The purpose of this study was to probe the sex-based differences in the stroke and movement dynamics of Grand Slam hard-court tennis. Player and ball tracking data were collated for 102 male and 95 female players during the 2012-2014 Australian Open tournaments. Serve, serve return, groundstroke and movement data were compared between sexes. Serve statistics were the subject of the largest differences, with males achieving significantly faster speeds, aces and unreturned serves while also winning a greater percentage of service points. When returning serve, women contacted the ball closer to the net, lower to the ground and achieved flatter ball trajectories than males. Groundstroke frequencies were similar between sexes, although males hit with greater speed, flatter trajectories and impacted more shots inside the baseline. Distance covered per set or during points won or lost was not sex dependent, yet men exhibited faster average movement speeds. These findings highlight the need for sex-specific training and practice designs that cater to the different stroke dynamics, particularly in relation to the first serve and serve-return, as well as movement speeds.

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.

The aim of this study was to determine the high-speed running and sprinting profiles of elite female soccer players during competitive matches using a new Optical Player Tracking system. Eight stationary video cameras were positioned at vantage points surrounding the soccer field so that when each camera view was combined, the entire field could be viewed simultaneously. After each match, an optical player tracking system detected the coordinates (x, y) of each player for every video frame. Algorithms applied to the x and y coordinates were used to determine activity variables for 12 elite female players across 7 competitive matches. Players covered 9,220-10,581 m of total distance, 1,772-2,917 m of high-speed running (3.4-5.3 m·s) distance, and 417-850 m of sprinting (>5.4 m·s) distance, with variations between positional groups (p < 0.001; partial η = 0.444-0.488). Similarly, the number of high-speed runs differed between positional groups (p = 0.002; partial η = 0.342), and a large proportion of high-speed runs (81-84%) and sprints (71-78%) were performed over distances less than 10 m. Mean time between high-speed runs (13.9 ± 4.4 seconds) and sprints (86.5 ± 38.0 seconds) varied according to playing position (p < 0.001; partial η = 0.409) and time of the match (p < 0.001; partial η = 0.113-0.310). The results of this study can be used to design match-specific conditioning drills and shows that coaches should take an individualized approach to training load monitoring according to position.

No abstract

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.