minute Per Minute Group (min) (m) (total acc/dec; (au) (avg acc/dec per 10 m; (avg acc/dec; (au) (count) (m min 1) (au min 1) m s 2) · − · − m s 2) m s 2) · − · − · − Competition Match 55.4 26.8 3150.3 1765.3 1876.3 1009.2 451.9 226.6 286.5 145.4 44.2 23.7 59.67 12.37 8.15 1.23 6.04 0.68 0.57 0.10 ± ± ± ± ± ± ± ± ± ± In-season Practice Match 56.8 12.9 3739.2 1070.3 2303.5 589.7 458.2 144.1 287.8 84.4 44.3 16.9 67.01 13.21 8.10 1.66 6.22 0.55 0.68 0.10 ± ± ± ± ± ± ± ± ± ± Pre-season Practice Match 64.9 21.9 3230.1 866.1 2050.8 464.0 477.3 159.0 256.7 136.1 54.0 16.4 58.76 10.11 7.47 1.29 6.42 0.49 0.62 0.07 Defender ± ± ± ± ± ± ± ± ± ± Pre-season Cup Match 43.4 11.3 - - 365.1 96.2 232.7 63.2 38.2 15.1 - 8.39 1.40 - - ± ± ± ± ± Pre-season Training 67.3 15.2 3228.5 784.8 2281.9 540.7 461.3 110.8 295.5 70.3 40.4 13.2 48.27 9.58 7.00 1.35 7.10 0.79 0.56 0.07 ± ± ± ± ± ± ± ± ± ± In-Season Training 60.6 13.3 3099.0 813.5 2109.5 505.1 418.8 116.4 265.1 71.3 42.9 17.0 51.46 7.91 6.89 1.20 6.88 0.63 0.59 0.07 ± ± ± ± ± ± ± ± ± ± Competition Match 64.4 23.4 4478.2 1907.9 2653.2 1109.5 614.5 239.0 383.2 148.9 43.6 22.9 76.30 9.74 9.38 1.39 6.01 0.38 0.71 0.09 ± ± ± ± ± ± ± ± ± ± In-season Practice Match 61.2 13.9 4740.2 1090.2 2866.9 701.1 580.8 148.0 365.8 91.7 49.4 19.5 78.21 6.17 9.49 1.09 6.03 0.36 0.78 0.05 ± ± ± ± ± ± ± ± ± ± Pre-season Practice Match 69.6 20.9 3801.6 1359.0 2240.5 748.9 602.4 176.7 347.5 155.6 49.1 21.8 75.01 4.27 8.81 1.20 5.96 0.26 0.74 0.04 Midcourter ± ± ± ± ± ± ± ± ± ± Pre-season Cup Matches 51.9 7.8 - - 478.2 52.4 298.1 29.6 36.4 12.5 - 9.29 0.75 - - ± ± ± ± ± Pre-season Training 68.0 14.2 3524.6 843.2 2416.0 584.9 497.0 110.8 317.4 71.7 39.4 14.6 51.94 9.60 7.40 1.21 6.87 0.69 0.59 0.08 ± ± ± ± ± ± ± ± ± ± In-Season Training 60.3 13.9 3383.7 902.1 2296.3 598.1 453.8 126.8 290.3 79.5 39.7 19.8 56.67 8.18 7.53 1.21 6.80 0.58 0.64 0.08 ± ± ± ± ± ± ± ± ± ± Competition Match 57.4 25.7 3143.4 1939.5 1846.5 1016.9 417.9 188.8 259.8 114.9 58.1 33.1 58.22 21.35 7.38 1.29 6.04 0.96 0.54 0.15 ± ± ± ± ± ± ± ± ± ± In-season Practice Match 63.0 10.9 3725.2 1535.5 2350.5 713.7 455.4 152.5 283.2 94.7 62.4 24.9 59.56 21.02 7.16 1.74 6.67 1.21 0.62 0.14 ± ± ± ± ± ± ± ± ± ± Pre-season Practice Match 64.3 22.8 2792.7 1127.5 1735.0 592.4 441.0 147.8 243.2 131.8 61.8 29.0 58.75 18.52 7.10 1.61 6.36 1.00 0.60 0.11 Goaler ± ± ± ± ± ± ± ± ± ± Pre-season Cup Matches 48.9 15.7 - - 375.8 146.1 237.2 94.3 60.0 24.2 - 7.64 1.60 - - ± ± ± ± ± Pre-season Training 66.8 18.0 2713.1 854.0 1957.1 561.3 399.1 132.2 257.6 87.4 40.1 22.6 40.51 11.11 6.05 1.53 7.36 1.19 0.48 0.09 ± ± ± ± ± ± ± ± ± ± In-Season Training 58.3 12.2 2798.9 859.7 1915.1 555.0 373.9 117.0 233.1 74.9 47.6 22.9 47.66 9.16 6.36 1.21 6.91 0.64 0.54 0.08 ± ± ± ± ± ± ± ± ± ± Abbreviations: Defender: goal defence, goal keeper; Midcourter: centre, wing attack, wing defence; Goaler: goal shooter, goal attack; m: metres; acc/dec: acceleration and deceleration; m s 2: metres per second; au: arbitrary units; m s 1: metres per minute; avg: average. - symbol: denotes no positioning data available. · − · − Appl.Appl. Sci. 2020,, 10, 7689x FOR PEER REVIEW 6 of6 of10 10 Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 10

Figure 1. Season trends in total weekly PlayerLoad by positional group. Each point on the vertical FigureFigure 1.1. SeasonSeason trendstrends inin total total weekly weekly PlayerLoad PlayerLoad by by positional positional group. group. Each Each point point on on the the vertical vertical axis axis represents a single athlete’s weekly total PlayerLoad. The boxplots show the mean and quartile representsaxis represents a single a single athlete’s athlete’ weeklys weekly total PlayerLoad.total PlayerLoad. The boxplots The boxp showlots show the mean the mean and quartile and quartile ranges ranges for each week across all athletes. Abbreviations; au = arbitrary units. forranges each for week each across week allacross athletes. all athletes Abbreviations;. Abbreviations; au = arbitrary au = arbitrary units. units.

FigureFigure 2. Trends in PlayerLoad by by session session type type (see (see legend). legend). Each Each point point on on the the vertical vertical axis axis represents represents Figure 2. Trends in PlayerLoad by session type (see legend). Each point on the vertical axis represents eacheach athlete’sathlete’s total PlayerLoad for for the the session. session. Ea Eachch boxplot boxplot shows shows the the mean mean and and interquartile interquartile range range each athlete’s total PlayerLoad for the session. Each boxplot shows the mean and interquartile range forfor aa weekweek consideringconsidering all athletes. A Abbreviations:bbreviations: au au == arbitraryarbitrary units. units. for a week considering all athletes. Abbreviations: au = arbitrary units. 3.2. Relative Session Demands 3.2. Relative Session Demands 3.2. Relative Session Demands MovementMovement parameters relative to to time time (intensity) (intensity) are are presented presented in in Table Table 1.1. Metreage Metreage per per minute minute Movement parameters relative to time (intensity) are presented in Table 1. Metreage per minute forfor allall match-play match-play session session types types (pre-season (pre-season practice practice match, match, in-season in-season practice match,practice and match, competition and for all match-play session types (pre-season practice match, in-season practice match, and match)competition was higher match) than wastraining higher than sessions training for allsessions positional for all groups. positional Midcourters groups. Midcourters worked at the worked highest competition match) was higher than1 training sessions for all positional groups. Midcourters worked averageat the highest running average intensity running (m min intensity− ) in every(m∙min session−1) in every type, session followed type, by followed defenders by and defenders then goalers. and at the highest average running· intensity (m∙min−1) in every session type, followed by defenders1 and Similarly,then goalers. accelerometer-derived Similarly, accelerometer-deriv movement intensityed movement (PlayerLoad intensity per minute; (PlayerLoad au min −per) showed minute; the then goalers. Similarly, accelerometer-derived movement intensity (PlayerLoad· per minute; sameau∙min pattern−1) showed of intensities the same acrosspattern session of intensities typesand across positional session types groups. and A positional visual representation groups. A visual of the au∙min−1) showed the same pattern of intensities across session types and positional groups. A visual movementrepresentation intensity of the for movement each positional intensity group, for during each positional each session group, type (includingduring each the session distributions), type representation of the movement intensity for each positional group, during each session type is(including presented the in distributions), Figure3. is presented in Figure 3. (including the distributions), is presented in Figure 3. AllAll positionspositions recorded the highest acceleration /deceleration/deceleration demands demands (acceleration (acceleration density) density) All positions recorded the highest acceleration/deceleration demands (acceleration density) duringduring match-play sessions, sessions, specifically speciﬁcally during during in-season in-season practice practice matches. matches. Midcourters Midcourters showed showed the during match-play sessions, specifically during in-season practice matches. Midcourters showed the thehighest highest acceleration acceleration density, density, followed followed by defenders by defenders and then and goalers then for goalers in-season for in-seasonpractice matches. practice highest acceleration density, followed by defenders and then goalers for in-season practice matches. matches.When average When averageacceleration acceleration and deceleration and deceleration are standardised are standardised by the by theamount amount of ofarea area covered covered When average acceleration and deceleration are standardised by the amount of area covered Appl. Sci. 2020, 10, 7689 7 of 10 Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 10

(acceleration(acceleration densitydensity index; index; average average per per 10 m10 covered), m covered), all positional all positional groups groups show the show highest the demands highest demandsduring pre-season during pre-season training sessions. training Allsessions. three positional All three groupingspositional experiencedgroupings experienced higher acceleration higher accelerationdensity index density values index in training values sessions in training than sessions in any match-playthan in any sessions.match-play sessions.

FigureFigure 3. 3. PlayerLoadPlayerLoad per per minute minute by by session session type type and and positi positionalonal group. group. Each Each point point on on the the vertical vertical axis axis representsrepresents a a single single session. session. The The raincloud raincloud plot plot shows shows the the raw raw data data (points), (points), distribution distribution (split (split violin), violin), andand summary summary statistics statistics (boxplots). (boxplots). Abbreviati Abbreviations:ons: au/min au/min == arbitraryarbitrary units units per per minute. minute. 4. Discussion 4. Discussion To our knowledge, this is the first study to present on-court movement and external workload To our knowledge, this is the first study to present on-court movement and external workload demands across an entire season of elite netball (pre-season and in-season) using both positional and demands across an entire season of elite netball (pre-season and in-season) using both positional and accelerometer sensors. Minor undulations across the season for all positions were observed in player accelerometer sensors. Minor undulations across the season for all positions were observed in player load variables (see Figure1); they align with key events across the season. Load management strategies load variables (see Figure 1); they align with key events across the season. Load management can be identified in Figure1, where increased weekly PlayerLoad occurs in the pre-season and precedes strategies can be identified in Figure 1, where increased weekly PlayerLoad occurs in the pre-season periods of extended recovery (e.g., Netball World Cup break), whereas weekly PlayerLoad decreases and precedes periods of extended recovery (e.g., Netball World Cup break), whereas weekly leading into the start of the season and around finals. There are clear outliers that show an acute PlayerLoad decreases leading into the start of the season and around finals. There are clear outliers increase in weekly load during early March, which directly corresponds to the pre-season cup, where that show an acute increase in weekly load during early March, which directly corresponds to the athletes played up to four matches in three days, albeit with shortened quarters (10-min quarters) and pre-season cup, where athletes played up to four matches in three days, albeit with shortened rolling-substitutions. It appears that the shortened time per quarter resulted in the lowering of total quarters (10-min quarters) and rolling-substitutions. It appears that the shortened time per quarter movement demands (volume) per match, as indicated by the lower total PlayerLoad across all positional resulted in the lowering of total movement demands (volume) per match, as indicated by the lower groups when compared to standard competition matches (see Table1). However, the intensity was total PlayerLoad across all positional groups when compared to standard competition matches (see similar when compared to standard competition matches. While it is possible that players’ intensities Table 1). However, the intensity was similar when compared to standard competition matches. While were higher due to a lack of residual season fatigue (being pre-season competition), they were required it is possible that players’ intensities were higher due to a lack of residual season fatigue (being pre- to play with a higher frequency than usual and less recovery time. Therefore, the use of shortened season competition), they were required to play with a higher frequency than usual and less recovery quarters and rolling substitutions, which were not standard for the regular-season competition, may be time. Therefore, the use of shortened quarters and rolling substitutions, which were not standard for useful in practice match-play when coaches aim to restrict the volume, while maintaining or increasing the regular-season competition, may be useful in practice match-play when coaches aim to restrict the intensity of the session. the volume, while maintaining or increasing the intensity of the session. The parameters of session volume presented in Table1, such as total distance and total PlayerLoad, The parameters of session volume presented in Table 1, such as total distance and total follow the expected pattern of midcourters accumulating higher loads than defenders or goalers, PlayerLoad, follow the expected pattern of midcourters accumulating higher loads than defenders or based on position-specific movement demands during match-play [7]. However, within the positional goalers, based on position-specific movement demands during match-play [7]. However, within the groupings there appears to be substantial spread, particularly for match-play session types. This large positional groupings there appears to be substantial spread, particularly for match-play session spread indicates that there may be clustering within groups, based on the position played for the types. This large spread indicates that there may be clustering within groups, based on the position session (match). There are distinctly different demands for some positions within their positional played for the session (match). There are distinctly different demands for some positions within their groupings [6–8,21]. For example, goal attack covers the second highest distance in competition matches positional groupings [6–8,21]. For example, goal attack covers the second highest distance in (5337 165 m), while goal shooters cover the least (2134 103 m), yet both are classified as goalers [7]. competition± matches (5337 ± 165 m), while goal shooters± cover the least (2134 ± 103 m), yet both are The raincloud plots in Figure3 identify these separations as demonstrated by the binomial distribution classified as goalers [7]. The raincloud plots in Figure 3 identify these separations as demonstrated by the binomial distribution shape for sessions involving match-play [22]. This is most apparent for Appl. Sci. 2020, 10, 7689 8 of 10 shape for sessions involving match-play [22]. This is most apparent for the defender and goaler groups in competition matches but can also be observed to a lesser extent during in-season practice matches. The training sessions appear to distribute intensity more evenly between athletes within each positional group. Midcourt players appear to show a more consistent range of intensities both within and between all session types. The midcourters’ intensity is consistently higher than goalers or defenders across all session types (training and match-play). The different spatial constraints on a netball court imposed on each playing position likely contribute to these findings (e.g., goal attack can move in two thirds of the court, whereas goal shooters can only move within one third) [23]. Graham et al. [24] investigated the differing peak PlayerLoad intensity demands in state-level netball using accelerometers, finding that for epochs larger than 30 s, intensity demands largely correspond to the athlete’s zone constraints (highest peak demands for three-thirds—centre position). Movement demands may differ substantially between positions, even within typically used tactical positional groupings (defenders, goalers, midcourters). The results of the current study highlight that current training practices appear to generalise positions and do not show the same distribution of intensities as can be seen in match-play. The highest volume of running, across all positions, elicited during in-season practice matches is likely due to the flexible nature of these sessions. Coaches have more control over increasing the number of quarters played, running additional set-plays, testing new combinations of players/positions, or extending the clock-time. More time or load may be accumulated during the less strictly scheduled warm-ups, skill drills, or half-court drills that occur prior to the start of the practice match, when compared to the strictly timed schedule in competition. It is considered appropriate to expose athletes to similar movement and skill demands during training as they would experience in matches, as this provides the stimulus for athletes to adapt in preparation for performance [24–26]. Training session intensity as measured by PlayerLoad per minute (au min 1) does not appear · − to reflect the demands of match-play, with both training session types (pre-season and in-season) showing lower values across all positional groups. This is likely due to the large variation within training sessions (e.g., high intensity drills and slower technical components) where some portions aim to expose athletes to peak demands, while others focus on technical or tactical information and skill development. Future analysis should further differentiate the specific components of netball training (within-session drills), in order to better understand whether the athletes are exposed to the types of movement demands incurred in competition during specific drills or training activities. There are a few potential limitations with the current study, which should be considered when interpreting these findings. First, the Netball World Cup was held during the regular season timeframe (12–21 July), resulting in a four-week halt of the domestic competition. Unfortunately, this resulted in a large gap in the data collection, as labelled on Figures1 and2. The break may also have impacted the coach’s standard prescription of training characteristics, as they potentially attempted to re-adjust athlete workloads. Second, the data presented in this study were compiled from one team and may not be representative of the other elite netball teams. Third, small squad sizes (n = 10) in the Suncorp Super Netball competition limit the available options for statistical analysis. The use of positional groupings (i.e., defenders, goalers, and midcourters) also does not allow for athlete/position-specific findings. However, athlete anonymity is paramount given the novelty of this data set. Fourth, there are a number of factors that can impact training and match demands that may be specific to the team’s circumstances, such as quality of the opposition, number of days between games, and travel demands [27]. Therefore, where possible, teams should monitor the movement demands of their athletes across the season, to provide specific feedback to coaches and support staff about the trends in physical movement demands within their own programs, and aim to forecast and adapt to known or emerging challenges.

5. Conclusions All phases of the netball season’s cycle should be monitored, as there are diﬀerences across session type and season phase for each positional group. Monitoring throughout the full competition cycle may allow for more responsive and agile session planning and periodisation. Alterations to Appl. Sci. 2020, 10, 7689 9 of 10 the structure and duration of sessions or quarters have impacts on the demands imposed on the athletes. A greater understanding of the interactions among these factors, along with match scheduling, to identify dates when multiple matches are planned in a short time frame (such as pre-season tournaments), may enhance training load planning and athlete preparation. Players from within the defender and goaler groupings may need training modiﬁcations (such as increased running volume or change of direction drills in restricted space) to prepare them for a positional change in match-play, given the highly specialized positional movement demands in netball, even from within the same positional grouping.

Author Contributions: Conceptualization, E.R.B., A.C.B., A.S.F. and L.M.B.; methodology, E.R.B., A.C.B., A.S.F. and L.M.B.; formal analysis, E.R.B.; data curation, E.R.B.; writing—original draft preparation, E.R.B.; writing—review and editing, E.R.B., A.C.B., A.S.F. and L.M.B.; visualization, E.R.B., A.C.B., A.S.F. and L.M.B.; supervision, A.C.B., A.S.F. and L.M.B.; project administration, E.R.B., A.C.B., A.S.F. and L.M.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors would like to acknowledge and thank the participating club and athletes for their involvement with this study. E.R.B. is supported by an Australian Government Research Training Program Scholarship. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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