Player Load in Elite Netball: Match, Training, and Positional Comparisons

Young, Chris; Gastin, Paul B.; Sanders, Nick; Mackey, Luke; Dwyer, Dan

doi:10.1123/ijspp.2015-0156

Cited by 42 publications

(79 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accelerometer derived PlayerLoad™ data of elite female netball athletes was grouped by k -means clustering (Young et al, 2016). Optimal clustering was the greatest Euclidean distance obtained from two to five clusters (Young et al, 2016).…”

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

“…Optimal clustering was the greatest Euclidean distance obtained from two to five clusters (Young et al, 2016). The seven netball playing positions were divided into two groups according to playing intensity and relative time spent in a low-intensity zone (Young et al, 2016). The PlayerLoad™ for the goal based positions was lower than the attacking and wing positions, likely due to the time spent performing low intensity activity (Young et al, 2016).…”

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

“…The seven netball playing positions were divided into two groups according to playing intensity and relative time spent in a low-intensity zone (Young et al, 2016). The PlayerLoad™ for the goal based positions was lower than the attacking and wing positions, likely due to the time spent performing low intensity activity (Young et al, 2016). This study was the first to use data mining techniques, including k -means clustering, to examine athlete load data.…”

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

See 3 more Smart Citations

When Is a Sprint a Sprint? A Review of the Analysis of Team-Sport Athlete Activity Profile

et al. 2017

View full text Add to dashboard Cite

The external load of a team-sport athlete can be measured by tracking technologies, including global positioning systems (GPS), local positioning systems (LPS), and vision-based systems. These technologies allow for the calculation of displacement, velocity and acceleration during a match or training session. The accurate quantification of these variables is critical so that meaningful changes in team-sport athlete external load can be detected. High-velocity running, including sprinting, may be important for specific team-sport match activities, including evading an opponent or creating a shot on goal. Maximal accelerations are energetically demanding and frequently occur from a low velocity during team-sport matches. Despite extensive research, conjecture exists regarding the thresholds by which to classify the high velocity and acceleration activity of a team-sport athlete. There is currently no consensus on the definition of a sprint or acceleration effort, even within a single sport. The aim of this narrative review was to examine the varying velocity and acceleration thresholds reported in athlete activity profiling. The purposes of this review were therefore to (1) identify the various thresholds used to classify high-velocity or -intensity running plus accelerations; (2) examine the impact of individualized thresholds on reported team-sport activity profile; (3) evaluate the use of thresholds for court-based team-sports and; (4) discuss potential areas for future research. The presentation of velocity thresholds as a single value, with equivocal qualitative descriptors, is confusing when data lies between two thresholds. In Australian football, sprint efforts have been defined as activity >4.00 or >4.17 m·s−1. Acceleration thresholds differ across the literature, with >1.11, 2.78, 3.00, and 4.00 m·s−2 utilized across a number of sports. It is difficult to compare literature on field-based sports due to inconsistencies in velocity and acceleration thresholds, even within a single sport. Velocity and acceleration thresholds have been determined from physical capacity tests. Limited research exists on the classification of velocity and acceleration data by female team-sport athletes. Alternatively, data mining techniques may be used to report team-sport athlete external load, without the requirement of arbitrary or physiologically defined thresholds.

show abstract

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

Section: Alternate Approaches To Classify Athlete Activitymentioning

confidence: 99%

See 2 more Smart Citations

When Is a Sprint a Sprint? A Review of the Analysis of Team-Sport Athlete Activity Profile

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Research on the match movement output of netballers, according to playing position, is largely confined to estimates from video analysis (Fox, Spittle, Otago, & Saunders, 2013) or examined in a sub-elite cohort (Cormack, Smith, Mooney, Young, & O'Brien, 2014). Recently, the load of elite netball athletes was examined during training and matches according to playing position (Young, Gastin, Sanders, Mackey, & Dwyer, 2016). Although athlete displacement and velocity was not measured, the accelerometer-derived player load from each playing position was clustered into groups using a data mining technique (Young et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Discovering frequently recurring movement sequences in team-sport athlete spatiotemporal data

Sweeting

Aughey

Cormack

et al. 2017

Journal of Sports Sciences

View full text Add to dashboard Cite

Athlete external load is typically analysed from predetermined movement thresholds. The combination of movement sequences and differences in these movements between playing positions is also currently unknown. This study developed a method to discover the frequently recurring movement sequences across playing position during matches. The external load of 12 international female netball athletes was collected by a local positioning system during four national-level matches. Velocity, acceleration and angular velocity were calculated from positional (X, Y) data, clustered via one-dimensional k-means and assigned a unique alphabetic label. Combinations of velocity, acceleration and angular velocity movement were compared using the Levenshtein distance and similarities computed by the longest common substring problem. The contribution of each movement sequence, according to playing position and relative to the wider data set, was then calculated via the Minkowski distance. A total of 10 frequently recurring combinations of movement were discovered, regardless of playing position. Only the wing attack, goal attack and goal defence playing positions are closely related. We developed a technique to discover the movement sequences, according to playing position, performed by elite netballers. This methodology can be extended to discover the frequently recurring movements within other team sports and across levels of competition.

show abstract

Daily energy expenditure and water turnover in female netball players from the Netball Super League: A doubly labeled water observation study

Costello,

Jones,

Roe

et al. 2024

European Journal of Sport Science

View full text Add to dashboard Cite

To establish the criterion‐assessed energy and fluid requirements of female netball players, 13 adult players from a senior Netball Super League squad were assessed over 14 days in a cross‐sectional design, representing a two‐ and one‐match microcycle, respectively. Total energy expenditure (TEE) and water turnover (WT) were measured by doubly labeled water. Resting and activity energy expenditure were measured by indirect calorimetry and Actiheart, respectively. Mean 14‐day TEE was 13.46 ± 1.20 MJ day−1 (95% CI, 12.63–14.39 MJ day−1). Resting energy expenditure was 6.53 ± 0.60 MJ day−1 (95% CI, 6.17–6.89 MJ day−1). Physical activity level was 2.07 ± 0.19 arbitrary units (AU) (95% CI, 1.95–2.18 AU). Mean WT was 4.1 ± 0.9 L day−1 (95% CI, 3.6–4.7 L day−1). Match days led to significantly greater TEE than training (+2.85 ± 0.70 MJ day−1; 95% CI, +1.00– +4.70 MJ day−1; p = 0.002) and rest (+4.85 ± 0.70 MJ day−1; 95% CI, +3.13–+6.56 MJ day−1; p < 0.001) days. Matches led to significantly greater energy expenditure (+1.85 ± 1.27 MJ; 95% CI, +0.95–+2.76 MJ day−1; p = 0.001) than court‐based training sessions. There was no significant difference in TEE (+0.03 ± 0.35 MJ day−1; 95% CI, −0.74–+0.80 MJ day−1; p = 0.936) across weeks. Calibrated Actiheart 5 monitors underestimated TEE (−1.92 ± 1.21 MJ day−1). Energy and fluid turnover were greatest on match days, followed by training and rest days, with no difference across weeks. This study provides criterion‐assessed energy and fluid requirements to inform dietary guidance for female netball players.

show abstract

Player Load in Elite Netball: Match, Training, and Positional Comparisons

Abstract: Accelerometry is a valuable method of measuring player load in netball, and the present results provide new information about the activity profile of different playing positions.

Cited by 42 publications

References 0 publications

When Is a Sprint a Sprint? A Review of the Analysis of Team-Sport Athlete Activity Profile

When Is a Sprint a Sprint? A Review of the Analysis of Team-Sport Athlete Activity Profile

Discovering frequently recurring movement sequences in team-sport athlete spatiotemporal data

Daily energy expenditure and water turnover in female netball players from the Netball Super League: A doubly labeled water observation study

Contact Info

Product

Resources

About