4.2.1 Participants
Twenty-five skilled junior field hockey players with an average age of 12.2 ± 0.9years and a standing height of 1.56 ± 0.11 metres volunteered to participate in this study, after ethical approval was granted by the university ethics committee and parental consent was obtained. All children played in a
regional team from their city. These teams play in the zone challenges, a competition organised by the State Hockey association for talented club players from the State.
4.2.2 Experimental design
The number of players and playing density was systematically manipulated during field hockey games. Density or the individual playing area per player in m2 was manipulated by comparing the adult game of hockey (228m2 - labelled standard density) with a half-field game (158m2 – labelled scaled density) commonly used in competition for U10s. The number of players was then co-varied with density with the adult game (11 players a side – labelled standard numbers) compared with 8 players per team (labelled scaled numbers) which is a typical playing number in junior competition (see Table 4.1). Teams completed a 25 minute field hockey match in each condition and there were 11 participants who played in all four game conditions.
Table 4.1. Characteristics of the four different experimental conditions.
4.2.3 Apparatus and test procedures
All matches were played on a sand-based hockey pitch with a standard field hockey ball and the participants own equipment. After fitting participants with a back vest containing a global positioning system (GPS) unit (Optimeye S5, Catapult Innovations, Melbourne, Australia) sampling at 10Hz, a common 10-minute warm-up was performed. Only one match was played each day with all matches
Number of players Density (m ) Length/Width ratio Pitch dimensions (m) Standard density – standard numbers 11 per side 228 ± 1.6 91 x 55
Scaled density – standard numbers 11 per side 158 ± 1.2 64 x 54
Standard density – scaled numbers 8 per side 228 ± 1.6 77 x 47
played on the same day of the week one week apart. The games were recorded for analysis with a digital video camera (JVC, model GY-HM100, recording at 25 HZ) positioned on the side of the field and elevated 4m above the pitch. Pre-determined performance variables were quantitatively analysed by the primary researcher using Sportscode (Sportstec Limited, Sydney, Australia).
4.2.4 Dependent variables
Each game was coded and analysed for the following performance variables:
Successful pass: The successful attempt of a player to deliver the ball to another teammate. Unsuccessful pass: The unsuccessful attempt of a player to deliver the ball to another teammate. Total passes: The total of attempts of a player to deliver the ball to another teammate. Successful dribble: The successful attempt of a player to move while controlling the ball with the
stick.
Unsuccessful dribble: The unsuccessful attempt of a player to move while controlling the ball with the stick.
Total dribbles: The total of attempts of a player to move while controlling the ball with the stick.
Skilled actions The sum of total dribbles and total passes.
Successful actions The sum of successful passes and successful dribbles. Unsuccessful actions The sum of unsuccessful passes and unsuccessful dribbles.
High pressure: The physical pressure applied by a player on an opponent who receives the ball from a teammate within 1 metre of the player.
Medium pressure: The physical pressure applied by a player on an opponent who receives the ball from a teammate between 1 and 5 metres from the player.
Low pressure: The physical pressure applied by a player on an opponent who receives the ball from a teammate from more than 5 metres from the player.
Performance variables for each game were indicated as the frequency of the variables per 25-minute playing time, a method previously used by Dellal, et al. (2011) and Klusemann et al. (2012). The primary researcher conducted the coding and intra-rater reliability demonstrated an ICC around the 0.90 for all variables. The activity profiles of the participants, captured by the GPS units, was analysed using Catapult Sprint 5.1 software (Catapult Innovations, Melbourne, Australia). The speed zones for walking (0-3 km/h), jogging (3-8 km/h), running (8-13 km/h), high-speed running (13-18 km/h) and sprinting (>18 km/h) were based on previous research examining the activity profiles of youth players in team sports
(See Castagna, D’Ottavio, & Abt, 2003; Castagna, Manzi, Impellizzeri, Weston, & Barbero-Alvarez, 2010).
The positional data from the GPS units of all participants was used to calculate the real density per player. Using Matlab R2014A (MathWorks, Natick, Massachusetts, United States) the GPS coordinates were transformed into X- and Y-coordinates using the bottom left corner of the pitch as the origin (see Figure 4.1). Real density per player was defined as the total space covered by a team divided by the number of field players (Figure 4.1). A convex hull method was used to measure the total space covered by a team (Frencken, Lemmink, Delleman, & Visscher, 2011).
Figure 4.1. Graphical representation of player position and real density (dashed line) measurements of both teams. Coordination of x-axis and y-axis with origins in the left-bottom corner of the pitch.
4.2.5 Data analysis
A two-way analysis of variance (ANOVA) with repeated measures (with number of players and density as within-participant factors) was used to determine the effect of the number of players and density on the performance variables, activity profile and positional data of players. Post hoc comparisons were investigated through the use of t-tests with Bonferroni correction. To calculate the effect size, partial eta squared ( p2) was used, the descriptive terms were: small effect = 0.01, medium effect = 0.06 and large effect = 0.14 (Cohen, 1988). The raw mean differences and 95% confidence intervals were reported for significant main effects. Statistical significance was set at p < 0.05.